M&AAIdue-diligence

Deal Scanner Template: How investors and buyers should vet AI vendors post-Siri/Gemini

sstartups

2026-02-04

11 min read

Practical due‑diligence checklist to vet AI vendors post‑Siri/Gemini. Tech, model provenance, governance, integration and commercial checks.

Hook: Why standard vendor checks fail for AI in 2026 — and what to do instead

Acquirers and marketplace partners still trying to vet AI vendors with a standard RFP or basic SOC report are losing deals or inheriting expensive, risky integrations. Since Apple’s 2025 move to run Siri on Google’s Gemini stack, the market bifurcated: strategic platform tie‑ups amplify vendor value — and also the hidden operational and legal risks. Investors and buyers need a tighter, AI‑specific deal scanner that treats model provenance, governance, and integration as first‑class due‑diligence items.

Topline: What this deal scanner delivers

This article gives you a practical, field‑tested due‑diligence checklist for AI vendors in 2026. Use it to rapidly triage targets, negotiate commercial terms, and run a technical audit. It focuses on four critical pillars: tech stack, model provenance, governance & regulatory risk, and business strategy & commercial terms. Each section includes probing questions, required artifacts, and red/yellow/green scoring cues so teams can act fast.

The 2026 context you must assume

Recent developments that shape this checklist:

Platform consolidation: Big tech partnerships like Apple+Gemini (2025–26) make foundation models strategic assets with commercial exclusivity or privileged access that change integration assumptions.
Regulatory pressure: The EU AI Act enforcement and strengthened U.S. regulatory guidance (FTC/NIST updates through 2025) mean many vendor offerings now carry explicit regulatory profiles — some are classed as high‑risk.
Funding volatility: Late‑2025 funding slowdowns and buyout activity (eg. talent migration from underperforming startups) mean financial and team stability checks must be part of diligence — not optional.
Operational complexity: RAG pipelines, vector DBs, and hybrid on‑device/cloud models have created new failure modes (data leakage, stale indexes, hidden cost overruns) that require a technical audit beyond simple uptime and latency metrics.

How to use this deal scanner

Run the high‑level business & team check to decide if you proceed.
Issue the AI Vendor Data Pack (see artifact list below).
Conduct parallel technical and legal reviews with a scoring rubric.
Negotiate SLAs and commercial protections informed by identified risks.
Post‑close: require remediation milestones, telemetry access, and kill‑switch clauses.

1. Business strategy & team (first filter)

Why it matters: Technical brilliance doesn’t replace a clear go‑to‑market or product strategy. Recent examples (late‑2025) show teams with foundation model R&D but weak commercial alignment lose talent and buyers fast.

Key questions

What is the vendor’s core revenue model (SaaS, API consumption, licensing of weights, revenue share)?
Who are their top 5 customers and ARR concentration?
What is the runway, recent fundraising cadence, and talent attrition rate?
Do they rely on a single foundation model provider (e.g., Gemini) or multiple backends?

Artifacts to request

Cap table summary and last 12 months of MRR/ARR metrics.
Customer reference list and anonymized usage metrics.
Org chart with critical engineers and retention risk notes.

Red flags

High customer concentration (>40% ARR from one buyer).
Opaque roadmap or missing product‑market fit evidence.
Staff departures from central ML roles in last 6 months.

2. Technical audit: the system, not just the model

Why it matters: Integration risk is often where deals fail. Ask for a walkthrough of the entire production pipeline — from data ingestion to monitoring and retraining.

Areas to audit

Architecture diagram (runtime, data flows, 3rd‑party services).
Deployment model: hosted API, self‑hosted weights, edge inference.
Observability: logs, telemetry (latency, token usage), error budgets.
Scalability: concurrency limits, autoscaling tests, cost per 1M tokens.
Reliability & SLAs: historical uptime, incident postmortems for last 12 months.
Data pipelines: ingestion, labeling, retention, and data lineage tooling.
Security controls: encryption at rest/in transit, secrets management, pentest/bug bounty results.

Practical checks and tests

Run an integration proof‑of‑concept (7–14 days) measuring latency, throughput and cost under realistic traffic.
Validate observability by generating synthetic anomalies and inspecting alerting.
Confirm RBAC, data partitioning and multi‑tenant isolation with whitebox tests.

3. Model provenance: the single biggest new risk

Why it matters: Post‑Siri/Gemini, the origin and lineage of models determine legal exposure, update guarantees, and compatibility with downstream use cases.

What to verify

Model card / datasheet with training data sources, tokenizer, and intended use cases.
Weights access: Are weights proprietary, licensed from a foundation model, or open? If licensed, what are the contractual limits? (provider contracts and gating matter)
Fine‑tuning lineage: If the vendor fine‑tuned a foundation model, what dataset was used, and what prompts/recipes were applied?
Third‑party components: presence of adapters, plugin ecosystems, or external retrieval layers (RAG).
Watermarking & provenance tech: are outputs identifiable or traceable back to training sources?

Artifacts to request

Model card and version history (semantic versioning).
Fine‑tuning datasets (or redacted samples), licenses, and consent artifacts.
Third‑party model provider contracts and license copies.

Red flags

Vague or missing model cards.
Training data with unknown provenance or scraped content without licensing.
Unclear licensing on weights that could trigger IP or export control issues.

4. Governance, safety, and regulatory risk

Why it matters: Regulators now look for operational controls — not just designs on paper. The EU AI Act, enforcement updates in 2025–26, and stronger U.S. guidance mean your vendor must show ongoing compliance capability.

Key audit points

Risk assessments: documented model risk assessments and mitigation plans.
Red‑teaming: latest red team reports, safety incident history, and remediation logs.
Privacy impact: DPIA (or equivalent) for models handling personal data.
Regulatory classification: is the product considered high‑risk under the EU AI Act or sector rules (healthcare, finance)?
Explainability: evidence of output explainability or decision tracing where required.

Artifacts to request

Latest DPIA, risk register, and red team summary with CVE‑like tracking of issues.
Insurance certificates (cyber, E&O) and limits covering model liabilities.
Retention & deletion policies for training and customer data.

5. Commercial terms, pricing, and partnership risk

Why it matters: Ambiguous pricing or lack of portability means high TCO and lock‑in. Post‑deal remediation is expensive; negotiate upfront protections.

Key terms to negotiate

Pricing model: fixed subscription vs usage — insist on predictable caps and cost‑per‑token guarantees for scale.
Service levels: uptime SLA, incident response time, and credits for failures.
Data ownership: explicit clauses stating customer data and generated outputs remain yours.
Portability: access to exported fine‑tuned models or data dumps on termination.
IP indemnities: coverage for downstream IP claims tied to training data or model outputs.

Commercial protections

Escrow of model artifacts or training recipes for critical integrations.
Short initial term with performance milestones and phased commitments.
Right to audit clauses with clear scope and frequency.

6. Integration & operational readiness

Why it matters: You won’t buy an AI vendor — you’ll integrate a living system. Ensure operational handoffs and runbooks are part of the deal.

Pre‑integration checklist

Provided SDKs, client libraries and supported runtimes.
Example IaC templates and Helm charts for deployment.
Standardized CI/CD hooks for model updates and rollback plans.
Runbooks for incident handling, security breach response, and model drift remediation.
Training materials and onboarding timeline for product & ops teams.

Operational KPIs to lock in

Mean time to recover (MTTR) for model outages.
False positive/negative rates for supervised components (as applicable).
Drift detection cadence and retrain window guarantees.

7. Legal, IP and export control checks

Why it matters: Foundation models, third‑party data, and international distribution expose acquirers to IP and export controls that often surprise buyers.

Checklist

Confirm all training data licenses and consent artifacts; request redacted proof of right to use.
Review source licenses for included open source components (GPL, Apache, proprietary).
Assess export control risks if model weights or tech rely on restricted components (US export rules for certain AI tech tightened since 2023).
Define IP indemnity scope and carveouts; insist on seller disclosure of outstanding claims.

8. Scoring rubric (quick triage)

Use a simple 0–3 per category system to quantify risk: 0 = Fail, 1 = High Risk, 2 = Moderate Risk, 3 = Low Risk.

Business & team (0–3)
Technical architecture (0–3)
Model provenance (0–3)
Governance & compliance (0–3)
Commercial terms & pricing (0–3)
Integration readiness (0–3)

Score 15–18: proceed with standard terms. Score 10–14: proceed with mitigations and escrow. Score <10: require remediation or walk away.

9. Practical templates and artifacts to request (AI Vendor Data Pack)

Ask for a single zipped dataset containing:

Model card, architecture diagram, and version history.
Redacted dataset samples and training/labeling SOPs.
Service metrics (SLA, incident reports, MRR snapshots).
Red team summary, DPIA, and insurance info.
Key contracts for foundation models and cloud providers.

10. Post‑deal clauses every acquirer should insist on

Telemetry access: continuous production metrics delivered to your monitoring stack.
Escrow & portability: model artifacts and exportable data on a time schedule tied to payments.
Performance milestones: phased payments contingent on latency, accuracy, and drift metrics.
Kill switch & rollback: contractual right to freeze model updates and trigger emergency rollback.
Audit rights: regular right to conduct technical and compliance audits with notice periods.

11. Who should run the scan — recommended team & timeline

Run this as parallel workstreams to compress time to decision. Typical 14–30 day cadence:

Day 0–3: Business & legal triage (CEO/CFO + GC).
Day 3–10: Technical audit and POC (CTO + lead ML engineer + SRE).
Day 7–14: Governance review and regulatory check (compliance officer + external counsel for AI law).
Day 14–30: Negotiate terms, escrow and post‑close plan (procurement + legal).

12. Case study vignette: why model provenance saved a deal

Early in 2026 a mid‑market acquirer nearly closed on an NLP vendor that had excellent demos. During the deal scanner review the acquirer discovered the vendor had fine‑tuned a high‑performance chatbot using a mix of proprietary customer transcripts and purchased scraped datasets without proper licensing. That provenance gap created a potential IP and privacy exposure that would have transferred to the acquirer. By insisting on redaction, a short retraining plan, and escrowed artifacts, the acquirer closed a safe deal — and negotiated a 20% reduction in price to cover remediation costs.

13. Advanced strategies and future proofing (2026+)

To stay ahead:

Prefer multi‑backed architectures: avoid single‑foundation model lock‑in by requiring adapters that let you swap model providers (partner onboarding patterns).
Insist on model lineage tooling: model provenance platforms that create immutable lineage traces will be mainstream by 2027 — leverage them now (perceptual provenance & storage).
Embed compliance automation: require integration points for automated DPIA and audit export reports (artifact export tooling).
Negotiate shared R&D commitments: co‑funded guardrails or labeling pipelines reduce long‑term TCO and align priorities.

Bottom line: In 2026, AI vendor diligence must treat models, data and governance as business assets. A fast, structured deal scanner reduces legal exposure, integration surprises, and protects long‑term ROI.

Actionable takeaways — run this today

Before any demo, run the Business & Team filter; don’t proceed if the vendor scores low.
Request the AI Vendor Data Pack up front and set a 14‑day window for delivery.
Run a 7‑day technical POC that stresses observability, security, and cost under load.
Insist on contractual portability, telemetry access, and milestone‑based payments.
Score vendors on the 0–3 rubric and only proceed with remediation plans for low scores.

Downloadable checklist & template

Use the checklist above as your master deal scanner. For speed, create a two‑page RISK SUMMARY (one page: scores & red flags; one page: remediation plan & milestones) to include in every LOI. If you want a ready‑made template that maps questions to artifacts and contract clauses, reach out to our marketplace team for the downloadable Deal Scanner Template tailored to acquirers and partners.

Final note on partnership risk

Partnerships with platform titans (like the Apple/Gemini example) can accelerate product velocity — but they also concentrate strategic risk. When a vendor depends on a single foundation model provider, treat that dependency as material risk and price protections accordingly. Require runbooks for vendor/provider outages and an agreed migration path should access change.

Call to action

Ready to run a deal scan on a target? Download our Deal Scanner Template or book a 30‑minute advisory review with our acquisition and technical due‑diligence team. We’ll map the checklist to your procurement process and deliver a prioritized remediation plan you can use in negotiations.

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