Infographic answering: What specific questions will a buyer likely ask during due diligence for an AI startup, and how can we best prepare our answers and documentation?

What specific questions will a buyer likely ask during due diligence for an AI startup, and how can we best prepare our answers and documentation?

What Buyers Ask During Due Diligence for an AI Startup — And How to Prepare

For founders of AI startups, the due diligence phase of an M&A process can feel like a high-stakes interrogation. But in reality, it’s a structured, methodical process designed to validate the business’s value, surface risks, and confirm strategic fit. The more prepared you are, the more leverage you retain — and the smoother the path to closing.

At iMerge, we’ve advised on numerous software and AI transactions, and we’ve seen firsthand how well-prepared sellers can command stronger valuations and more favorable terms. In this article, we’ll walk through the specific questions buyers are likely to ask during due diligence for an AI startup — and how to prepare your answers and documentation to inspire confidence, not concern.

1. Product and Technology: Is the AI real, scalable, and defensible?

Buyers — especially strategic acquirers and technical investors — will scrutinize your AI stack to determine whether your product is truly differentiated or simply riding the hype cycle. Expect questions like:

What proprietary models, algorithms, or data pipelines have you developed?
Is your AI trained on proprietary data, open-source datasets, or third-party sources?
How do you ensure model accuracy, fairness, and explainability?
What is your model retraining cadence and infrastructure?
What are the compute costs associated with inference and training?

How to prepare: Maintain a detailed technical architecture document, versioned model documentation, and a data lineage map. Be ready to demonstrate how your AI delivers measurable outcomes — not just predictions, but business value. If you use third-party APIs (e.g., OpenAI), clarify your dependency risk and mitigation strategy.

2. Data Rights and Compliance: Do you own the data — and is it legally usable?

AI startups often rely on large datasets, but buyers will want to know:

Do you have the legal right to use and commercialize your training data?
Are there any data privacy or IP risks (e.g., scraped web data, user-generated content)?
How do you comply with GDPR, CCPA, and other data regulations?

How to prepare: Organize data source agreements, user consent records, and privacy policies. If you’ve used synthetic or anonymized data, document your methodology. Buyers will also want to see your data governance policies and any third-party audits or compliance certifications.

3. Go-to-Market and Revenue Model: Is there a repeatable, scalable business?

AI technology alone doesn’t justify a premium valuation — buyers want to see a viable business model. Expect questions such as:

What is your pricing model — usage-based, seat-based, or flat-rate?
What percentage of revenue is recurring vs. project-based?
What are your customer acquisition costs (CAC) and lifetime value (LTV)?
What is your churn rate and net revenue retention (NRR)?

How to prepare: Build a clean, investor-grade financial model with clear assumptions. Segment revenue by product line, customer cohort, and geography. If you’re pre-revenue, articulate a credible path to monetization with early traction metrics (e.g., pilots, LOIs, pipeline).

For more on financial metrics that matter, see What Are the Key Financial Metrics Buyers Look for in a Software Company?

4. Intellectual Property: Is your IP protected and assignable?

Buyers will want to ensure that your core technology is protected and that they can legally acquire it. Key questions include:

Who owns the codebase and models — the company or individual contractors?
Are there any open-source components, and are they properly licensed?
Have all employees and contractors signed IP assignment agreements?

How to prepare: Maintain a clean IP assignment trail, including signed agreements from all contributors. Conduct an internal audit of open-source usage and ensure compliance with license terms. If you’ve filed patents, have documentation ready — even provisional filings can add value.

For more on this topic, see How Do I Handle Intellectual Property Rights in the Sale of My Tech Business?

5. Team and Talent: Who’s behind the AI — and will they stay?

In AI, talent is often the most valuable asset. Buyers will ask:

Who are the key technical leaders, and what are their backgrounds?
What is your retention plan for critical team members post-acquisition?
Are there any key person risks?

How to prepare: Create a team org chart with bios, roles, and tenure. Highlight any retention agreements or equity incentives. If your company is heavily reliant on a single founder or researcher, consider how to mitigate that risk — a topic we explore in What’s the Best Way to Handle Key Person Risk Before Selling?

6. Competitive Landscape and Moat: What prevents others from doing the same?

AI is a fast-moving field, and buyers will want to understand your defensibility. Expect questions like:

Who are your direct and indirect competitors?
What is your unique advantage — data, distribution, domain expertise?
How do you plan to stay ahead as models commoditize?

How to prepare: Develop a clear competitive matrix and articulate your moat. This could be proprietary data, vertical integration, regulatory barriers, or a unique user experience. Avoid vague claims — back up your positioning with evidence.

7. Legal and Corporate Structure: Are there any red flags?

Buyers will conduct a full legal review, including:

Cap table accuracy and investor rights
Outstanding liabilities or litigation
Contractual obligations, including customer SLAs and vendor agreements

How to prepare: Assemble a clean data room with your cap table, charter documents, board minutes, and key contracts. Address any convertible notes, SAFEs, or option grants that could complicate the deal. If you’ve raised venture capital, be ready to discuss liquidation preferences and investor consents.

For a broader checklist, see our guide on Due Diligence Checklist for Software (SaaS) Companies.

Final Thoughts: Preparation Is a Value Driver

Due diligence is not just a box-checking exercise — it’s a trust-building process. The more organized, transparent, and thoughtful your responses, the more confidence a buyer will have in your business. That confidence translates into better terms, fewer surprises, and a higher likelihood of closing.

Firms like iMerge specialize in helping AI and software founders prepare for this process — from pre-LOI positioning to post-close integration planning. We know what buyers look for, and we help you present your company in the best possible light.

Founders navigating valuation or deal structuring decisions can benefit from iMerge’s experience in software and tech exits — reach out for guidance tailored to your situation.