AI Diligence // for M&A buy-side # You're buying the AI story. We price the AI reality. Every target now has an AI narrative. Almost none of them have been diligenced. We score the AI a company actually runs — how it operates, what it sells, and whether its market survives — across eight weighted domains, and convert every finding into a deal term: a number, a verdict, a decision. Book a scoping call → See the product ↓ The confirmatory assessment — the actual product, not a mockup. 3+1 AI surfaces + financial base 8 Weighted domains 14 Red-flag triggers Days Not weeks 01 // The scope, and the deal ## AI is the unowned scope. We run it across the whole deal. Five diligence scopes are already owned — Commercial, Financial, Operational, Technology, Legal. AI is the white space, and it moves the return more than any of them. This is the instrument: the gap sits at the center, and the sweep runs it gate by gate, screen to exit. Commercial Financial Operational Technology Legal AI · unowned 02 // The surfaces ## AI hits a target three ways — and all three land in the financials. A company can run brilliantly on AI, sell a product whose AI claims collapse under scrutiny, and sit in a market about to be disrupted. Three surfaces, each re-pricing the deal differently — all underpinned by one: their financial impact. Operational AI ### How they run it The AI inside the business — a margin and efficiency lever, or a fragile dependency with no fallback. — Cost & margin impact — Dependency & concentration — Fallback & resilience ▲ Red flag  Critical processes automated with no manual fallback. Product AI ### What they sell The AI in the offering — defensible, model-backed growth, or claims outrunning the model. — Growth attributable to AI — Model & data moat — AI-washing exposure ▲ Red flag  Revenue attributed to AI the usage data won't support. Market AI ### The market they're in The AI reshaping the category over the hold — positioned to ride it, or disintermediated before exit. — Disruption trajectory — Competitive displacement — Exit-multiple risk ▲ Red flag  A funded AI-native entrant compressing the exit multiple. Financial AI impact — the foundation ### What it does to the numbers The through-line. Operational efficiency, product growth, and market position all resolve here — in EBITDA, cash, and enterprise value. This is the surface where AI capability and AI risk become money. — EBITDA & margin bridge — AI cost base · compute, licensing, model spend — Revenue quality & durability — Valuation & multiple exposure ▲ Red flag AI compute and licensing costs scaling faster than the revenue they support. Underpins Operational · Product · Market — every surface lands here 03 // What we catch ## The patterns that quietly kill returns. Named AI failure modes — the ones a generalist workstream misses because it isn't looking for them. Each maps to one of the fourteen red-flag triggers. AI-washing not in the product Thin wrapper no moat over a model Model concentration one provider, no fallback Data-rights gap can't prove ownership Shadow AI ungoverned, invisible Regulatory exposure EU AI Act unmapped Liability surface wrong answer is costly Talent single-point capability that can walk Each pattern → a trigger → a capped score and a priced impairment. Nothing gets waved through on a strong average. 04 // Build → place → prove ## How the score is made, judged, and defended. One flow, three moves. Eight weighted domains build the composite (S = Σ wᵢ·sᵢ), with 14 red-flag triggers that can cap it; the banded scale places 2.4 against the 3.6 sector baseline; and provenance proves it — every number drilling to its source. The same eight domains, live in the app — every score drilling to its evidence. 05 // What it's worth ## We price the finding. Then we bank it in the terms. The value engine translates AI capability into six drivers of enterprise value, so a finding lands as a number — and that number lands in the deal as price, structure, or a walk. An Impairment finding is the overpayment you don't make. Y = A · F(K, L) Value coefficient Priced across six drivers Revenue reach Gross margin Opex Working capital Treasury & tax Market reach Finding Value at risk Deal term Product-AI growth the usage data won't support Revenue reach $6.2M Price reduction · revised operating model Critical process automated with no fallback Opex · continuity $3.1M Escrow / holdback · 100-day remediation Training-data rights the company can't prove Gross margin · moat $4.4M Specific reps & warranties · indemnity Market disruption trajectory over the hold Market reach · multiple $5.0M Revised hold thesis · earnout A fatal red-flag trigger fires Circuit breaker · unpriceable Walk Total value at risk identified $18.7M — the overpayment you don't make 06 // The upside ## Every risk is also a lever. We price that too. The same finding that costs you at entry can pay you over the hold. An AI-specific fix does two things: it remediates the value at risk , and it creates new value on top — margin, revenue, operating leverage. We price both, and hand you the value-creation play. Section 05 — value at risk Protects the entry price · defense Section 06 — the upside Builds the exit · offense The AI fix Value created — remediate + create Value-creation play Operational AI Add a fallback, then optimize the process automation $7.9M $3.1M remediated + $4.8M created · +2.4 pts gross margin 100-day: harden the fallback, then extend automation — a named margin lever in the VCP Product AI Re-base the product AI on owned data & models $11.7M $6.2M remediated + $5.5M created · durable, defensible revenue Reposition the growth thesis on real AI — repricing the exit story Market AI Secure the data rights, formalize the moat $7.6M $4.4M remediated + $3.2M created · +1.6 pts pricing power Lock the moat pre-close — underpins hold-period margin Value-creation potential identified $27.2M — priced into the plan, before you own it Remediation — value at risk removed Creation — new margin, revenue, leverage Value creation, tracked across every holding — scored at entry, re-scored over the hold. 07 // The crosswalk ## Every surface. Every workstream. The full matrix. We assess the AI dimension of all five diligence workstreams across all four surfaces — nothing sampled, nothing skipped. Where you already run a workstream, we plug into it; the shading marks where each surface carries the most weight. AI surface ↓   Workstream → Commercial Financial Operational Technology Legal Operational AI Product AI Market AI Financial impact the base ● every surface is assessed against every workstream  ·  shaded = where that surface carries the most weight. The financial base lands in all five. 08 // The product ## Not a static readout. Live intelligence you interrogate. Delivered as three AI-native applications on the Diligence360 engine. Filter to your thesis, drill from a score into the evidence, and move from the whole fund down to a single model. Per-deal ### Assessment Gate · Confirmatory diligence The deep interactive read — surfaces, value, risk, evidence. Ask Copilot anything. Open the app → Screen ### Flash Gate · Pre-LOI screen The one-glance verdict for a fast pre-LOI go / no-go. Open the app → Fund ### Portfolio Gate · Hold & exit Every holding on one panel, benchmarked on a common coordinate system. Open the app → 09 // How it works ## Your data never moves. The compute comes to it. Diligence usually means shipping a target's data out to whoever is analyzing it. We invert that: the engine crosses into the target's environment, works in an isolated clean room, and only governed findings ever come back — sized to the confirmatory window. Crosses in The engine and the questions. Models and analysis are sent to the data — sized to the confirmatory window. Crosses out Findings, scores, evidence references. Governed results only — each one traceable back to its source. Never crosses Raw data, PII, model training. The target's data is never copied out and never used to train any model. ■ Clean room · isolated, engagement-scoped ■ No training on target data · ever ■ Deleted on close · on request 10 // One finding, end to end ## One finding pays for the engagement. Trace a single finding from a surface to a signed term — and to $6.2M of value at risk . Then set that one number against the fee: the engagement is sized to your window and priced against the overpayment it prevents, so it typically returns on a single finding. ↓ that one finding, against the bill That single finding is worth more than the entire engagement. This one finding · value at risk $6.2M Full engagement fee a fraction One finding. Many times the fee. And a real diligence surfaces several — so the engagement pays for itself before the confirmatory window even closes. The pre-LOI screen — where a $6.2M finding first surfaces, in days. 11 // Who it's for ## Built for the buy-side. Read by both sides of the table. Three parties, one deal, one evidence base — each entering at a different gate with a different question. PE buy-side Price the AI, or overpay for it → Sell-side They will find it. Find it first → Lenders Underwrite the AI, not just the EBITDA → Operating partners · fund-level Score the whole portfolio — find the hidden risks and synergies → Industry coverage · seven sectors The pattern library — the same failure, wearing different clothes → The engine doesn't take a side. It produces one sourced, attested read — and each seat at the table asks it a different question. 12 // Engagement tiers ## Three ways in. Each one includes the last. Buy the depth the gate calls for. Screen a target in days, run the full confirmatory read when the deal is real, then keep the whole fund scored over the hold. Every tier includes named SME hours — the engine does the sweep, people do the judgment. The people on it No finding reaches your investment committee without a human SME standing behind it. The hours below are included in the fee — not billed on top. Flash Screen ≈ 10 SME hours - Scope the target and set the trigger thresholds - Validate what the engine flags — no unreviewed output - Write the screen memo: proceed, price, or pass - 30-minute readout with the deal team Confirmatory Assessment ≈ 70 SME hours - Stand up the clean room and scope the connectors - 3–5 management and technical interviews - Human validation of every finding and its evidence - Findings → terms working session with the deal team - IC-ready readout and 100-day plan input Portfolio Intelligence ≈ 15 SME hours / holding / quarter - Quarterly re-score review with the operating partner - Calibrate benchmarks as the sector baseline moves - Track the value-creation plan against the original thesis - Annual deep-dive per holding What you get Flash Assessment Portfolio 8-domain weighted composite score 14 red-flag triggers / circuit breakers Three AI surfaces + financial foundation Value at risk, quantified Findings → deal terms conversion Upside / value-creation plan Evidence drill-through + attestation Copilot — interrogate the read Benchmark vs sector baseline Fund-wide portfolio benchmarking Hold-period re-scoring & monitoring SME time — included in the fee Named engagement lead Management & technical interviews Evidence validation by a human SME Findings → terms working session IC-ready readout Quarterly re-score review Fee basis Per target Per deal Per fund / yr Included Headline level Not included Flash fee credits toward the Assessment if you proceed past LOI. ## Don't sign for AI you haven't seen. Start with a scoping call. We'll score the AI behind your target — how it operates, what it sells, what it's worth — and hand your deal team the price, the terms, or the walk. Book a scoping call →