Chapter 09 · Part IV · The market: Who it is for and how it is judged

Solutions catalogue

pages 263–30013 figures27 tables

Part IV · The market: Who it is for and how it is judged

Part IV

1. The market Who it is for and how it is judged#

Part IV maps the framework to clients: the roadmap and how it is derived, one profile per decision archetype with its value equation, the customer segments with one solution page each, and the buyer's guide a CIO uses to evaluate decision optimization without waiting for a track record.

Chapter 09Solutions catalogue

The roadmap and how it is derived with a client, the eleven decision archetype profiles, the customer segments and one solution page per segment.

Chapter 10CIO buyer's guide

How a CIO should evaluate decision optimization: many dimensions, three levels, never one outcome.

Chapter 09 · The market

Solutions catalogue · Roadmap, archetypes, segments

2. The solutions catalogue#

What we sell to whom: the roadmap, the eleven decision archetypes, the customer segments and one solution per segment

How a roadmap is derived with a client in seven steps; eleven decision archetype profiles with what makes each superhuman, the four alphas it delivers and its value equation; twenty-two segments in five clusters, with a one-page solution for each.

01 · roadmap	02 · archetypes	03 · segments
Derived with the client	Eleven profiles	Twenty-two, in five clusters
Seven steps from long list to signed decision portfolio.	Superhuman edge, questions, KPIs, four alphas and a value equation.	One page per segment: challenge, target, value and its decisions.

Summary

3. In brief#

A roadmap is an ordered portfolio of use cases.

It fixes which decision policies, analyses and solutions land in which wave, the alpha each use case is underwritten for, the proof it must show at its gate, and the readiness work before it. It is fixed at G1 and revised at every gate.

Seven steps derive it with the client.

From a long list of candidates at three levels, scored on desirability, viability, feasibility and risk by the client's owners, to a signed decision portfolio. Every step has an owner on the client side; vencortex challenges, scores alongside and writes up.

Eleven decision archetypes, one profile each.

Selection, Conviction test, Price, Treatment, Timing, Financing, Allocation, Portfolio construction, Hedging, Trigger design and Root cause, grouped under the four decision policy types and recurring across every investor type.

Each profile says what makes the archetype superhuman.

The part of the decision where the platform does what no team can: scale, simulation, calibration or memory. Then the questions it answers, the AI capabilities it rests on, its KPIs, the inefficiencies the audit typically finds, and its value equation.

Four alphas, one value equation.

Every archetype delivers information, analysis, behavioural and governance alpha, the same four sources of pain the segments use. The benefit is written as an equation with the client's own numbers: never a promise, always checkable later.

Three axes, one of which defines the segment.

Firm type defines the segment, so each firm sits in one segment only. Asset class picks the data, ontology and causal drivers. Strategy picks the solution archetype and the lead use case.

Four decision roles, two lenses.

Decision architect, decision maker, recommender and analyst: the titles change by segment, the roles do not. Pains are listed by source, the four alphas, and benefits by form: efficiency, scale, coverage, effectiveness, experience and innovation.

One solution page per segment, and a prime target.

Twenty-two segments in five clusters, each with challenge, target, value, decisions as questions and example analyses. Asset owners that insource and want to see what they own are the prime target, because both needs are infrastructure needs.

7steps that derive the roadmap with the client in Discovery, from the long list to the sponsor's signature at G1.

11decision archetype profiles, each with its superhuman edge, questions, capabilities, KPIs, four alphas and value equation.

22customer segments in five clusters, asset owners, public markets, private markets, corporates and advisors, with one solution page each.

Roadmap · Definition and method

4. The roadmap: definition and method#

A roadmap is not a project plan. It is the ordered portfolio of use cases a client will engineer, each underwritten, gated and proven on its own, and it is derived with the client, not for the client.

DEFINITION

Roadmap

The ordered portfolio of use cases a client will engineer: which decision policies, analyses and solutions land in which wave, the maturity level each wave reaches, the alpha each use case is underwritten for, the proof it must show at its gate, and the readiness work that must precede it. It is derived with the client in Discovery, fixed at G1 and revised at every gate.

What a roadmap contains#

Waves

The order in which use cases land. Wave 1 is one decision policy with one owner; later waves add policies, then the solution that connects them.

Use cases per wave · Each with its level (analysis, decision policy, solution), its owner, its long-list row and its templates (A, B or C).

Maturity level per wave · L2 analysis augmented, L3 single decision policy, L4 end-to-end solution, L5 total portfolio approach; the entry level comes from the assessment.

Underwritten alpha · The benefit equations per use case with the client's numbers (Template V), summed per wave; what the wave is expected to be worth in $.

Proof plan and gate · What each use case must show at its gate (Template F, Q): analysis, outcome, process and innovation proof against the audited status quo.

Readiness work · Data, people and controls that must be in place before a wave starts (Template R); the owner of each item and its date.

Dependencies · Analyses shared by several policies and the order that builds them once; hand-offs the solution wave will connect.

Seven steps to derive the roadmap with the client#

The steps run in Discovery, mostly in two workshops with the sponsor and the decision owners, and end with the sponsor's signature at G1. Every step has an owner on the client side; vencortex challenges, scores alongside and writes up.

Step	What happens	With whom	Templ.	Output
1 Long list	Collect every candidate use case at the three levels from the decision map, the maturity profile and the open questions of the questionnaire: the decisions people named, the analyses behind them, the decision lifecycles that connect them.	Sponsor, decision owners	T, M, H, L	Long list, one row per candidate with its level and owner
2 Score	Score each candidate on desirability, viability and feasibility (0 to 10) and risk on its own; the client's owners score, vencortex challenges. Potential = D + V, complexity = (10 − F) + R.	Decision owners, vencortex	L	Scored long list
3 Underwrite	Write the value of the top candidates as equations with the client's own numbers (decisions per year × capital per decision × Δ quality; hours freed × cost; Δ loss probability × exposure), never as a promise.	Sponsor, decision owner, vencortex	V	Underwritten alpha per use case, in $
4 Position	Place every candidate on potential against complexity: land the fruit (high potential, low complexity), stage the moonshots (high potential, high complexity) behind the analyses they need, park the rest.	Sponsor, vencortex	L	Quadrant with land, stage and park
5 Sequence	Order the landed and staged use cases into waves. Rules: wave 1 is one decision with one owner and a proof within a quarter; an analysis shared by several policies is built in the earliest wave that needs it and reused after; no owner carries more than two use cases per wave; a blocked use case keeps its place and another takes its slot.	Sponsor, decision owners	S, R	Waves with their use cases, dependencies and readiness work
6 Set the target	Give every wave its maturity level (L2 to L5), its gate and its proof plan: what evidence the use case must show, measured against the status quo recorded in the audit.	Sponsor, vencortex	S, F, Q	Target level and proof plan per wave
7 Agree and revisit	The sponsor signs the roadmap at G1 as the decision portfolio. It is reviewed at every gate: use cases move between waves on evidence, and the whole portfolio is re-ranked at Scaling with the repeated assessment.	Sponsor, gate board	S, Q	Signed roadmap; revisions logged per gate

Rules that keep a roadmap honest#

01Fruit before moonshots
The first wave shows efficiency and coverage fast; effectiveness needs a track record and comes once the first wave funds it.
02One decision, one owner
Wave 1 is a single decision policy with a named owner and a measurable outcome, never a platform roll-out.
03Analyses are the shared path
A reused analysis is built once in the earliest wave that needs it; later policies inherit it at the cost of a configuration.

Readiness gates the wave

Data, people and controls are checked before the wave starts (Template R); a missing item defers the use case, it does not shrink it.

Example · illustrative. A buy-and-build fund enters at L3 with the add-on go / no-go and price (wave 1, one deal partner, proof within two add-ons), adds add-on selection and financing in wave 2 on the same synergy and price analyses, connects the decision lifecycle from platform bid to exit in wave 3 (L4), and repeats the assessment at Scaling before deciding on the fund-wide total portfolio view (L5). Underwritten: 0.5 turn lower average add-on multiple across 10 add-ons, before efficiency and coverage.

Evidence moves use cases

A use case advances a wave only on proof at its gate; a failed proof sends it back to engineering, not to the next wave.

Decision archetypes · Profiles

5. Decision archetype profiles#

A decision archetype is a decision with one clear objective that recurs across asset classes; concrete decision policies are built from it. Each profile says what DeOS does there that a team cannot, which questions it answers, which capabilities it rests on, how it is measured, how it delivers the four alphas, what goes wrong today and how its value is written as an equation in euros.

How to read a profile#

BLOCK · WHAT IT SAYS

Superhuman · The part of the decision where the platform does what no team can: scale, simulation, calibration or memory. The sales argument in one sentence.

Questions answered · The decision question and its two supporting questions, in abstract form; a concrete policy makes them specific.

AI capabilities · The analysis components and types the decision archetype rests on, in the order of the intelligence flow: perceive, diagnose, predict, recommend.

KPIs

What to measure to prove the use case and benchmark it against the status quo: outcome KPIs first, then calibration and efficiency.

Four alphas · How the use case delivers information, analysis, behavioural and governance alpha; the same four sources of pain the segments use.

Inefficiencies today · What the audit typically finds before engineering; the status quo the KPIs are measured against.

Value equation · The benefit written as an equation with the client's own numbers (Template V); never a promise, always checkable later.

WHAT MAKES IT SUPERHUMAN MAIN QUESTIONS ANSWERED KPIS TO MEASURE AND BENCHMARK VALUE EQUATION → $ ANALYS IS ALPHA AI CAPABILITIES IT RESTS ON INEFFICIENCIES TODAY

SELECTION · DECISION ARCHETYPE

Selection

Which candidates deserve our time, and in what order?

Ranks the whole universe, not the part a team has time for, on every criterion of the mandate at once, with a probability that each candidate is the best,

refreshed as context changes.

Which candidates deserve our time, and in what order? How likely is each to be the best choice against our criteria? What would we have missed?

Shortlist hit rate (candidates that later clear the conviction test); rank correlation of the ranking with realised outcomes; universe coverage per analyst; time from universe to shortlist

Criteria scored from causal drivers, not labels

Context construction and scan (perceive); contextual valuation and returns (diagnose, predict); probabilistic Selection with explanation (recommend)

Screens by habit and label; coverage limited to what analysts know; criteria weighted differently by each team; no record of what was dropped and why

Δ shortlist hit rate × candidates pursued per year × value per successful pursuit + analyst hours saved per screen × screens per year × cost per hour

INFORMATION ALPHA

Thousands of names scanned on the ontology, not a few hundred by habit

BEHAVIOURAL ALPHA

No anchoring on the familiar; the ranking records why each name is where it is

GOVERNANCE ALPHA

The mandate's criteria and weights applied the same way to every candidate

WHAT MAKES IT SUPERHUMAN MAIN QUESTIONS ANSWERED KPIS TO MEASURE AND BENCHMARK INFORMATION ALPHA ANALYS IS ALPHA AI CAPABILITIES IT RESTS ON INEFFICIENCIES TODAY GOVERNANCE ALPHA VALUE EQUATION → $

GO / NO-GO · DECISION ARCHETYPE

Conviction test

Should we proceed, and on what terms?

Turns a yes or no into a calibrated probability against an explicit threshold, with the terms under which it holds, stress tested across the paths the team

cannot enumerate.

Should we proceed, and on what terms? How confident are we, and is that confidence calibrated? Under which scenarios does the case break?

Calibration of conviction (Brier score, reliability by bucket); hit rate above and below threshold; base-rate error on passed cases; cases per committee hour

Comparable situations and base rates the memo never cites

A distribution of outcomes instead of one base case

Peer and situation scan (perceive); causal mispricing and driver diagnosis; default or catalyst prediction and stress testing (predict); conviction against threshold with explanation (recommend)

Base cases with no probability; committees that pass on narrative; overrides without record; noise between underwriters on the same case

Every pass and every override recorded with its rationale

Δ probability of a bad pass × cases passed per year × average loss given a bad pass + Δ good cases not missed × value per case

BEHAVIOURAL ALPHA

Overconfidence and groupthink checked by a recorded probability and threshold

WHAT MAKES IT SUPERHUMAN MAIN QUESTIONS ANSWERED KPIS TO MEASURE AND BENCHMARK INFORMATION ALPHA VALUE EQUATION → $ ANALYS IS ALPHA AI CAPABILITIES IT RESTS ON INEFFICIENCIES TODAY BEHAVIOURAL ALPHA GOVERNANCE ALPHA

POLICY OPTIMIZATION · DECISION ARCHETYPE

Price

What should we pay or accept?

Searches the full price and term space against the distribution of value and the walk-away, instead of anchoring on the last comparable or the seller's ask.

What should we pay or accept? Where is the walk-away, and what moves it? How much of the price is paid for synergy we cannot prove?

Realised multiple or spread against the recommended range; share of deals won inside the range; overpayment ex post (price paid minus realised value); walk-away discipline

Base rates for the asset's context, not the anchor in the room

Value as a distribution, price as an optimum against it

Contextual base rates (perceive); contextual valuation and returns (diagnose, predict); price optimization and contextual impact (recommend)

Prices set from the last deal or the ask; walk-away moved during the process; synergies paid for in full; spreads that do not reflect the issuer's drivers

Anchoring and deal fever countered by a pre-agreed walk-away

Price policy and limits applied before the bid, not negotiated after

Δ price paid or received (turns, bps) × deal volume per year + deals lost or won at the right price × value per deal

POLICY OPTIMIZATION · DECISION ARCHETYPE

Treatment

Which levers must management pull, in which order?

Finds the ordered set of levers that moves the outcome most for this company in this context, from a causal model of its drivers, rather than a generic

playbook.

Which levers must management pull, in which order? How much will each move the outcome, with what delay? Which lever is wasted in this context?

Realised EBITDA or cash bridge against the plan by lever; forecast error of lever effects; time to first measurable effect; levers abandoned

Driver data across peers the operating team cannot assemble

Causal effects per lever, not correlations in a benchmark

Peer and driver scan (perceive); causal driver decomposition (diagnose); fundamental forecasting and treatment response (predict); treatment optimization (recommend)

Generic value creation playbooks; levers chosen by precedent; effects not measured against the counterfactual; plans rewritten every budget round

The plan written before the budget negotiation, with its uncertainty

A lever order the board can audit against outcomes

Δ realised value from levers (EBITDA × multiple) per company × companies per year + management time saved per plan

POLICY OPTIMIZATION · DECISION ARCHETYPE

Timing

When is the best window to act?

Reads regime and catalyst probabilities across situations like this one to put the action in the window with the best expected value, rather than on the

calendar.

When is the best window to act? What is the cost of waiting, and of acting now? Which signal would close the window?

Realised outcome against the best window ex post; share of actions inside the recommended window; regret of timing (value lost by acting early or late)

Regime and catalyst evidence from thousands of comparable situations

Δ value captured by timing (bps × capital moved) + Δ cost of waiting avoided × decisions per year

Timing as an expected value curve, not a date

Similar situation and point-in-time scan (perceive); contextual regime (diagnose); catalyst and macro prediction (predict); timing optimization with systemic impact (recommend)

Exits and entries on the calendar or on fund life; catalysts noticed late; waiting cost never priced

Procrastination and calendar habit replaced by an explicit window

Timing rules recorded and reviewed, not improvised

WHAT MAKES IT SUPERHUMAN MAIN QUESTIONS ANSWERED KPIS TO MEASURE AND BENCHMARK VALUE EQUATION → $ ANALYS IS ALPHA AI CAPABILITIES IT RESTS ON INEFFICIENCIES TODAY GOVERNANCE ALPHA

POLICY OPTIMIZATION · DECISION ARCHETYPE

Financing

How should we structure the capital?

Optimizes structure, leverage and covenants across the distribution of cash flows and rate paths, with the downside tail explicit, instead of one base case

and a bank's term sheet.

How should we structure the capital? How much leverage survives the bad paths? Which covenant will bind first, and when?

Cost of capital against the optimum; covenant breaches and waivers; refinancing executed inside the window; downside coverage (paths with headroom)

Δ cost of capital (bps) × debt volume + Δ probability of a covenant event × exposure × loss given event

Headroom tested across thousands of paths

Contextual regime (perceive, diagnose); macro forecasting and stress testing (predict); financing optimization and systemic response (recommend)

Structures from term sheets; leverage set on the base case; covenants tested once; refinancing walls discovered late

Leverage policy and limits applied to every structure

INFORMATION ALPHA

Rate paths and refinancing walls read across the market, not from one bank

BEHAVIOURAL ALPHA

Optimism in the base case replaced by a tail that is seen before signing

WHAT MAKES IT SUPERHUMAN MAIN QUESTIONS ANSWERED KPIS TO MEASURE AND BENCHMARK INFORMATION ALPHA VALUE EQUATION → $ AI CAPABILITIES IT RESTS ON INEFFICIENCIES TODAY GOVERNANCE ALPHA

POLICY OPTIMIZATION · DECISION ARCHETYPE

Allocation

How should capital be deployed across opportunities?

Allocates against the mandate's own goals and constraints across every opportunity at once, with the ripple-through between them, instead of silo by silo

against a generic benchmark.

How should capital be deployed across opportunities? What does the mandate, not the index, imply? Which allocation is robust across regimes?

Return against the mandate's objective per unit of risk or capital; drift from the recommended allocation; regime robustness (worst-regime outcome); rebalancing cost

Shared drivers across sleeves that silos cannot see

Δ return per unit of risk × assets allocated + rebalancing and reporting hours saved × cost per hour

Contextual regime (perceive); knowledge fusion and contextual returns (diagnose, predict); ripple-through and allocation optimization with systemic synergy (recommend)

Generic benchmarks set the portfolio; silos compete for capital; interactions between sleeves ignored; rebalancing by calendar

Allocation policy encoded, with every deviation recorded

ANALYS IS ALPHA

Expected returns fused from models and experts, allocated with their interactions

BEHAVIOURAL ALPHA

Herding with peers and benchmark hugging replaced by the mandate's own answer

POLICY OPTIMIZATION · DECISION ARCHETYPE

Portfolio construction

How large should each position be?

Sizes every position from conviction, codependency and contribution to the book at once, so sizing adds to selection instead of leaking it.

How large should each position be? Which positions share one driver? Where does sizing leak the alpha of selection?

Sizing skill (return from sizing against equal weight); hidden concentration (positions sharing one driver); realised against intended risk; turnover

Codependencies read from drivers, not from sector labels

Δ sizing contribution (bps) × book size + Δ drawdown avoided from hidden concentration × probability

Sizes from the joint distribution, not one position at a time

Codependency scan (perceive); causal driver decomposition (diagnose); contextual returns (predict); portfolio construction with systemic synergy (recommend)

Sizes by habit or equal weight; concentration hidden under labels; selection alpha lost in sizing; limits checked after the fact

Sizing by conviction habit replaced by a rule the PM set

Limits and construction rules checked before the trade

POLICY OPTIMIZATION · DECISION ARCHETYPE

Hedging

Which exposures do we keep, and which do we hedge?

Decides which exposures are paid for and which are noise, and sizes the hedge against the stress paths, instead of hedging by rule of thumb or not at all.

Which exposures do we keep, and which do we hedge? What does the hedge cost against what it protects? How does the hedge behave in the stress paths?

Realised against intended exposure; hedge cost per unit of protection; drawdown in stress periods against the unhedged book; basis risk

Factor and macro exposures measured on drivers across the whole book

Δ drawdown avoided × probability of stress + Δ hedge cost saved × notional hedged

Hedge sized against the distribution of stress paths

Causal driver decomposition (diagnose); macro forecasting and stress testing (predict); hedging optimization with systemic response (recommend)

Hedges by rule of thumb or after a loss; unintended factor bets; hedge cost not measured; basis risk unknown

Fear and complacency cycles replaced by a standing hedge policy

Hedge ratios and limits recorded and reviewed

MONITORING · DECISION ARCHETYPE

Trigger design

What should trigger action, and at what threshold?

Watches every holding on its drivers continuously and fires only when a signal changes the odds of the thesis, instead of a monthly review that misses what

matters and alerts on what does not.

What should trigger action, and at what threshold? Which signal changes the odds of the thesis? Which alerts are noise?

Lead time of the trigger before the event; precision and recall of alerts; losses avoided per trigger; review hours per holding

Every holding watched on drivers, beyond the team's bandwidth

Δ loss avoided per early trigger × events per year + review hours saved × holdings × cost per hour

Triggers set on the probability of the event, not on a price move

Codependency and anomaly scan (perceive, diagnose); default or catalyst prediction and ripple-through (predict); monitoring policy with contextual impact (recommend)

Reviews by calendar; alerts on price not on drivers; thesis breaks noticed late; actions improvised when the alert comes

Thesis-break denial countered by a pre-set trigger and action

Monitoring policy recorded; every trigger and response logged

WHAT MAKES IT SUPERHUMAN MAIN QUESTIONS ANSWERED KPIS TO MEASURE AND BENCHMARK ANALYS IS ALPHA AI CAPABILITIES IT RESTS ON INEFFICIENCIES TODAY GOVERNANCE ALPHA VALUE EQUATION → $

MONITORING · DECISION ARCHETYPE

Root cause

Why did the outcome differ from what we expected?

Separates skill from luck and market from decision in every outcome, along the causal decision graph, so the firm learns from the right lesson.

Why did the outcome differ from what we expected? Was the decision sound given what was known? What should change: the policy, the analysis or nothing?

Share of outcomes attributed by cause; calibration of past decisions by bucket; policy changes made on evidence; time from outcome to review

Variance attributed to market, factor, thesis, timing and execution

Relative performance and driver decomposition (perceive, diagnose); catalyst effect and impact (predict); contextual and systemic impact attribution (recommend)

Post-mortems that blame the outcome; skill and luck mixed; lessons not fed back; managers judged on noisy records

Every review feeds the decision memory and the next policy version

Δ value of policies improved on evidence × decisions per year + Δ cost of wrong lessons avoided (managers kept or fired on luck)

INFORMATION ALPHA

Outcomes compared with the counterfactual paths, not with the plan alone

BEHAVIOURAL ALPHA

Outcome bias and hindsight replaced by a verdict on the decision, not the result

Example: PE buy and build. Names, numbers and thresholds are illustrative, not client data or product output. The capability names follow the DeOS Glossary; the analysis chains per decision archetype are in the Structure Handbook, chapter 8.

Customer segments · Version 14 october 2026

6. Customer segments#

DeOS sells one promise to every segment: automate the analysis, augment the decision. What changes from segment to segment is which decision matters most, who owns it and how success is measured. Twenty-two segments in five clusters, placed by firm type first, so each firm sits in exactly one place. This chapter is the segment map of the Customer Segments and Personas document; the full segment blocks with pains, benefits, personas and objections are there.

Three axes, one of which defines the segment#

Axis	Question	Values and role
1 Firm type	Whose mandate is it, and who carries the accountability?	Asset owner, public-markets manager, private-markets manager, corporate, advisor. Defines the segment; each firm sits in one segment only.
2 Asset class	What is being decided on?	Listed equity, credit, private equity, multi-asset, real assets. Picks the data, ontology and causal drivers.
3 Strategy	Which chain of decisions creates the value?	Value, growth, long/short, thematic, buy and build, buyout, direct lending, total portfolio, corporate capital, transaction. Picks the solution archetype and the lead use case.

Segment map#

Cluster	Segment	Size sweet spot	Solution archetype	Lead decision to start with	Page
A · Asset owners	Pension funds	Fit and size	Total portfolio (CIO)	Manager hire, size, keep or terminate	ch. 04 · 01
A · Asset owners	Insurance	Fit and size	Multi-asset credit · total portfolio under capital	Allocation under capital and ALM constraints	ch. 04 · 02
A · Asset owners	Sovereign wealth funds	Fit and size	Total portfolio (CIO)	Strategic and tactical allocation	ch. 04 · 03
A · Asset owners	Family offices	Fit and size	Family office	Direct deal go / no-go; manager true alpha	ch. 04 · 04
B · Public markets	Institutional asset managers	Fit and size	Value and long-only · multi-asset credit	Position sizing and sell discipline	ch. 04 · 05
B · Public markets	Active ETF providers	Fit and size	Value and long-only · rules variant	Rules for inclusion, weighting, rebalancing	ch. 04 · 06
B · Public markets	Hedge funds	Fit and size	Long / short equity · multi-asset credit	Risk capital across PMs; cut or hold	ch. 04 · 07
B · Public markets	Active owners	Fit and size	Activist and active owner · engagement variant	Which engagement lever, at which company	ch. 04 · 08
B · Public markets	Activist investors	Fit and size	Activist and active owner · campaign variant	Campaign go / no-go and the ask	ch. 04 · 09
B · Public markets	Value investors	Fit and size	Value and long-only	Margin-of-safety test and sizing	ch. 04 · 10
B · Public markets	Growth equity: public	Fit and size	Value and long-only · growth variant	Durability of growth; entry timing	ch. 04 · 11
B · Public markets	Long/short equity	Fit and size	Long / short equity	Short selection; sizing; exit triggers	ch. 04 · 12
B · Public markets	Thematic investors	Fit and size	Value and long-only · thematic variant	Which names truly carry the theme	ch. 04 · 13
C · Private markets	PE buyout	Fit and size	PE buy and build · single-asset variant	IC go / no-go and price	ch. 04 · 14
C · Private markets	PE buy and build	Fit and size	PE buy and build	Add-on go / no-go and price	ch. 04 · 15
C · Private markets	Growth equity: private	Fit and size	PE buy and build · minority growth variant	Entry valuation and growth durability	ch. 04 · 16
C · Private markets	Private credit: direct lending	Fit and size	Private credit	Underwriting go / no-go and pricing	ch. 04 · 17
D · Corporates	PE-owned companies	Fit and size	Corporate capital allocation · sponsor variant	Value creation lever order	ch. 04 · 18
D · Corporates	Small and mid cap public companies	Fit and size	Corporate capital allocation	Capital allocation and M&A	ch. 04 · 19
E · Advisors Investment advisors		Fit and size	Total portfolio · client level	Allocation and manager selection per client	ch. 04 · 20
E · Advisors Transaction advisory:	sell side	Fit and size	M&A advisory · sell side	Buyer universe, timing and price	ch. 04 · 21
E · Advisors Transaction advisory:	buy side	Fit and size	M&A advisory · buy side	Target screening and synergy case	ch. 04 · 22

The four decision roles, and the two lenses every segment uses#

ROLES

Who buys, who uses

Decision architect designs the solution, the policies and the process; champion and project owner. Decision maker owns the decision, sets goals and constraints, decides and overrides with a recorded reason; economic buyer. Recommender turns analyses into a recommendation with options, conviction and rationale; daily user. Analyst supplies data, models and expert input and reviews automated analyses; daily user, sometimes the data gatekeeper. The titles change by segment; the roles do not.

LENSES

Pains by source, benefits by form

Pains are listed by their source, which are the four alphas DeOS creates: GOVERNANCE

INFORMATION

ANALYSIS

BEHAVIOUR

Benefits are listed by the form they take: efficiency (same output, less effort), scale (more decisions without more people), coverage (what would we have missed), effectiveness (better decisions, and is it skill), experience (better to work with) and innovation (what we could not do before).

Sweet spot. The firm that is sophisticated enough to need a decision system but too small to build one: about $0.5bn to 5bn for managers and funds, $2bn to 30bn for pension funds and insurers with an in-house team, $100m to 2bn enterprise value for PE- owned companies, $0.3bn to 10bn market cap for active acquirers, 5 to 150 professionals for advisors.

The prime target: asset owners that insource and want to see what they own#

Among the twenty-two segments, pension funds, insurers and sovereign wealth funds are the best targets when two things are true at once: the fund is insourcing activities it used to buy from managers and consultants, and it wants full visibility into the strategies it owns, whether they run in-house or at an external manager. Both needs are infrastructure needs. An insourcing fund has to build the decision process, the context and the governance it is taking over, and a team of twenty cannot do that with spreadsheets; a fund that wants look-through into the strategies it owns needs one decision language and one decision memory across every mandate, in-house and external, so that it can judge each strategy by its decision quality and not only by its reported return. That is what DeOS is: the owner's own decision infrastructure, with every strategy it holds run as a solution on its instance.

Signal	What IT looks like	What DeOS gives the owner
Insourcing programme	Internal teams taking over allocation, manager selection, direct investments or co-investments; consultants and fiduciary managers being reduced.	The decision policies, context and governance of the insourced activity as reusable decision assets from day one, instead of rebuilding a manager's process by hand.
Full visibility into strategies owned	Trustees or the board ask what each strategy really does, where its return comes from and whether the manager's skill would repeat.	Every strategy, in-house or external, run as a DeOS solution on the owner's instance: the same decision language, the same decision quality measures, causal attribution of each strategy's return into systemic beta, structural alpha, tactical alpha and luck.
Ownership of data, models and decision memory	The fund wants the learning from its decisions and its managers' decisions to stay with the fund, across manager changes and staff turnover.	A dedicated instance the owner controls: its policies, its context, its models, its decision memory; nothing shared, nothing trained for anyone else.
Governance and fee evidence	Pressure to show the rationale of every decision and the value of every fee paid.	A decision record for every allocation, hiring, sizing and termination decision, and fees compared with the true active return each strategy delivers.

How to read the map. Cluster A segments are the prime targets when the signals above are present; managers and private-markets firms (clusters B and C) are the next best, because they own one decision lifecycle end to end; corporates and advisors follow. Within every cluster, the fit and size on the one pager still apply.

Solutions catalogue · One page per segment

7. Solutions catalogue#

One page per segment. The solution is the archetype and its variant with the segment's stages; the challenge is the segment's pains in its own numbers, by source; the target is the fit and the goals it measures itself by; the value is the benefits by form and the equation to underwrite; the decisions are the policies the solution holds, each named by the question it answers, with its type and decision archetype; the analyses are examples of what runs inside them.

Block

Where IT comes from and where IT goes

Solution and stages · Solution archetype directory (Structure Handbook ch. 7) and the segment's decision lifecycle; becomes Template C and the decision map (Template T).

Challenge · Firm-level pains by source from the segments document; becomes the status quo of the decision audit (Template D).

Target segment · Fit, size, examples and goals from the segments document; qualifies the prospect and sets the KPIs.

Value

Benefits by form from the segments document plus the value equation; becomes the alpha underwriting (Template V).

Decisions as questions · The decision policies of the solution, each with its objective as the question its Recommend step answers, its type and decision archetype; each becomes a Template B.

Example analyses · Illustrations of the analyses inside the decisions; the full chains per decision archetype are in chapter 02 and in the handbook.

Roles

Typical titles of the four decision roles in this segment, from the personas; who to talk to.

Solutions catalogue · 01 · Cluster a · Asset owners

Pension funds#

Solution: Total portfolio (CIO) · Sweet spot: $2bn to 30bn · Start with: Manager hire, size, keep or terminate

CHALLENGE

GOVERNANCE

consultant in which decisions are rarely recorded in a reviewable form; investment beliefs are not applied the same way twice

INFORMATION

ratio of 0.3 needs about 44 years of returns to prove skill, while mandates are reviewed every 3 to 5 years

ANALYSIS

add up while active risk does not; much of the Norwegian fund's active return was factor exposure (Ang, Goetzmann and Schaefer, 2009)

BEHAVIOUR

that do not persist, and fired managers do no worse than their replacements (Goyal and Wahal, 2008)

Active mandates overlap, so fees

A chain of trustees, IC, CIO and

A manager with an information

Sponsors hire after strong returns

TARGET SEGMENT

A pension fund holds assets to pay pensions over decades.

Fit: $2bn to 30bn, a CIO office that writes its own investment beliefs, several external managers plus some in-house capability, and a board pushing for fee and governance evidence. Best fit: a fund that is insourcing manager selection or whole strategies and wants full visibility into the strategies it owns, not only quarterly manager reports.

Examples that fit (illustrative): Ärzteversorgung Westfalen-Lippe, Pensionskasse Stadt Zürich, Railways Pension Scheme (UK).

Goals and measures: Funding ratio and return versus the liability benchmark; fees per unit of true active return; share of decisions with a recorded rationale and time from proposal to decision.

Prime target when: the fund is insourcing activities and wants full visibility into the strategies it owns, in-house and at external managers (chapter 03).

VALUE

EFFICIENCY

built from the same analysis, not rewritten each cycle

The whole line-up reviewed on drivers

SCALE

every quarter by the same small team

COVERAGE

across managers visible before the next hire

EFFECTIVENESS

and terminations; fees paid only for true alpha

EXPERIENCE

range and a record of why

INNOVATION

fund's own mandate; TPA funds outperformed by 1.8% a year in a small self-reported sample (Thinking Ahead Institute, 2024)

Manager reviews and IC papers

Overlaps and hidden factor bets

Fewer return-chasing hires

Trustees get one number with its

Total portfolio approach on the

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Strategic allocation	Which asset mix meets the liability benchmark within the board's risk budget?	PO · Allocation
Manager hire or terminate	Does this manager have skill we can pay for, or a record we cannot distinguish from luck?	Go / No-Go · Conviction test
Mandate size and fee	How large should the mandate be, and what fee is justified by true alpha?	PO · Portfolio construction, Price
Rebalancing and overlay	When does the portfolio drift enough to act, and what should be hedged?	MON · Trigger design; PO · Hedging
Manager review	Was the result skill, factor exposure or luck?	MON · Root cause

Example analyses inside the decisions

Manager true alpha: returns split into beta, factor and residual with the probability of skill
Holdings-level overlap and shared-driver analysis across the line-up

of skill

Liability-aware allocation under regimes
Fee per unit of true alpha

Value equation → $ Δ fees paid for beta (bps × AUM) + Δ value of hires and terminations made on skill × mandates changed per year + review hours saved

DECISION ARCHITECT
Head of investment strategy, CIO office
lead

DECISION MAKER
CIO, head of equity, investment
committee

RECOMMENDER
Head of manager selection, senior
portfolio manager

ANALYST
Manager research analyst, risk analyst

Existing papers: Pension head of equity; Total portfolio

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 02 · Cluster a · Asset owners

Insurance#

Solution: Multi-asset credit · total portfolio under capital · Sweet spot: $2bn to 30bn general account · Start with: Allocation under capital and ALM constraints

CHALLENGE

GOVERNANCE

decision must be auditable for the board and supervisor, yet the rationale sits in memos that cannot be replayed

INFORMATION

moves are seen from ratings and reports, after the fact

ANALYSIS

share one driver: in the MAC credit paper, 19% of a $4.2bn fund for insurers and pensions is tied to one refinancing wall (illustrative)

BEHAVIOUR

underwriters pricing the same case differed by a median 55% where executives expected 10% (Kahneman, Sibony and Sunstein, 2021)

Sectors look balanced while issuers

Every allocation and issuer

Rating migrations and spread

Noise in expert judgment: two

TARGET SEGMENT

An insurer invests premiums against its liabilities under capital rules such as Solvency II.

Fit: $2bn to 30bn general account, an in-house investment team or captive asset manager, a growing private credit and infrastructure book, and a CRO who wants decisions reproducible for model validation. Best fit: an insurer bringing credit or private-markets mandates in-house and wanting full look-through into what it owns.

Examples that fit (illustrative): Gothaer, Signal Iduna, Helvetia, Vaudoise and similar mid-sized composite and life insurers.

Goals and measures: Investment yield per unit of solvency capital requirement; credit losses, downgrades and early-warning lead time; validation findings closed and decisions reproducible point in time.

Prime target when: the fund is insourcing activities and wants full visibility into the strategies it owns, in-house and at external managers (chapter 03).

VALUE

EFFICIENCY

papers produced from one analysis per issuer

SCALE

Hundreds of issuers monitored on

drivers by the same team

COVERAGE

drivers across sleeves caught before they bite

EFFECTIVENESS

fewer downgrades held too long

EXPERIENCE

reproducible decisions with calibration evidence

INNOVATION

allocation on the same capital-aware policy

Credit reviews and committee

Shared refinancing, sector and rate

Higher yield per unit of capital;

Board and supervisor see

Private credit and infrastructure

STAG E 1
Allocation

Issuer selection

STAG E 2

Sizing and hedging

STAG E 3

STAG E 4
Monitoring

STAG E 5
Rebalancing

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Allocation under capital	Which allocation gives the best yield per unit of solvency capital within the ALM limits?	PO · Allocation
Issuer selection	Which issuers are worth the capital charge, ranked on drivers?	Selection · Selection
Issuer go / no-go and spread	Do we take this issuer, and at what spread does the capital earn its keep?	Go / No-Go · Conviction test; PO · Price
Early warning	Which driver move should trigger a review before the rating moves?	MON · Trigger design
Hedging and duration	Which rate and spread exposures do we keep against the liabilities?	PO · Hedging

Example analyses inside the decisions

Causal rating and default prediction per issuer, point in time
Shared refinancing, sector and rate drivers across sleeves

◆Yield per unit of capital under stress paths

Value equation → $ Δ yield per unit of capital × general account + Δ probability of a downgrade held too long × exposure × loss + issuer review hours saved

DECISION ARCHITECT

Head of ALM, head of strategic asset allocation, CRO office

DECISION MAKER

Group CIO, investment committee, CFO

◆Rating migration triggers on drivers

RECOMMENDER

Head of credit or fixed income, portfolio manager

ANALYST

Credit analyst, ALM analyst

Existing papers: None yet

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 03 · Cluster a · Asset owners

Sovereign wealth funds#

Solution: Total portfolio (CIO) · Sweet spot: Younger or smaller funds of USD 2bn to 30bn that are building investment capability now · Start with: Strategic and tactical allocation

CHALLENGE

add steps; decisions must be explained publicly but are rarely recorded in a reviewable form

portfolio: USD 23tn is indexed or benchmarked to the S&P 500 (S&P Dow Jones Indices, 2025), whatever the fund's own goals

without a view of shared drivers; much of Norway's active return was factor exposure (Ang, Goetzmann and Schaefer, 2009)

under public scrutiny invite herding with peers

GOVERNANCE

INFORMATION

ANALYSIS

BEHAVIOUR

Reference portfolio

STAG E 1

Board, ministry and CIO each

Generic benchmarks set the

Asset-class silos compete for capital

Large concentrated positions

STAG E 2

Strategic allocation

TARGET SEGMENT

A sovereign fund invests national wealth for future generations or development goals.

Fit: Younger or smaller funds of USD 2bn to 30bn that are building investment capability now, with a multi-asset mandate, private-market stakes and a wish to keep the IP in-house. Best fit: a fund moving from external mandates to in-house and direct investing, which wants full visibility into every strategy it owns.

Examples that fit (illustrative): Ireland Strategic Investment Fund, Nigeria Sovereign Investment Authority, and state or regional development funds.

Goals and measures: Long-horizon real return versus the reference portfolio; fit with policy and development goals; share of decisions with a recorded rationale and reuse of analyses across teams.

Prime target when: the fund is insourcing activities and wants full visibility into the strategies it owns, in-house and at external managers (chapter 03).

Tactical allocation and direct

STAG E 3

stakes

Risk overlay

VALUE

EFFICIENCY

from one shared analysis

A small team covers the whole portfolio

SCALE

on drivers

COVERAGE

private and direct holdings visible in one view

EFFECTIVENESS

against the fund's own mandate, not a generic index

EXPERIENCE

odds and a full record

INNOVATION

DeOS Studio; IP stays with the fund

STAG E 4

Allocation papers and reviews built

Shared drivers across public,

Allocation recommended

Board and ministry see options,

The team builds new use cases in

STAG E 5

Review

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Strategic allocation	Which allocation serves the fund's own mandate and development goals, not a generic index?	PO · Allocation
Tactical tilt	When does a regime shift justify moving away from the strategic weights?	PO · Timing, Allocation
Direct stake go / no-go	Does this direct investment clear the return hurdle on enough paths, and at what price?	Go / No-Go · Conviction test; PO · Price
External manager selection	Which managers add skill the fund cannot build in-house?	Selection · Selection
Review	Which part of the result came from the mandate, the tilt and the managers?	MON · Root cause

Example analyses inside the decisions

Mandate-aware allocation with ripple-through between asset classes
Regime detection across public, private and direct holdings
Direct deal valuation as a distribution with stress paths
Manager true alpha

Value equation → $ Δ real return against the reference portfolio (bps × assets) + Δ cost of herding avoided + analysis reuse across teams (hours × cost)

DECISION ARCHITECT

DECISION MAKER
CIO, investment committee, board

RECOMMENDER

ANALYST

Solutions catalogue · 04 · Cluster a · Asset owners

Family offices#

Solution: Family office · Sweet spot: $0.5bn to 5bn · Start with: Direct deal go / no-go; manager true alpha

CHALLENGE

GOVERNANCE

family and a few executives; succession puts them at risk

INFORMATION

and managers than it can analyse; in the paper's example, 120 of 310 screened companies face founder succession (illustrative)

ANALYSIS

in the example, the reserve covers 14 months of add-ons in the worst 5% of paths, not the 36 assumed (illustrative)

BEHAVIOUR

a 9.0x bid the example target clears the hurdle on only 14% of paths (illustrative)

STAG E 1

Mandate

Principles live in the heads of the

A small team sees more deals

Liquidity is planned in spreadsheets:

Gut-feel decisions by principals; at

Manager and direct deal

STAG E 2

selection

TARGET SEGMENT

A family office manages one or several families' wealth across generations.

Fit: $0.5bn to 5bn, a professional CIO, a team of 3 to 15, a mix of managers and direct deals, and a family that wants decisions it can read and review. Best fit: an office taking more direct deals and strategies in-house and wanting full visibility into everything it owns.

Examples that fit (illustrative): multi-family offices such as HQ Trust, Marcard, Stein & Co and Focam, and single family offices of entrepreneurial families with direct holdings.

Goals and measures: Real return, drawdown and liquidity coverage; hit rate of direct deals and fees per unit of true alpha; decisions recorded so the next generation can follow them.

Prime target when: the fund is insourcing activities and wants full visibility into the strategies it owns, in-house and at external managers (chapter 03).

STAG E 3

Allocation

Monitoring

VALUE

EFFICIENCY

without hiring analysts

SCALE

An institutional process run by a small

team

COVERAGE

managers and direct holdings in one view

EFFECTIVENESS

managers kept for true alpha

EXPERIENCE

in plain terms

INNOVATION

individual people and trains the next generation

STAG E 4

Deal and manager screens

Liquidity and shared drivers across

Fewer overpriced direct deals;

Principals see options and odds

A decision record that outlives

STAG E 5

Liquidity

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Direct deal go / no-go and price	Do we take this stake, at what price, and on which terms does the case hold?	Go / No-Go · Conviction test; PO · Price
Manager selection	Which managers show true alpha for the fee, and which only beta?	Selection · Selection; MON · Root cause
Allocation	How should the family's capital be spread across managers, direct deals and liquidity?	PO · Allocation
Liquidity planning	Which commitment path keeps the reserve safe in the worst 5% of paths?	PO · Financing
Holding review	Which direct holding or manager needs a decision this quarter?	MON · Trigger design

Example analyses inside the decisions

Deal screening against the family mandate (founder succession, sector, size)
Contextual valuation and hurdle-clearance probability per deal

◆Liquidity paths across commitments and distributions

Value equation → $ Δ overpriced deals avoided × deal volume + Δ fees for beta (bps × managed assets) + screening hours saved

DECISION ARCHITECT

CIO, COO

DECISION MAKER

Family principals, investment committee

◆Manager true alpha

RECOMMENDER

Investment director, head of direct investments

ANALYST

Investment associate, analyst

Existing papers: Family office

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 05 · Cluster b · Public markets

Institutional asset managers#

Solution: Value and long-only · multi-asset credit · Sweet spot: $0.5bn to 5bn AUM · Start with: Position sizing and sell discipline

CHALLENGE

GOVERNANCE

so quality varies and cannot be compared or proven to clients

INFORMATION

half of US fund assets (USD 13.3tn passive against 13.2tn active, Morningstar, 2023); research capacity per name keeps shrinking

ANALYSIS

trailed the S&P 500 in 2025, and 71% of euro global equity funds trailed their index (SPIVA 2025)

BEHAVIOUR

judgment leaks value; AI pilots speed up notes but do not change decisions

79% of active US large-cap funds

Each team runs its own process,

Passive now holds more than

Sizing and selling are where

TARGET SEGMENT

An active asset manager runs equity, credit or multi-asset funds and mandates for institutions and wholesale clients.

Fit: $0.5bn to 5bn AUM, 2 to 6 fundamental teams, fee pressure, and a CIO who wants a consistent, provable process.

Examples that fit (illustrative): Shareholder Value Management, Lupus alpha, Mandarine Gestion and similar independent boutiques.

Goals and measures: Net alpha and information ratio versus benchmark and peers; net flows and mandate wins; hit rate, sizing skill and decisions recorded per fund.

VALUE

EFFICIENCY

client reporting from one analysis

SCALE

More names and funds per analyst

without new hires

COVERAGE

teams visible to the CIO

EFFECTIVENESS

discipline; skill separated from luck per PM

EXPERIENCE

documented, consistent process

INNOVATION

existing policies and analyses

Research updates, IC papers and

Shared drivers across funds and

Better sizing and sell

Consultants and clients see a

New funds and mandates built on

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Idea selection	Which names deserve research time, across every team's universe?	Selection · Selection
Conviction test	Does this name clear the fund's threshold, and is our conviction calibrated?	Go / No-Go · Conviction test
Position sizing	How large, given conviction, codependency and the book?	PO · Portfolio construction
Sell discipline	Which signal breaks the thesis, and what do we do when it fires?	MON · Trigger design
PM review	Which PM's result is skill, and which is beta or luck?	MON · Root cause

Example analyses inside the decisions

Contextual returns across the universe with driver-based peers
Codependency map of the book

◆Thesis-break triggers on drivers

Value equation → $ Δ sizing and sell contribution (bps × AUM) + names covered per analyst × analyst cost + mandates won on documented process

DECISION ARCHITECT

Head of investment process, COO investments

DECISION MAKER

CIO, head of equities or fixed income

◆Skill attribution per PM and fund

RECOMMENDER

Portfolio manager, sector lead

ANALYST

Buy-side analyst, quant analyst

Existing papers: CIO series; Investment committee

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 06 · Cluster b · Public markets

Active ETF providers#

Solution: Value and long-only · rules variant · Sweet spot: $0.5bn to 5bn in ETF assets · Start with: Rules for inclusion, weighting, rebalancing

CHALLENGE

GOVERNANCE

judgment has to be encoded, not added ad hoc

INFORMATION

assets reached USD 3.22tn after 39 straight months of inflows (ETFGI, 2026), mostly to passive

ANALYSIS

benchmarks catch names with little real exposure

BEHAVIOUR

look alike and are hard to defend

Theme labels and generic

Rules must be transparent, so

Crowded shelf: European ETF

Launches follow fashion; products

TARGET SEGMENT

An active or thematic ETF issuer turns an investment idea into a transparent, rules-based product.

Fit: $0.5bn to 5bn in ETF assets, a range of active, smart-beta or thematic products, and an in-house product or index team.

Examples that fit (illustrative): HANetf, Rize ETF, Tema ETFs and asset managers converting funds into active ETFs.

Goals and measures: Excess return over the generic benchmark and tracking to the intended exposure; net inflows and AUM per product; time to launch.

VALUE

EFFICIENCY

rebalancing reports generated from the policy

SCALE

More products on the same rules

engine and team

COVERAGE

on causal exposure, not on its label

EFFECTIVENESS

intended exposure and beat the generic benchmark

EXPERIENCE

methodology they can read and test

INNOVATION

exposure in weeks

Methodology documents and

Every candidate stock measured

Products that track the

Clients and platforms get a

New products built from causal

STAG E 1
Universe

STAG E 2
Rule design

Inclusion and weighting

STAG E 3

STAG E 4
Rebalancing

STAG E 5
Review

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Inclusion rule	Which names carry the stated objective, by drivers rather than labels?	Selection · Selection
Weighting rule	How should weights follow conviction and codependency within the index constraints?	PO · Portfolio construction
Rebalancing trigger	When does drift or a driver change justify a rebalance, net of cost?	MON · Trigger design; PO · Timing
Rule review	Did the rule do what the prospectus promised, and why?	MON · Root cause

Example analyses inside the decisions

Driver-based classification of the universe
Codependency and concentration in the basket

◆Rebalancing cost against expected benefit

Value equation → $ Δ active return of the rule (bps × ETF assets) + rebalancing cost saved + research hours saved per index review

DECISION ARCHITECT

Head of product or index development

DECISION MAKER

CIO, head of ETFs, index committee

◆Attribution of the rule's result

RECOMMENDER

Portfolio manager, index strategist

ANALYST

Index analyst, research analyst

Existing papers: Beyond generic benchmarks

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 07 · Cluster b · Public markets

Hedge funds#

Solution: Long / short equity · multi-asset credit · Sweet spot: $0.5bn to 5bn AUM · Start with: Risk capital across PMs; cut or hold

CHALLENGE

GOVERNANCE

factor, net), so books within limits can share one driver nobody decided to take

INFORMATION

published predictors earn 58% less after publication (McLean and Pontiff, 2016)

ANALYSIS

hedge fund in 13 of 16 years to 2025 by 6.6 points a year (HFRI, via RIABiz); much of the return is beta (Fung and Hsieh, 2004)

BEHAVIOUR

stops: quant funds lost heavily within days in August 2007 (Khandani and Lo, 2011); Melvin Capital lost about 53% in January 2021

The S&P 500 beat the average

Limits are set on labels (sector,

Edges decay once known:

Crowded de-grossing and blunt

TARGET SEGMENT

A hedge fund turns the ideas of one or several portfolio managers into a return stream investors cannot get elsewhere.

Fit: $0.5bn to 5bn AUM, fundamental equity, event-driven or credit, 1 to 15 PMs, a risk function that looks beyond the factor model, and investors questioning fees.

Examples that fit (illustrative): Kite Lake Capital, Trium Capital, Lucerne Capital and similar single-strategy funds.

Goals and measures: Alpha after factor exposure, Sharpe ratio and correlation to markets; drawdown and loss in stress events; return on risk capital per PM.

VALUE

EFFICIENCY

built from the same driver analysis

SCALE

Every position monitored on drivers

without adding analysts

COVERAGE

both legs of the book seen before stress

EFFECTIVENESS

counterfactuals; smaller drawdowns at the same return

EXPERIENCE

beta, structural and tactical alpha

INNOVATION

same causal graph and policies

Risk reports and investor letters

Shared drivers across PMs and

Cut-or-hold decided on

Investors see returns split into

New strategies tested on the

STAG E 1
Strategy

STAG E 2
Risk capital

Idea selection and sizing

STAG E 3

STAG E 4
Monitoring

STAG E 5
Cut or hold

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Risk capital allocation	How much capital does each PM deserve, on skill rather than recent P&L?	PO · Allocation
Idea and short selection	Which longs and shorts are worth the book's risk?	Selection · Selection
Entry and sizing	Does the trade clear the threshold, and how large?	Go / No-Go · Conviction test; PO · Portfolio construction
Cut or hold	Has the thesis broken, or is this noise we should hold through?	MON · Trigger design
Hedge	Which factor exposures are paid for, and which are hedged?	PO · Hedging

Example analyses inside the decisions

Skill versus luck per PM with calibration evidence
Causal mispricing and catalyst prediction per name

◆Factor and codependency exposure of the book

Value equation → $ Δ return from capital moved to skilled PMs (bps × AUM) + Δ losses cut earlier × events per year + Δ hedge cost

DECISION ARCHITECT

COO, chief risk officer, head of risk analytics

DECISION MAKER

CIO, founder or managing partner

◆Thesis-break triggers

RECOMMENDER

Portfolio manager

ANALYST

Analyst, quant researcher

Existing papers: Hedge fund CIO

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 08 · Cluster b · Public markets

Active owners#

Solution: Activist and active owner · engagement variant · Sweet spot: $0.5bn to 5bn AUM · Start with: Which engagement lever, at which company

CHALLENGE

GOVERNANCE

and walk-away prices drift across holdings and partners

INFORMATION

companies: openings such as successions are spotted late

ANALYSIS

paper's example, a $900m target earns 11% where its franchise supports 15%, and 70% of the gap is fixable (illustrative)

BEHAVIOUR

attachment; plans are judged on the partner's story, not on odds

The gap is rarely split by cause: in the

Ownership rules, holding limits

Few people for many

Long holding periods breed

TARGET SEGMENT

An active owner takes significant minority stakes in listed companies and works with boards and management, privately and over years, to close the gap between what a company earns and what its franchise could earn.

Fit: $0.5bn to 5bn AUM, 10 to 25 concentrated holdings, board seats or close dialogue, and a small investment team.

Examples that fit (illustrative): Active Ownership Capital, Teleios Capital, Accendo Capital.

Goals and measures: Return per holding and against the market; share of the value gap closed by cause; plans adopted by management; cost of building stakes.

VALUE

EFFICIENCY

updates from one model of each company

SCALE

More holdings per partner without

losing depth

COVERAGE

screened for fixable gaps

EFFECTIVENESS

and management capacity; stakes built more cheaply (market impact 3.2% to 1.7% in the example, illustrative)

EXPERIENCE

who knows the business and brings a model they can run

INNOVATION

outcomes; base rates that improve with every holding

Board materials and investor

Companies outside the network

Initiatives ordered by value

Management meets an owner

Proprietary data on initiatives and

STAG E 1
Screening

Engagement agenda

STAG E 2

STAG E 3
Campaign

STAG E 4
Monitoring

STAG E 5

Exit

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Target selection	Which holdings have the most value to unlock through engagement?	Selection · Selection
Engagement agenda	Which changes, in which order, move value most for this company?	PO · Treatment
Escalation	When does engagement become a campaign, and is the ask proportionate?	Go / No-Go · Conviction test; PO · Timing
Progress monitoring	Which signal shows the lever is working, or not?	MON · Trigger design
Exit	When has the engagement earned its value?	PO · Timing

Example analyses inside the decisions

Value creation tree with causal driver decomposition per company
Lever effect and delay estimates from comparable engagements

◆Market reaction and catalyst prediction

Value equation → $ Δ value unlocked per engagement × engagements per year + engagements prioritised on value rather than noise

DECISION ARCHITECT

COO, head of research

DECISION MAKER

Managing partner, investment committee

◆Progress attribution

RECOMMENDER

Partner, investment director

ANALYST

Investment analyst

Existing papers: Active owner

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 09 · Cluster b · Public markets

Activist investors#

Solution: Activist and active owner · campaign variant · Sweet spot: $0.5bn to 5bn AUM · Start with: Campaign go / no-go and the ask

CHALLENGE

GOVERNANCE

under the pressure of a public fight

INFORMATION

in 2025, a third consecutive record, with M&A 35% and capital allocation 31% of objectives (Lazard, 2025)

ANALYSIS

diversified firms trade at a 13% to 15% discount (Berger and Ofek, 1995), but not every gap is fixable; in the paper's example only 31 of 140 deep-discount companies have a mostly fixable gap (illustrative)

BEHAVIOUR

to change course, settle or walk away

Sum-of-the-parts in one number:

Limits and hedging policy drift

A crowded field: 297 campaigns

Public commitment makes it hard

TARGET SEGMENT

An activist takes a stake in an undervalued listed company and pushes, often publicly, for change: break-ups, capital returns, board changes or strategy.

Fit: $0.5bn to 5bn AUM, 3 to 10 campaigns a year, mid-cap targets, and a small team that must win votes.

Examples that fit (illustrative): Bluebell Capital Partners, Petrus Advisers, Enkraft Capital.

Goals and measures: Campaign return against the market and a sector hedge; share of asks adopted; votes won; cost per campaign.

VALUE

EFFICIENCY

analyses from one model

SCALE

More targets screened per partner

COVERAGE

votes predicted per holder

EFFECTIVENESS

adoption; sector hedge lifts campaign-specific P&L from 35% to 78% in the example (illustrative)

EXPERIENCE

the business and a plan they can adopt privately

INNOVATION

policy; proprietary data on levers, boards and votes

White papers, letters and vote

Targets in less familiar markets;

Asks valued with odds of

Boards meet a fund that knows

New campaign styles on the same

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Target screening	Which companies combine a value gap with a winnable case?	Selection · Selection
Campaign go / no-go and the ask	Do we launch, and which ask has the best expected value against the board's likely response?	Go / No-Go · Conviction test; PO · Treatment
Entry sizing and timing	How much, and when, given catalysts and liquidity?	PO · Portfolio construction, Timing
Campaign monitoring	Which signal changes the odds of winning?	MON · Trigger design
Exit	When has the campaign's value been realised?	PO · Timing

Example analyses inside the decisions

Value gap and winnability scoring across the universe
Counterfactual value under each ask

◆Board and shareholder response prediction

Value equation → $ Δ campaign hit rate × campaigns per year × value per win + Δ cost of lost campaigns avoided

DECISION ARCHITECT

COO, head of research

DECISION MAKER

Founder, managing partner

◆Catalyst and timing analysis

RECOMMENDER

Partner, campaign lead

ANALYST

Investment analyst

Existing papers: Activist investor

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 10 · Cluster b · Public markets

Value investors#

Solution: Value and long-only · Sweet spot: $0.5bn to 5bn AUM in value or quality-value strategies · Start with: Margin-of-safety test and sizing

CHALLENGE

GOVERNANCE

applied differently by each analyst and over time

INFORMATION

value: 92% of S&P 500 market value is intangible, up from 17% in 1975 (Ocean Tomo, 2025)

ANALYSIS

trail their benchmark over 15 years, and 47% were merged or closed (SPIVA 2025)

BEHAVIOUR

traps held too long, winners sold too early

93% of US large-cap value funds

Margin-of-safety and sell rules

Book value no longer measures

Anchoring on the entry price: value

TARGET SEGMENT

A value manager buys companies below what they are worth and waits for the gap to close.

Fit: $0.5bn to 5bn AUM in value or quality-value strategies, a concentrated book of 25 to 60 names, and a team of 3 to 10.

Examples that fit (illustrative): Acatis, Paladin Asset Management, Pelargos Capital, and value teams inside mid-sized managers.

Goals and measures: Excess return versus the value index; hit rate and holding-period return; drawdown on value traps.

VALUE

EFFICIENCY

refreshed automatically

SCALE

Wider universe screened without more

analysts

COVERAGE

included in value, not only book and earnings

EFFECTIVENESS

conviction, sell triggers set before entry

EXPERIENCE

held and what would break the thesis

INNOVATION

capitalised R&D, tested on the fund's own policy

Valuation and thesis checks

Intangibles and causal drivers

Fewer value traps; sizing by

Clients see why each name is

Modern value definitions, such as

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Idea selection	Which names are cheap for a reason we can see, and which are traps?	Selection · Selection
Margin-of-safety test	Is the intrinsic value distribution far enough above price to buy?	Go / No-Go · Conviction test
Position sizing	How large, given conviction and the book's codependencies?	PO · Portfolio construction
Sell discipline	Has value been realised, or has the thesis changed?	MON · Trigger design
Review	Was the loss a value trap, timing or the market?	MON · Root cause

Example analyses inside the decisions

Intrinsic value as a distribution with trap-risk diagnosis
Causal mispricing: why the market prices it this way

◆Catalyst prediction for value realisation

Value equation → $ Δ value traps avoided × positions per year × average loss + Δ sizing contribution (bps × AUM)

DECISION ARCHITECT

CIO, head of research

DECISION MAKER

Lead portfolio manager

◆Portfolio construction under codependency

RECOMMENDER

Senior analyst

ANALYST

Analyst

Existing papers: Value investing; Value portfolio construction

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 11 · Cluster b · Public markets

Growth equity: public#

Solution: Value and long-only · growth variant · Sweet spot: $0.5bn to 5bn AUM in quality-growth or growth strategies · Start with: Durability of growth; entry timing

CHALLENGE

GOVERNANCE

strong markets; no fixed price limits

INFORMATION

companies make about 40% of the S&P 500, so the benchmark is a few growth stories

ANALYSIS

underperform over 15 years (SPIVA 2025); sector and rate beta are mistaken for company growth

BEHAVIOUR

paying for stories

94% of US multi-cap growth funds

Valuation discipline relaxes in

Concentration: the ten largest

Extrapolating recent growth;

TARGET SEGMENT

A public growth manager buys listed companies whose earnings will compound faster than the market expects.

Fit: $0.5bn to 5bn AUM in quality-growth or growth strategies, 25 to 60 holdings, a team of 3 to 10.

Examples that fit (illustrative): Seilern Investment Management, Lightman Investment Management, and growth teams inside mid-sized managers.

Goals and measures: Excess return versus the growth index; downside capture; hit rate on growth durability.

VALUE

EFFICIENCY

updated with every data point

More candidates assessed per analyst

SCALE

COVERAGE

holding visible

EFFECTIVENESS

specific, durable growth; smaller drawdowns on rate moves

EXPERIENCE

own and why

INNOVATION

not by labels

Growth models and scenarios

Rate and sector sensitivity of every

Paying only for company-

Clients see which growth they

Growth defined by causal drivers,

STAG E 1
Universe

Durability test

STAG E 2

STAG E 3
Entry timing

STAG E 4
Sizing

Monitoring and exit

STAG E 5

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Idea selection	Which growth stories have drivers that can last?	Selection · Selection
Durability test	How likely is the growth rate to hold for the holding period, and at what price is it paid for?	Go / No-Go · Conviction test; PO · Price
Entry timing	When does the expected value of entering peak against the catalysts?	PO · Timing
Sizing	How large, given the growth's uncertainty?	PO · Portfolio construction
Exit trigger	Which driver change ends the growth case?	MON · Trigger design

Example analyses inside the decisions

Growth durability prediction from causal drivers and comparable trajectories
Price paid for growth against the distribution

◆Catalyst and regime timing

Value equation → $ Δ growth cases that held × positions × value + Δ entries timed inside the window (bps × capital)

DECISION ARCHITECT

CIO, head of research

DECISION MAKER

Lead portfolio manager

◆Driver-based exit triggers

RECOMMENDER

Senior analyst

ANALYST

Analyst

Existing papers: Thematic growth (partly)

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 12 · Cluster b · Public markets

Long/short equity#

Solution: Long / short equity · Sweet spot: $0.5bn to 5bn AUM · Start with: Short selection; sizing; exit triggers

CHALLENGE

GOVERNANCE

drivers between longs and shorts

INFORMATION

long book sat in the top ten positions at the 2023 peak (Goldman Sachs)

ANALYSIS

reads 30% net has 279% of capital on one AI capex driver (illustrative); momentum shorts crash in rebounds (Daniel and Moskowitz, 2016)

BEHAVIOUR

Melvin Capital lost about 53% in January 2021

In the paper's example a book that

Net and gross limits hide shared

Record crowding: 70% of the

Squeezes and crowded shorts:

TARGET SEGMENT

A long/short equity fund holds long positions it expects to outperform and shorts it expects to fall, aiming for returns that do not depend on the market.

Fit: $0.5bn to 5bn AUM, one to five PMs, sector or generalist books with meaningful short exposure.

Examples that fit (illustrative): Ennismore Fund Management, Sector Asset Management, Lucerne Capital.

Goals and measures: Alpha on each leg; Sharpe ratio and net exposure; short-book return and squeeze losses.

VALUE

EFFICIENCY

refreshed automatically

SCALE

More shorts researched per analyst

COVERAGE

and crowding risk visible

EFFECTIVENESS

risk cut (30% to 4% in the example, illustrative)

EXPERIENCE

cause

INNOVATION

shorts run as separate, measured books

Short theses and risk checks

Shared drivers across both legs

Fewer squeezes; margin-call

Investors see alpha by leg and by

Behavioural and fundamental

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Long and short selection	Which longs and shorts deserve work, by drivers and catalysts?	Selection · Selection
Short entry	Does the short clear the threshold given squeeze, crowding and catalyst risk?	Go / No-Go · Conviction test
Sizing and hedge	How large on each side, and which factor exposure is hedged?	PO · Portfolio construction, Hedging
Trim and exit triggers	Which signal says trim, add or exit?	MON · Trigger design
Review	Did the short lose on thesis, crowding, factor or timing?	MON · Root cause

Example analyses inside the decisions

Causal mispricing and catalyst prediction per name
Crowding, momentum and squeeze risk on shorts

◆Factor exposure of the book and hedge sizing

Value equation → $ Δ short hit rate × shorts per year × average size × Δ return + Δ losses from crowding avoided

DECISION ARCHITECT

COO, risk manager

DECISION MAKER

Portfolio manager, CIO

◆Exit trigger design on drivers

RECOMMENDER

Senior analyst

ANALYST

Analyst

Existing papers: LS equity; LS shorts and momentum

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 13 · Cluster b · Public markets

Thematic investors#

Solution: Value and long-only · thematic variant · Sweet spot: $0.5bn to 5bn AUM in thematic funds or ETFs · Start with: Which names truly carry the theme

CHALLENGE

GOVERNANCE

fashion and launches

INFORMATION

funds hold USD 779bn, about 15% below the 2021 peak (Morningstar, 2025)

ANALYSIS

exposure; in the paper's example 500 bp of a fund's 400 bp excess return was theme beta and selection cost 100 bp (illustrative)

BEHAVIOUR

in: 55% of US thematic funds did not survive 15 years (Morningstar, 2024)

Labels catch names with little

Theme definitions drift with

A crowded shelf: 3,198 thematic

Buying the theme after it is priced

TARGET SEGMENT

A thematic manager invests in a structural trend such as AI, energy transition or ageing, across sectors and countries.

Fit: $0.5bn to 5bn AUM in thematic funds or ETFs, 30 to 80 holdings, a theme team of 3 to 10.

Examples that fit (illustrative): Thematics Asset Management, Rize ETF, and thematic teams inside mid-sized managers.

Goals and measures: Excess return versus theme and broad index; purity of exposure; fund survival and flows.

VALUE

EFFICIENCY

updated automatically

SCALE

More themes and products on the

same team

COVERAGE

to the theme measured

EFFECTIVENESS

beta instead of costing it

EXPERIENCE

theme and why

INNOVATION

weeks

Universe and exposure scores

Each company's causal exposure

Selection that adds to theme

Clients see what they own in the

New themes built and tested in

Theme definition

STAG E 1

STAG E 2
Universe

STAG E 3
Carrier test

STAG E 4
Sizing

STAG E 5
Monitoring

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Theme carrier selection	Which names carry the theme in their drivers, not in their labels?	Selection · Selection
Carrier test	How much of this company's value is exposed to the theme, and is it priced?	Go / No-Go · Conviction test; PO · Price
Sizing	How large, given how much the names share the same driver?	PO · Portfolio construction
Theme monitoring	Which signal says the theme is playing out, or is already priced?	MON · Trigger design
Review	Did the fund earn the theme, or a factor it did not intend?	MON · Root cause

Example analyses inside the decisions

Driver-based theme exposure per company
Codependency across theme carriers

◆Theme pricing against fundamentals

Value equation → $ Δ theme return captured (bps × AUM) + Δ concentration losses avoided + screening hours saved

DECISION ARCHITECT

Head of thematic investing, head of product

DECISION MAKER

Lead portfolio manager

◆Attribution of the theme return

RECOMMENDER

Theme specialist

ANALYST

Analyst

Existing papers: Thematic growth

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 14 · Cluster c · Private markets

PE buyout#

Solution: PE buy and build · single-asset variant · Sweet spot: $0.5bn to 5bn funds · Start with: IC go / no-go and price

CHALLENGE

GOVERNANCE

LPs cannot see which return is the firm's own. About two-thirds of 2010 to 2021 buyout returns came from multiple expansion and leverage (McKinsey)

INFORMATION

companies worth USD 3.8tn, distributions only 14% of NAV in 2025 (Bain, 2026)

ANALYSIS

IRR and MOIC (Gompers et al., 2016, 79 investors, USD 750bn+); 10% to 12% annual EBITDA growth now needed for 2.5x over five years, against 5% in the 2010s (Bain, 2026)

BEHAVIOUR

push marginal deals through IC

Deals judged on point estimates of

IC memos differ by deal team;

Exits are stuck: 32,000 unsold

Deal momentum and sunk effort

TARGET SEGMENT

A buyout firm buys control of companies with debt, improves them over a hold of five years or more and sells.

Fit: $0.5bn to 5bn funds, 8 to 20 portfolio companies, operating partners, and LPs asking for evidence beyond the value bridge.

Examples that fit (illustrative): Paragon Partners, Afinum, DPE Deutsche Private Equity.

Goals and measures: Net IRR, TVPI and DPI against top quartile; evidence of repeatable skill for the next fundraise; loss ratio and the share of IC decisions with a recorded independent view.

VALUE

EFFICIENCY

the same analysis

SCALE

More deals screened per deal team

COVERAGE

portfolio visible; in the paper's example five companies in four sectors share a refinancing exposure worth 38% of NAV (illustrative)

EFFECTIVENESS

the fund's odds, not a deal's base case

EXPERIENCE

a record of decisions

INNOVATION

and pacing

IC papers and LP reporting from

Shared exposures across the

Deals and exits decided on

LPs see returns split by cause and

Fund-level optimisation of exits

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Target screening	Which companies fit the thesis and the fund's capabilities?	Selection · Selection
IC go / no-go and price	Do we bid, and what is the maximum price at which the case clears the return hurdle on enough paths?	Go / No-Go · Conviction test; PO · Price
Financing	How much leverage survives the bad paths, with which covenants?	PO · Financing
Value creation plan	Which levers, in which order, for this company?	PO · Treatment
Exit timing	When does the exit window give the best expected value?	PO · Timing

Example analyses inside the decisions

Contextual valuation with IRR and MOIC distributions across scenarios
Causal driver decomposition against peers

◆Stress-tested financing paths

Value equation → $ Δ price discipline (turns × deal volume) + Δ value created from levers × companies + Δ IC hours per case

DECISION ARCHITECT

COO, head of portfolio management

DECISION MAKER

Managing partner, investment committee

◆Exit regime and timing

RECOMMENDER

Deal partner, principal

ANALYST

Associate, analyst

Existing papers: PE CIO; PE deal team

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 15 · Cluster c · Private markets

PE buy and build#

Solution: PE buy and build · Sweet spot: $0.5bn to 5bn funds · Start with: Add-on go / no-go and price

CHALLENGE

GOVERNANCE

limits checked late in each add-on

INFORMATION

buyouts by count (PitchBook, 2025); about half are at least a platform's fourth deal (Bain, 2024): the pipeline outgrows the team

ANALYSIS

synergy case; deals relying on multiple arbitrage returned 1.4x against 2.2x for those with a strategic rationale (Bain, 2024)

BEHAVIOUR

and the market reacts worse (Malmendier and Tate, 2008)

Each add-on judged alone on one

Leverage and concentration

Add-ons are 72.9% of US

Overconfident acquirers buy more

TARGET SEGMENT

A buy-and-build sponsor buys a platform in a fragmented market and grows it through a series of add-on acquisitions, integrating them to create a larger, more valuable company.

Fit: $0.5bn to 5bn funds, platforms doing 5 or more add-ons per hold, a central M&A or integration team.

Examples that fit (illustrative): Ufenau Capital Partners, Bregal Unternehmerkapital, Auctus Capital Partners.

Goals and measures: Platform MOIC of 2.5x or more; organic growth after add-ons; synergies realised versus case; add-ons closed within leverage limits.

VALUE

EFFICIENCY

days, not weeks

SCALE

More add-ons per platform team

COVERAGE

integration risk

EFFECTIVENESS

effect on the platform's exit value

EXPERIENCE

disciplined, explained process

INNOVATION

with every add-on

Add-on screens and IC papers in

Every target ranked on fit and

Add-ons decided on their

Sellers and lenders see a

Synergy base rates that improve

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Platform bid go / no-go and price	Do we bid for the platform, and at what maximum price given the add-on pipeline?	Go / No-Go · Conviction test; PO · Price
Add-on selection	Which add-ons deliver synergy the platform can integrate?	Selection · Selection
Add-on go / no-go and price	Do we buy this add-on, at what price, given the synergy case and capacity left?	Go / No-Go · Conviction test; PO · Price
Add-on financing	How much debt per add-on keeps the leverage path safe?	PO · Financing
Value creation levers and early warning	Which levers, and which signal says the plan is off track?	PO · Treatment; MON · Trigger design

Example analyses inside the decisions

Synergy capture analysis per add-on (example of the handbook)
Price analysis with walk-away

◆Financing stress paths across the add-on sequence

Value equation → $ Δ average add-on multiple (turns × add-on volume) + Δ synergy realised × add-ons + Δ financing cost (bps × debt)

DECISION ARCHITECT

Head of buy and build, head of portfolio operations

DECISION MAKER

Investment committee, deal partner

◆Integration capacity and early-warning triggers

RECOMMENDER

Platform CEO or CFO, operating partner

ANALYST

Associate, platform M&A manager

Existing papers: PE buy and build

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 16 · Cluster c · Private markets

Growth equity: private#

Solution: PE buy and build · minority growth variant · Sweet spot: $0.5bn to 5bn funds · Start with: Entry valuation and growth durability

CHALLENGE

GOVERNANCE

control; protections and follow-on rules vary by deal

INFORMATION

young companies, so judgment carries most of the decision

ANALYSIS

forecasts that are rarely given a range

BEHAVIOUR

missing out push valuations

Entry valuations rest on growth

Minority stakes give limited

Short histories: little data on

Founder stories and fear of

TARGET SEGMENT

A private growth equity firm buys minority or majority stakes in fast-growing, often founder-led companies that need capital to scale, usually with little or no leverage.

Fit: $0.5bn to 5bn funds, 10 to 25 companies, software, tech-enabled services or healthcare.

Examples that fit (illustrative): Highland Europe, Keensight Capital, Kennet Partners.

Goals and measures: Net IRR and TVPI; revenue growth and margin path per company; loss ratio on growth bets.

VALUE

EFFICIENCY

model of each company

SCALE

More companies screened per partner

COVERAGE

companies fill short histories

EFFECTIVENESS

odds of the growth case

EXPERIENCE

drives their value

INNOVATION

optimised across the portfolio

Investment memos from one

Base rates from comparable

Entry valuation tied to the

Founders get a clear view of what

Follow-on and exit decisions

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Sourcing and selection	Which companies combine durable growth with a round we can win?	Selection · Selection
Entry valuation and terms	What is the growth worth across paths, and which terms protect the downside?	Go / No-Go · Conviction test; PO · Price, Financing
Growth durability	How likely is the growth to hold through the holding period?	Go / No-Go · Conviction test
Value support	Which levers does a minority holder actually influence?	PO · Treatment
Exit route and timing	Which route, and when, for this company?	PO · Timing

Example analyses inside the decisions

Growth durability prediction from comparable trajectories
Entry valuation as a distribution with term structures

◆Lever influence under minority rights

Value equation → $ Δ entry price discipline × rounds + Δ growth cases that held × investments × value

DECISION ARCHITECT

COO, head of portfolio

DECISION MAKER

Managing partner, investment committee

◆Exit route and timing analysis

RECOMMENDER

Partner, principal

ANALYST

Associate, analyst

Existing papers: None yet

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 17 · Cluster c · Private markets

Private credit: direct lending#

Solution: Private credit · Sweet spot: $0.5bn to 5bn under management · Start with: Underwriting go / no-go and pricing

CHALLENGE

GOVERNANCE

applied loan by loan, without a portfolio view

INFORMATION

stress shows up too late to act

ANALYSIS

8% to 9% borrowing costs (Bain, 2026), interest coverage of about 3x can fall below 2x on a modest EBITDA miss (McKinsey)

BEHAVIOUR

sponsors softens terms

Underwriting uses one base case; at

Covenants and pricing rules

Borrower data arrives quarterly;

Relationship lending with repeat

TARGET SEGMENT

A direct lender makes senior or unitranche loans to mid-market companies, mostly sponsor-backed, and holds them to maturity.

Fit: $0.5bn to 5bn under management, 50 to 150 borrowers, sponsor-backed mid-market loans, and a monitoring team stretched by higher rates.

Examples that fit (illustrative): Kartesia, Beechbrook Capital, Capza.

Goals and measures: Spread net of losses; loss and recovery rate; early-warning lead time before covenant breaches; capital deployed at target pricing.

VALUE

EFFICIENCY

reports from one model per borrower

SCALE

More borrowers per credit analyst

COVERAGE

rate drivers across the book

EFFECTIVENESS

borrowers; better pricing for the downside tail

EXPERIENCE

clear record per loan

INNOVATION

evidence, with base rates from past cases

Credit memos and monitoring

Shared sponsor, refinancing and

Earlier action on weakening

LPs and lenders' agents see a

Workout routes compared on

STAG E 1
Origination

STAG E 2
Underwriting

STAG E 3
Structuring

STAG E 4
Monitoring

STAG E 5
Workout

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Origination screening	Which borrowers fit the fund's risk appetite and sectors?	Selection · Selection
Underwriting go / no-go and pricing	Do we lend, and at what spread does the risk earn its keep?	Go / No-Go · Conviction test; PO · Price
Covenant design	Which covenant binds first in the bad paths, and where should it sit?	PO · Financing
Early warning	Which driver move should trigger a review before the covenant breaks?	MON · Trigger design
Workout route	Which route recovers most, given the paths from here?	PO · Treatment, Timing

Example analyses inside the decisions

Causal rating and default hazard per borrower
Spread against the loss distribution

◆Covenant stress across cash-flow paths

Value equation → $ Δ losses avoided (Δ default probability × exposure × LGD) + Δ spread earned (bps × book) + underwriting hours per loan

DECISION ARCHITECT

Head of portfolio management, CRO

DECISION MAKER

Credit committee, CIO

◆Early-warning triggers on drivers; shared refinancing walls across the book

RECOMMENDER

Deal team director

ANALYST

Credit analyst, monitoring analyst

Existing papers: MAC credit (partly)

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 18 · Cluster d · Corporates

PE-owned companies#

Solution: Corporate capital allocation · sponsor variant · Sweet spot: $100m to 2bn enterprise value · Start with: Value creation lever order

CHALLENGE

GOVERNANCE

becomes the budget; board packs track variance, not drivers. About 40% of portfolio companies have been held more than five years (Bain, 2026)

INFORMATION

causes: in the paper's example only $1.5m of a $6.0m increase came from levers management controls (illustrative)

ANALYSIS

company; 10% to 12% annual EBITDA growth now needed for 2.5x equity over five years, against about 5% in the 2010s (Bain)

BEHAVIOUR

more than analysis for outcomes in 1,048 decisions (Lovallo and Sibony, 2010); overconfident CEOs overpay for acquisitions (Malmendier and Tate, 2008)

One base case for a leveraged

The plan is fixed at signing and

The EBITDA bridge mixes

Process matters about six times

TARGET SEGMENT

A portfolio company works to one owner's equity case: more equity value at exit than the sponsor paid, while servicing debt of several times

EBITDA.

Fit: $100m to 2bn enterprise value, 2 to 4 years left to exit, leverage of 3x to 6x EBITDA, a CFO with a finance team, and a sponsor that asks for value by cause; buy-and-build platforms fit best.

Examples that fit (illustrative): platforms owned by sponsors such as Ufenau, Afinum or Bregal Unternehmerkapital in building services, healthcare services or IT services.

Goals and measures: EBITDA growth from controllable levers, exit multiple and MOIC; free cash flow and covenant headroom; lever value realised versus plan.

VALUE

EFFICIENCY

by cause each month

SCALE

Levers, capex and add-ons assessed by

the same small finance team

COVERAGE

scenarios, not only against budget

EFFECTIVENESS

value; overstated value creation caught early

EXPERIENCE

management created, with evidence

INNOVATION

the first year, not assembled before exit

Board pack and bridge generated

Covenant headroom tested across

Levers ranked by effect on exit

The sponsor sees what

An equity story evidenced from

STAG E 1
Plan

STAG E 2
Allocation

STAG E 3
Levers

Performance diagnosis

STAG E 4

Exit readiness

STAG E 5

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Value creation lever order	Which levers, in which order, move EBITDA and cash most for this company?	PO · Treatment
Capex and project allocation	Which projects earn their capital within the sponsor's horizon?	PO · Allocation
Pricing and commercial decisions	Which price and mix changes hold against customer and competitor response?	PO · Price, Treatment
Performance diagnosis	Why did the result differ from the plan, by cause?	MON · Root cause
Add-on and exit readiness	Which add-on, and when is the company ready for exit?	Go / No-Go · Conviction test; PO · Timing

Example analyses inside the decisions

Value creation tree with causal lever effects and delays
Project return distributions under scenarios

◆Price response prediction

Value equation → $ Δ EBITDA from lever order × exit multiple + Δ capex returned above the return hurdle + planning hours saved

DECISION ARCHITECT

CFO, value creation office lead, sponsor's operating partner

DECISION MAKER

CEO, sponsor-controlled board

◆Variance attribution by cause

RECOMMENDER

CFO, business unit heads, head of M&A

ANALYST

FP&A, controlling

Existing papers: PE portfolio company CEO; PE value creation team

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 19 · Cluster d · Corporates

Small and mid cap public companies#

Solution: Corporate capital allocation · Sweet spot: $0.3bn to 10bn market cap · Start with: Capital allocation and M&A

CHALLENGE

GOVERNANCE

term: 78% of executives would give up economic value for smooth earnings (Graham, Harvey and Rajgopal, 2005)

INFORMATION

firms lost all sell-side coverage after MiFID II (Fang et al., 2019); small caps trade at a 21.8% discount to large caps on earnings (WisdomTree, 2025)

ANALYSIS

of businesses get almost the same capital every year, while top reallocators earn 30% higher TRS (McKinsey, 1,616 companies)

BEHAVIOUR

and the market reacts worse (Malmendier and Tate, 2008)

Capital stays where it was: one-third

Incentives point to the short

Thin coverage: 334 European

Overconfident acquirers buy more

TARGET SEGMENT

The best fit is a listed small or mid cap that allocates capital actively: a serial acquirer, or a group running a large and complex portfolio of businesses.

Fit: $0.3bn to 10bn market cap, 5 or more acquisitions a year or 10 or more business units, a corporate development team, and a board that wants an independent view on capital allocation.

Examples that fit (illustrative): Indus Holding, Mutares, Lagercrantz, Addtech.

Goals and measures: TSR versus peers and return on invested capital; acquisition returns against the cost of capital and synergies realised; discount to peers and analyst coverage.

VALUE

EFFICIENCY

cases from one model of the group

SCALE

More targets and business units

assessed by the same team

COVERAGE

and targets visible; in the paper's example, calibrating against 940 past deals lifts expected IRR from 19.4% to 22.3% (illustrative)

EFFECTIVENESS

earns most; fewer overpriced acquisitions

EXPERIENCE

capital allocation logic and its record

INNOVATION

group's own deal history

Board papers and investment

Shared drivers across businesses

Capital moved to where it

Investors and analysts see the

Proprietary base rates from the

STAG E 1
Strategy

STAG E 2
Allocation

STAG E 3
M&A

Performance diagnosis

STAG E 4

Capital return

STAG E 5

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Capital allocation	Which businesses and projects deserve capital, against the portfolio's drivers?	PO · Allocation
M&A target selection	Which targets create value we can integrate?	Selection · Selection
Deal go / no-go and price	Do we bid, and what is the walk-away given synergies we can prove?	Go / No-Go · Conviction test; PO · Price
Treatment of underperformers	Fix, hold or divest, and with which levers?	PO · Treatment
Capital return	Dividend, buyback or reinvestment, given the paths ahead?	PO · Financing, Allocation

Example analyses inside the decisions

Business unit driver decomposition and capital productivity
Target screening and synergy prediction

◆Price with walk-away and integration risk

Value equation → $ Δ return on allocated capital (bps × capital) + Δ overpayment avoided × deals + Δ value from underperformers treated

DECISION ARCHITECT

CFO, head of corporate development, head of strategy

DECISION MAKER

CEO, supervisory board or board of directors

◆Scenario paths for capital return

RECOMMENDER

Head of M&A, business unit heads

ANALYST

M&A analyst, FP&A, investor relations

Existing papers: SMID boardroom; Serial acquirer; Semiconductor CEO

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 20 · Cluster e · Advisors

Investment advisors#

Solution: Total portfolio · client level · Sweet spot: Smaller independent firms: 5 to 50 professionals · Start with: Allocation and manager selection per client

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Client allocation	Which allocation fits this client's goals and constraints, not the house view alone?	PO · Allocation
Manager and product selection	Which managers and products show true alpha for the fee?	Selection · Selection
Rebalancing	When does a client portfolio need action?	MON · Trigger design
Client review	What drove the result, and was the advice sound?	MON · Root cause

Example analyses inside the decisions

Mandate-aware allocation per client
Manager true alpha and fee analysis

◆Drift and driver triggers across all client books

Value equation → $ Δ clients per advisor × revenue per client + Δ fees for beta avoided for clients + review hours saved

DECISION ARCHITECT

Head of investment solutions, head of manager research

DECISION MAKER

Client IC or CIO; the firm's CIO for OCIO mandates

◆Attribution of advice

RECOMMENDER

Senior consultant

ANALYST

Manager research analyst

Existing papers: CIO buyer's guide (partly)

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 21 · Cluster e · Advisors

Transaction advisory: sell side#

Solution: M&A advisory · sell side · Sweet spot: Smaller boutiques and mid-market teams of 5 to 150 professionals · Start with: Buyer universe, timing and price

CHALLENGE

GOVERNANCE

MD's experience alone

INFORMATION

relationships and sector labels, not from who gains most from the asset

ANALYSIS

and rarely backed by causes; synergies for buyers are one number

BEHAVIOUR

seller's hopes

Equity stories are assembled late

Process choices rest on the

Buyer lists come from

Timing set by the calendar and the

TARGET SEGMENT

A sell-side M&A adviser runs the sale of a company for its owner, often a PE sponsor or founder: it builds the equity story, picks the buyers to approach, sets timing and runs the process.

Fit: Smaller boutiques and mid-market teams of 5 to 150 professionals, 10 to 60 mandates a year, sector specialisation.

Examples that fit (illustrative): Clairfield International, IMAP, Livingstone Partners.

Goals and measures: Price achieved versus first indication and number of competitive bids; time from mandate to signing; mandates won and repeat mandates.

VALUE

EFFICIENCY

built in days

SCALE

More mandates per MD

COVERAGE

by synergy

EFFECTIVENESS

matched buyers; timing tested on scenarios

EXPERIENCE

recommendation

INNOVATION

mandates anticipated from the PE universe

Buyer profiles and equity story

Buyers outside the network ranked

Higher prices from better-

Sellers see the odds behind each

Pitch before the owner calls:

STAG E 1
Mandate

Buyer universe

STAG E 2

STAG E 3
Ranking

STAG E 4
Pricing

Process and timing

STAG E 5

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Buyer universe and ranking	Which buyers can pay the most and close, ranked on fit and capacity?	Selection · Selection
Price guidance	What price can the market bear, and what is the floor?	PO · Price
Process timing	When does the market give the best window for this asset?	PO · Timing
Process design	Broad auction, limited or bilateral, for this buyer set?	PO · Treatment
Mandate review	What drove the outcome of the process?	MON · Root cause

Example analyses inside the decisions

Buyer capacity and strategic fit scoring across the universe
Contextual valuation from comparable transactions

◆Market regime and timing

Value equation → $ Δ price achieved × deal volume + Δ mandates won on evidence × fee per mandate + hours per pitch saved

DECISION ARCHITECT

Head of corporate finance, sector head

DECISION MAKER

The client (sponsor, founder, board); the MD for process

◆Process outcome attribution

RECOMMENDER

Managing director, director

ANALYST

Associate, analyst

Existing papers: Sell-side advisory

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

Solutions catalogue · 22 · Cluster e · Advisors

Transaction advisory: buy side#

Solution: M&A advisory · buy side · Sweet spot: Smaller strategy and due diligence firms of 5 to 150 professionals working for buy-and-build sponsors and serial acquirers. · Start with: Target screening and synergy case

CHALLENGE

GOVERNANCE

drifts in competitive auctions

INFORMATION

codes and partner networks

ANALYSIS

rarely say what to pay; synergies are one number. In the paper's example 55% of a target's growth came from falling raw-material prices (illustrative)

BEHAVIOUR

(Malmendier and Tate, 2008)

Reports describe the target but

The client's walk-away price

Target screens rely on sector

Overconfident acquirers overpay

TARGET SEGMENT

A buy-side adviser helps PE sponsors and corporate acquirers find, value and win targets: screening, commercial and financial due diligence, synergy cases and bidding strategy.

Fit: Smaller strategy and due diligence firms of 5 to 150 professionals working for buy-and-build sponsors and serial acquirers.

Examples that fit (illustrative): OC&C Strategy Consultants, Accuracy, CIL Management Consultants.

Goals and measures: Client deals that met their case and deals avoided that later failed; time to a go or no-go view; repeat work and fee per project.

VALUE

EFFICIENCY

synergy cases in days, not weeks

More mandates per partner

SCALE

COVERAGE

network screened by causal fit

EFFECTIVENESS

earnings, not the cycle; synergies given as ranges with odds

EXPERIENCE

odds

INNOVATION

and synergy audits; a provable record per mandate

Earnings split by cause and

Targets outside the partners'

Clients pay for normalised

Boards check the advice with its

New services: integration tracking

Decisions in the solution, and the question each answers

Decision	Objective · the question answered	Type · decision archetype
Target screening	Which targets fit the client's thesis, across the whole market?	Selection · Selection
Synergy case	How much synergy is real, and how fast?	Go / No-Go · Conviction test
Price and terms	What should the client pay, and where is the walk-away?	PO · Price
Diligence focus	Which risks move the value enough to test first?	PO · Treatment
Review	Did the deal deliver the case, and why?	MON · Root cause

Example analyses inside the decisions

Market-wide target screening on drivers
Synergy capture prediction from comparable integrations

◆Price with walk-away and integration risk

Value equation → $ Δ synergy cases proven × deals + Δ client overpayment avoided + screening hours per mandate saved

DECISION ARCHITECT

Head of transaction services or commercial due diligence

DECISION MAKER

The client's IC, board or CEO

◆Diligence prioritisation by value at stake

RECOMMENDER

Partner, director

ANALYST

Consultant, analyst

Existing papers: Buy-side advisory

Analyses are examples; the policy archetypes and analysis chains are in the Structure Handbook. Numbers illustrative.

← Hybrid intelligence technology CIO buyer's guide →

Decision optimization with DeOS · vencortex® – DeOS | Decision Optimization System. Converted from DeOS_Compendium_Light.pdf by scripts/pdf_to_markdown.py; built by scripts/build_book_site.py.

Part IV · The market: Who it is for and how it is judged

1. The market Who it is for and how it is judged#

2. The solutions catalogue#

3. In brief#

4. The roadmap: definition and method#

What a roadmap contains#

Seven steps to derive the roadmap with the client#

Rules that keep a roadmap honest#

5. Decision archetype profiles#

How to read a profile#

6. Customer segments#

Three axes, one of which defines the segment#

Segment map#

The four decision roles, and the two lenses every segment uses#

The prime target: asset owners that insource and want to see what they own#

7. Solutions catalogue#

Reading a one pager#

Pension funds#

Insurance#

Sovereign wealth funds#

Family offices#

Institutional asset managers#

Active ETF providers#

Hedge funds#

Active owners#

Activist investors#

Value investors#

Growth equity: public#

Long/short equity#

Thematic investors#

PE buyout#

PE buy and build#

Growth equity: private#

Private credit: direct lending#

PE-owned companies#

Small and mid cap public companies#

Investment advisors#

Transaction advisory: sell side#

Transaction advisory: buy side#