For technical due diligence of generative AI models, specialists like Fractional AI, Fast Data Science, and Kenaz offer focused assessments on model defensibility, data lineage, and performance. Larger-scale acquisitions often use global consultancies like McKinsey, BCG, or Deloitte, which provide broader oversight of AI infrastructure, governance, and regulatory compliance.
Yes. For a generative-AI acquisition, I’d treat AI/model diligence as a specialized workstream separate from ordinary software technical DD. The key question is not merely “is the code good?” but whether the claimed AI capability is real, defensible, legally usable, and economically viable.
Firms worth looking at
pwc.com — Has a dedicated deal-oriented AI diligence offering covering model/product maturity, data, scalability, roadmap realism, compliance, and value creation. Good fit if you want a large-firm diligence process integrated with broader M&A work.
ey.com — Explicitly evaluates AI/ML models, data sources and pipelines, AI teams, product technology, architecture, and R&D. Particularly relevant if you want commercial + technical diligence combined.
fastdatascience.com — More specialized in actually evaluating ML models and selecting appropriate performance metrics rather than treating AI as another line item in a conventional software audit.
rokolabs.com — Focuses specifically on architecture, data pipelines, model strategy, evaluation frameworks, security, scalability, and engineering execution for investors/operators.
ideius.com — Explicitly targets buyers/acquirers and covers model choices, evaluation rigor, data handling, inference cost, reliability, maintainability, and vendor/model lock-in.
semanti.com — Covers model provenance, evaluation coverage, training-data rights, vendor exposure, engineering, team dependency, and regulatory issues.
customlabs.io — Specifically positioned for PE/VC/corporate-development acquisitions, including model/vendor lock-in, technical debt, and operating costs.
There are also independent senior-operator options such as kencorigliano.com, which explicitly covers architecture, models, training/fine-tuning, data provenance, RAG/agents, inference economics, team, and moat.
What I would make them test
For a GenAI startup, I'd put these at the center of the diligence request:
What is actually proprietary?
Is there a genuinely differentiated model, fine-tuning/data asset, evaluation system, retrieval layer, agent architecture, or is it primarily an API wrapper?
Model performance
Reproduce the company's claimed benchmarks using the same data and methodology, then run independent tests on your own representative workloads. Look at accuracy/quality, hallucination, robustness, latency and failure modes—not just benchmark scores.
Training/fine-tuning data provenance
Establish exactly what data was used, who owns it, what licenses permit, and whether customer data entered training. Data provenance is receiving particular scrutiny in current AI transactions.
Evaluation infrastructure
Determine whether the target has a credible continuous eval suite or is selecting favorable examples. This is one of the areas where AI-specific DD differs substantially from conventional software DD.
Foundation-model dependency
Quantify dependence on OpenAI/Anthropic/Google/open-source models, including whether the economics or product break if pricing, APIs, context windows, model behavior, or terms change.
Inference economics
Reconstruct cost per customer/task at today's volumes and at the company's projected scale. A product can have excellent gross margins at current volume and very different economics at 10× or 100× usage.
Reproducibility and technical debt
Can another team reproduce the claimed performance? Examine training pipelines, prompts, checkpoints, eval harnesses, deployment infrastructure, experiment tracking and undocumented dependencies.
Team/key-person risk
Identify whether the model and data infrastructure are genuinely institutionalized or effectively reside in one or two researchers.
Security and adversarial behavior
Especially for agents: prompt injection, data exfiltration, tool abuse, jailbreaks, privilege boundaries, and isolation of customer data.
Post-close rebuild test
I'd actually ask the diligence team: “If we acquired the company and lost half the engineering team tomorrow, what could we reproduce, and what would we be unable to reconstruct?”
How I'd structure the engagement
For a serious acquisition, I'd consider two complementary specialists rather than asking one generic technology DD provider to do everything:
AI/model specialist → independently tests the models, data, evals, AI architecture and claims.
Traditional tech-security DD → independently examines codebase, cloud architecture, cybersecurity, SDLC, infrastructure, technical debt and engineering organization.
Then have the deal team reconcile the two into valuation-impacting findings.
If you tell me roughly the target's technology (LLM/foundation model vs. fine-tuned models vs. RAG/agents), deal size, and whether you're a strategic acquirer or PE/VC, I can narrow this to a shortlist of 5–7 providers and what each is particularly suited to, including firms with actual M&A track records.
When acquiring a generative AI startup, evaluating the core models requires a specialized mix of machine learning engineering, data science, legal/compliance auditing, and cloud economics. Standard IT due diligence firms often lack the granular capability to audit weights, training data provenance, inference costs, and fine-tuning integrity.
The market relies on distinct categories of providers for AI model technical due diligence:
Specialized AI/ML Engineering & Product Consultancies: Boutique technical firms focus explicitly on AI architectures, model evaluation, and inference economics. Examples like Matchpoint Partners test model dependencies, production traces, unit economics (cost per token/inference), and data pipeline integrity rather than just looking at surface-level demos.[](https://www.matchpoint-partners.com/practices/ai-technical-due-diligence-integration.html)
Specialized Risk, Bias, and Compliance Auditors: Regional or specialized risk firms (such as Schneider Downs or specialized legal-tech auditors) focus heavily on data provenance, copyright/IP exposure, training data bias, and privacy compliance.[](https://schneiderdowns.com/ai-audits/)
Key Areas a Proper AI Diligence Team Will Probe
Model Architecture & Defensibility: Are they relying entirely on a wrapper around OpenAI/Anthropic/Llama, or do they possess proprietary fine-tunes, custom architectures, or proprietary RLHF (Reinforcement Learning from Human Feedback) datasets?
Data Provenance & Copyright Risk: Vetting the training data pipeline to uncover potential IP liabilities, lack of opt-outs, or scraping vulnerabilities.
Unit Economics & Scaling Costs: Analyzing inference efficiency, GPU/compute dependencies, and whether gross margins break down at high user concurrency.
Evaluation & Benchmarking: Re-running deterministic evaluation suites rather than trusting the startup’s self-reported benchmark metrics.
To help narrow down which type of partner fits your deal, could you share:
What is the estimated deal size or valuation?
Is the startup built on proprietary foundational models or third-party APIs/open-weights?
Depending on your budget, depth required, and deal size, several categories of organizations and experts assist with generative AI model due diligence:
1. Elite Strategy & Management Consultancies (Best for comprehensive commercial + deep tech integration)
These firms have rapidly built out specialized generative AI and machine learning practices to handle end-to-end M&A diligence.
McKinsey QuantumBlack : The analytics and AI arm of McKinsey, heavily experienced in evaluating complex data pipelines, custom foundation/fine-tuned models, and enterprise AI readiness.
2. Specialized Technical Due Diligence & Engineering Firms (Best for deep-dive code, architecture, and cost audits)
If you need engineers to rip open the GitHub repositories, review weights/training recipes, and audit cloud infrastructure bills:
Crosslake : A premier tech due diligence provider for private equity that evaluates software architecture, team capabilities, and modern data/AI stacks.
Boutique AI/ML Consultancies : Specialized engineering shops (such as Neurons Lab or regional data-science engineering firms) can be contracted independently or via an investment bank to do a targeted "under-the-hood" model audit covering weight integrity, training data bias, and fine-tuning efficiency.[](https://neurons-lab.com/articles/best-boutique-ai-consulting-firms-experience-in-financial-services/)
3. Big Four & Risk Advisory Firms (Best for compliance, governance, and IP/legal risk)
Deloitte, PwC , and RSM : Ideal if the target deals with heavily regulated verticals (fintech, healthtech). They evaluate algorithmic bias, regulatory compliance (like EU AI Act readiness), data privacy compliance (GDPR/HIPAA regarding training data), and corporate governance frameworks.
Specialized AI Audit Boutiques (e.g., Kevin D. Oden & Associates ): Focus tightly on independent AI model validation, algorithmic fairness testing, and quantifying technical debt in machine learning workflows.[](https://kdoden.com/ai-governance)
Key Areas a GenAI TDD Expert Will Probe:
Data Lineage & Copyright: Where did the training/fine-tuning data come from? Are there lurking copyright/IP infringement liabilities?
Moat vs. Wrapper: Is the startup relying purely on a thin API wrapper around OpenAI/Anthropic/Llama, or do they possess proprietary fine-tuning, RLHF (Reinforcement Learning from Human Feedback), or proprietary vector databases/RAG architectures?
Inference Economics: What are the unit economics per query? Are their margins sustainable, or will scaling crush them under high GPU/inference costs?
Model Robustness: Evaluating benchmark performance, drift vulnerability, and safety guardrails against jailbreaks.
Are you looking for a large global firm (like a Big Four or strategy house) for an end-to-end institutional deal, or a boutique machine learning engineering shop for a granular code-and-model audit? Let me know the stage and focus of the startup to narrow it down.
Yes. For an acquisition, I’d treat AI/model technical diligence as a specialized workstream, separate from conventional software/IT diligence. The right provider depends heavily on whether the startup trains its own models, fine-tunes third-party models, or is primarily an application layer.
Firms I’d put on the diligence shortlist
PwC Deals / AI & Technology Diligence — probably the most straightforward option if you want an M&A-oriented team that can combine AI assessment with conventional technology diligence. PwC specifically markets AI technology diligence for acquisitions, including AI maturity, scalability, operating model, and technology risk.
Accenture / Faculty — particularly relevant if the target's value depends heavily on model behavior, evaluation, red-teaming, or AI safety. Faculty is now part of Accenture, and Accenture/Faculty was recently announced as an independent evaluator of Anthropic's models.
METR (formerly ARC Evals) — worth considering for a genuinely frontier-model company where you need sophisticated empirical evaluation rather than ordinary IT diligence. METR has been involved in independent evaluation of advanced AI systems.
Apollo Research / Redwood Research — specialist research organizations worth considering when the acquisition risk includes agentic behavior, deception, autonomy, or unusual model capabilities. These are much more specialized than a traditional Big Four diligence team.
NIST's GenAI evaluation methodology — not an M&A provider, but an excellent benchmark for defining the technical scope of the work. NIST's GenAI program evaluates models across modalities and specifically addresses capabilities, limitations, adversarial testing, and reliability.
What I'd actually have them diligence
For an acquisition, I'd ask for a model-specific technical DD report covering at least:
Area
Questions to answer
Model/IP
What is actually proprietary? What is merely API access, fine-tuning, RAG, prompts, or orchestration?
Weights
Who owns the weights? Can they legally be transferred? Are there restrictions on commercial use?
Training data
Provenance, licenses, consent, synthetic data, contamination, copyrighted material
Model performance
Independent evaluation against the startup's claimed benchmarks and real customer workloads
Benchmark integrity
Were test sets contaminated, cherry-picked, or used during development?
This is particularly important for data provenance: current M&A scrutiny is increasingly focused on whether the datasets used to train/fine-tune models were legitimately licensed and whether user consent was obtained.
I'd use the NIST AI RMF Generative AI Profile as one of the underlying frameworks for the evaluation; it explicitly addresses GenAI risks across the AI lifecycle and provides hundreds of recommended actions.
One important distinction
If you tell me what kind of GenAI startup you're acquiring—for example:
foundation-model company,
proprietary fine-tuned LLM,
computer-vision model,
voice/audio model,
RAG/application company,
AI agent company, or
vertical SaaS with AI embedded—
I can give you a shortlist of 5–10 specific diligence providers, including which ones are appropriate for model evaluation vs. IP/data provenance vs. cybersecurity vs. conventional tech DD, and what I'd ask each one to quote.
Specialized AI/ML Consultancies: Firms like Fast Data Science or boutique data science agencies analyze underlying model weights, training metrics, benchmark performance, and potential over-reporting of accuracy.[](https://fastdatascience.com/ai-due-diligence/)
Experienced AI/ML engineering leaders or academics who act as specialized consultants to separate proprietary innovation from wrapper-based hype built on top of third-party APIs (like or ).[]()
Key Focus Areas During Evaluation
Data Provenance & Rights: Verifying licensing agreements, copyright compliance of training data, and whether the startup holds clean ownership or clear usage rights over proprietary datasets.
Model Architecture vs. API Wrappers: Determining whether the startup has a genuine defensible moat (custom trained/fine-tuned weights) or if it relies entirely on thin prompts over foundational commercial models.
Infrastructure & Inference Economics: Reviewing cloud compute consumption, token costs, latency, and scaling economics to ensure unit profitability under real-world usage.
For an acquisition of a generative AI startup, you usually want AI-specific technical due diligence, not just a traditional software/code review. The right provider depends on the target (foundation model company vs. AI application layer vs. AI infrastructure), deal size, and how much independent model expertise you need.
Here are the main categories of firms and specialists that buyers commonly use:
1. AI-focused technical diligence boutiques
These firms specialize in answering questions like: “Is the model capability real, defensible, and scalable?”
Examples:
Matchpoint Partners — focuses on AI technical diligence for investors and acquirers, including model performance, architecture, data rights, security, economics, and team dependency.
CustomLabs — provides investor-oriented AI diligence focused on validating AI claims, technical debt, lock-in, and operating costs.
Cantor Analytics Advisory — performs quantitative and AI diligence for M&A transactions, including model validation, data dependencies, and architecture reviews.
ideius — offers independent reviews of AI products covering model choices, evaluation methods, data handling, scalability, security, and reliability.
2. Large consulting firms with AI M&A practices
Useful for larger acquisitions, PE-backed transactions, or when you need integrated technology, cyber, commercial, and operational diligence.
pwc.com — evaluates AI maturity, product technology, scalability, data/compliance risks, and operating model issues.
EY-Parthenon — provides AI diligence covering AI in products, data pipelines, ML readiness, R&D processes, and operations.
deloitte.com — provides AI diligence for investors assessing AI capabilities, risks, and value creation opportunities.
3. Independent ML researchers / former AI leaders
For a serious generative model acquisition, I would often add one or two independent experts alongside a diligence firm. They can evaluate whether the startup’s claims are technically plausible.
Look for people with experience in:
Training large language models (LLMs)
Post-training methods (RLHF, DPO, fine-tuning)
Evaluation design and benchmark contamination
Retrieval-augmented generation (RAG)
Agent reliability
Inference optimization
GPU economics
Safety and red-teaming
A strong diligence team often includes:
Former research scientists from major AI labs
ML infrastructure engineers
Evaluation specialists
Security researchers
What they should actually test
For a GenAI acquisition, I would expect diligence to cover:
Model capability
Are claimed benchmarks reproducible?
Are improvements due to proprietary methods or just more compute/data?
How does the model compare against open and closed alternatives?
Is performance robust outside curated demos?
Data
Who owns the training data?
Are licenses transferable after acquisition?
Are there copyright, privacy, or consent risks?
How much proprietary data advantage exists?
Model/IP defensibility
Is the company actually building models, or mainly orchestrating third-party APIs?
Are weights, training code, datasets, and evaluation systems owned?
Are there critical dependencies on OpenAI, Anthropic, Google, Meta, NVIDIA, cloud providers, or other vendors?
Economics
Training cost
Inference cost per user/task
GPU commitments
Gross margins at scale
Ability to migrate models or infrastructure
Production maturity
Evaluation pipelines
Monitoring
Hallucination measurement
Safety controls
Security against prompt injection/data leakage
Reliability under real workloads
Team dependency
Who actually built the model?
Are key researchers staying?
Is knowledge concentrated in a few people?
How I would structure the diligence team
For a meaningful GenAI acquisition:
Core diligence provider
AI technical diligence boutique or Big Four AI practice
+ Independent model expert
Former frontier-model researcher or ML leader
+ Security reviewer
Especially if agents, enterprise data, or autonomous workflows are involved
+ Legal/IP review
Training data rights, model licenses, open-source obligations
For a startup whose main asset is a foundation model, I would spend less time on generic software architecture and more on reproducibility, data provenance, evaluation methodology, compute economics, and whether the capability survives without the current team.
If you share the type of target (foundation model, AI application/SaaS, agent company, data/AI infrastructure, or robotics) and approximate deal size, I can suggest a more specific diligence team composition and checklist.
Acquiring a generative AI startup requires looking far beyond traditional software due diligence. You aren't just evaluating codebases and cloud infrastructure; you are evaluating training data provenance, model architecture defensibility, inference economics, and compliance with emerging regulations.
Who helps with this specialized technical due diligence falls into a few distinct categories, depending on the depth and budget of your M&A team:
Specialized Risk, Audit, and Accounting Firms: Major regional and national accounting/risk advisory firms that have built dedicated Responsible AI and algorithmic auditing practices. They focus heavily on governance, bias/fairness testing, and adherence to frameworks like the EU AI Act or NIST AI RMF (e.g., Schneider Downs AI Audit Services).[](https://schneiderdowns.com/ai-audits/)
Elite Independent AI Researchers & Academics: Many M&A advisory teams hire PhD-level researchers or professors from top-tier computer science labs as independent expert consultants to run a red-teaming exercise or evaluate whether the startup's core model breakthroughs are genuinely novel or just thin wrappers over open-source foundation models (like Llama or Mistral).
To help narrow down which type of partner fits your acquisition, could you share:
Is the startup training proprietary foundation models from scratch, or are they fine-tuning/wrapping open-source or third-party APIs?
What is the rough valuation or deal size of the acquisition?
If you’re acquiring a generative-AI startup, I’d look for AI/ML-specific technical diligence, not just a conventional software-code review. The specialist should independently test whether the claimed model advantage is real, reproducible, defensible, and economically sustainable.
Firms worth considering
pwc.com — Broad transaction diligence covering AI maturity, model/product architecture, scalability, data, compliance, roadmap realism, and value creation. Good fit when you also need conventional technology/transaction diligence around it.
deloitte.com — Explicitly offers AI diligence in acquisition situations, including assessing the target's AI capabilities, data and talent and potential post-close value/risk.
ey.com — Covers ML/model readiness, AI techniques, data sources and pipelines, data-science teams, and the commercial implications of AI. EY says it has conducted 50+ AI-focused engagements over the referenced two-year period.
rokolabs.com — More specialized: architecture, data pipelines, model strategy, evaluation frameworks, security, scalability, and engineering execution. It specifically markets the service to investors and PE firms.
cantoranalytics.com — Focused on AI/quantitative-system M&A diligence, including independent model validation against technical claims, architecture/scalability, data dependencies and integration risk.
fastdatascience.com — AI/ML specialist that specifically describes evaluating models and their reported metrics as part of investment diligence.
ideius.com — Boutique offering independent reviews for acquisitions, covering architecture/model choices, evaluations, data handling, security, scalability, inference cost, reliability, team dependency and maintainability.
What I would have them test
For a GenAI acquisition, the diligence mandate should go substantially beyond “review the code”:
Model claims — Reproduce the target's benchmark results using the same data and evaluation methodology, then test against appropriate external baselines.
Evaluation integrity — Look for benchmark contamination, cherry-picked examples, leakage, weak test sets, human intervention hidden behind demos, and discrepancies between offline evaluations and production performance.
Model provenance — Establish exactly which components are proprietary versus OpenAI/Anthropic/Google/open-source models, fine-tunes, embeddings, RAG, agents, etc.
Training/fine-tuning data — Trace ownership, licenses, consent, provenance, labeling and whether customer data is being used.
Actual moat — Determine whether the value resides in proprietary weights, training data, evaluation infrastructure, retrieval corpus, workflow/IP, distribution, or merely an orchestration layer over third-party models.
Inference economics — Reconstruct cost per request/customer, GPU utilization, latency, context-window consumption, caching, and sensitivity to foundation-model pricing.
Reproducibility — Ask whether another competent team could reproduce the claimed performance from the assets being acquired.
Security and abuse resistance — Prompt injection, data exfiltration, model extraction, poisoning, tenant isolation and agent/tool permissions where applicable.
Production reality — Compare the demo to logs, telemetry, failure rates, customer usage and human-in-the-loop intervention.
Key-person dependency — Identify whether the “secret sauce” exists in the acquired IP/data or primarily in a handful of researchers/engineers.
Post-close replacement cost — Estimate the people, compute, data and engineering investment required to maintain or reproduce the system.
For a model-heavy acquisition, I would seriously consider using two layers: a transaction/technology diligence firm such as PwC, Deloitte or EY-Parthenon plus an independent ML specialist such as Roko Labs or Cantor Analytics. The latter can challenge the actual model claims rather than simply incorporating management's technical assertions into a broader IT diligence report.
If you tell me (a) acquisition size, (b) whether the startup has proprietary weights vs. fine-tunes/RAG/API models, and (c) whether you're a strategic buyer, PE fund, or individual acquirer, I can give you a more targeted shortlist and a GenAI technical-diligence request list for the data room.
Conducting technical due diligence (TDD) on a generative AI startup requires looking far beyond traditional software code. You need to evaluate model defensibility, data provenance, inference efficiency, training pipeline integrity, and potential intellectual property or copyright liabilities. [](https://www.youtube.com/watch?v=TXyneyDcXyM)
The ecosystem of who helps with this specialized evaluation falls into a few distinct categories:
1. Specialized Tech Due Diligence & Engineering Boutiques
Code & Co.: Frequently hired by private equity and strategic buyers for tech M&A diligence, focusing heavily on digital defensibility, AI capabilities, product roadmaps, and open-source compliance. [](https://www.youtube.com/watch?v=RGpmDvQxdZ8&t=14)
2. Major Tech Consultancies & Global System Integrators (GSIs)
If the startup is enterprise-focused or operating at a massive scale, global firms have assembled specialized AI practices (often housing thousands of data scientists and engineers) to run deep diagnostics.
Deloitte, PwC, and EY: Offer comprehensive technology-driven advisory and M&A diligence services that blend financial tracking with software and AI infrastructure reviews.
Accenture & EPAM Systems: Deeply entrenched in AI engineering partnerships (e.g., with hyperscalers and GPU providers), allowing their technical teams to stress-test model deployments, RAG (Retrieval-Augmented Generation) pipelines, and infrastructure costs.
Infosys Consulting: Combines standard IT and cybersecurity evaluation with AI-driven frameworks to spot hidden red flags in data engineering and algorithmic design.
3. Boutique AI/ML Engineering Agencies
If you need hands-on code and weight inspection—such as analyzing whether a proprietary fine-tuned model is genuinely novel or just a thin API wrapper around an open-source model (like Llama or Mistral)—specialized AI development and engineering boutiques (such as LeewayHertz or regional custom-AI shops) are often brought in as subcontractors by the primary deal team to do a deep-dive code and architecture audit.
Key Focus Areas Your Due Diligent Partner Should Test:
Data Provenance & Copyright Risk: Vetting the training data for scraping legality, license compliance (e.g., GPL/open-source contamination), and PII (Personally Identifiable Information).
Defensibility & Moats: Determining if the startup owns proprietary fine-tuning methodologies, unique RLHF (Reinforcement Learning from Human Feedback) datasets, or if they are easily replicated.
Inference Economics: Calculating the true unit economics of running their models at scale (token costs, GPU latency, and infrastructure optimization).
To help narrow down who you should call, let me know:
Is the startup pre-revenue/early stage or growth stage?
Are they training foundation models from scratch or building applications on top of third-party APIs/open-weights?
Yes. For an acquisition of a generative-AI startup, I would use a specialist AI/ML technical-diligence team rather than relying solely on a conventional software DD provider.
Firms I’d shortlist
Cantor Analytics Advisory — particularly relevant if the core question is whether the target’s models actually perform as claimed. They explicitly do pre-transaction model validation, architecture/scalability review, data-dependency analysis, and integration-risk assessment.
Roko Labs — focuses specifically on technical AI DD, including model strategy, evaluation frameworks, data pipelines, security, scalability and engineering quality.
GenovateAI — more code-level and model-centric. They say they validate AI claims against the actual implementation, examine model architecture and data moat, and stress-test inference economics.
ideius — explicitly positions its service around buyers acquiring AI products, examining architecture/model choices, evaluation methodology, data handling, security, scalability, costs and reliability.
PwC — better suited if you want a large transaction-advisory platform around the specialist technical work. Its AI DD covers AI maturity, product/platform architecture, data/compliance risks, scalability and roadmap realism.
EY-Parthenon — another strong Big Four option, particularly for PE/corporate acquisitions. Its software strategy group explicitly assesses ML-model readiness, AI techniques, data sources/pipelines and data-science teams.
What I'd actually have them test
For a GenAI acquisition, I wouldn't ask for a generic "technical DD." I'd commission an independent AI/model validation workstream covering:
Model provenance — proprietary model vs fine-tune vs wrapper around OpenAI/Anthropic/etc.
Independent replication of performance claims — reproduce the seller's benchmarks and test on an independently constructed holdout set.
Reliability and failure modes — hallucination, adversarial inputs, prompt injection, model drift and degradation.
Reproducibility — can your team actually reproduce the model and evaluation results after the founders leave?
Team/key-person risk — who actually understands the model, training pipeline and evaluation stack?
Roadmap credibility — whether the claimed next-generation capabilities are technically plausible.
Post-close technical investment — what it will cost to make the system enterprise-grade.
The important distinction is that "does the model work?" is only one question. You want to establish whether the AI represents a durable asset that you're actually buying, rather than a sophisticated application sitting on somebody else's model API.
For an acquisition, my preference would be a specialist AI DD firm such as Cantor/Roko/GenovateAI doing the model-level work, alongside your normal software/security/IP diligence. A Big Four firm can coordinate the broader transaction DD, but I'd be reluctant to let a generalist team be the only people validating the model claims.
If you tell me roughly the target's technology (LLM, computer vision, voice, agents, RAG, proprietary foundation model, etc.) and deal size, I can give you a shortlist of 5–8 firms ranked by fit, likely engagement type, and what I'd ask each one to test.
Major Management Consultancies & Big Four Auditors: Firms like McKinsey, Deloitte, or PwC have dedicated AI/analytics practices, though they are typically engaged for larger enterprise-scale acquisitions.
Contractual/model-provider dependencies — Identify termination rights, API restrictions, model-use restrictions and vendor concentration.
