The diligence call you don't want to take
You're three weeks into a fundraise. Term sheet is on the table. The lead investor has scheduled a technical diligence call with their AI partner. They've already run their automated tools across your codebase, your product, and your public claims. They've cross-referenced your AI capability descriptions against your actual deployment.
The call opens with a specific question. Your stated training data sources don't match the data your model was trained on, based on their analysis. Can you explain the discrepancy?
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You can't, not in real time, not with confidence. The VC's partner is patient. They have other questions queued up.
This is the conversation most AI startups don't realize they're walking into. More than 75% of VC deal reviews are now informed by AI and data analytics, with over 90% of VCs using AI tools for investment workflows. The diligence finds things faster than founders can fix them. By the time the question lands, the valuation has already moved.
What VCs are actually checking
Recent venture due diligence checklists for AI startups concentrate on four categories: data quality and sourcing, model architecture and scalability, AI talent assessment, and intellectual property considerations. VCs want to understand training data sources, model performance metrics, technical team capabilities, and how the startup plans to maintain competitive advantages.
The specific questions that come up most often:
Where did your training data come from, and do you have rights to use it? VCs now check this against public sources. If your model was trained on scraped data without clear rights, that's an exposure that will surface in diligence.
Can your backend handle 10x today's user or data volume? VC playbooks specifically probe scalability architecture, security model, and data handling at projected scale. Founders who can't answer this lose the conversation.
What's the gap between your marketing claims and your actual capabilities? The diligence tools cross-reference your public website and pitch deck against your codebase. Inconsistencies surface automatically.
What are the hidden biases in your model? VCs are now looking for fairness audit evidence. Diligence tools flag hidden biases that can lead to inaccurate predictions or discriminatory outcomes. Founders who haven't run this audit themselves are about to.
The seven-item self-audit
The self-audit founders should run before the diligence call lands. Each item is something an investor's tooling will check. The founder who has the answer ready closes faster valuations than the founder who doesn't.
1. Training data provenance and rights
For every model in production, name the data sources, the rights you have to use them, and the documentation that supports those rights. If you're using a foundation model, name the vendor and their terms. If you fine-tuned, name the dataset and the license.
The failure mode: training data that came from "various public sources" without a written record. VCs flag this within minutes of starting diligence.
2. Model evaluation against business metrics
Your model passed eval. Eval against what? VCs want to see business-outcome metrics, not just accuracy scores. The acceptance rate. The user satisfaction trend. The cost per successful interaction. The retention impact.
Models with strong technical eval and weak business metrics are common. They also get marked down in diligence.
3. Data handling and privacy posture
Where does customer data flow? Through which vendors? Under what consent? What's the retention? Recent AI startup diligence checklists put data handling at the top of the technical review. Founders should be able to trace any piece of customer data from collection to deletion in 15 minutes.
4. Vendor and model lock-in math
If your primary LLM vendor cuts you off or doubles their price, what happens? VCs want a documented migration path with named alternatives and rough cost in dollars and weeks. The startup with "we'd figure it out" as an answer is the startup the VC marks as a risk.
5. Public claims reconciled with actual capabilities
Your website says your AI does X. Your product actually does Y. The gap, if there is one, is the most preventable category of diligence finding. Audit your public claims against your production behavior every 90 days.
6. Security review of AI-specific surfaces
Prompt injection. Data exfiltration via model output. Inference cost attacks. These are AI-specific security surfaces that traditional security audits don't always cover. VC tooling now checks for them. So should you.
7. The talent dependency map
Who on your team would the company struggle to operate without? If the answer is one person, that's a key-person risk VCs will price. If you can name three engineers who can operate the platform, the risk is bounded. Founders who haven't thought about this often have a higher key-person concentration than they realize.
Ask yourself: If a VC's technical partner ran an AI audit on your company tomorrow, how many of the seven items would surface a discrepancy or a gap? If the answer is more than two, that's a valuation question you'll be answering instead of a product question.
What changes if the diligence finds something
Three outcomes, depending on severity.
The minor finding: the VC's tool flagged something, you have an explanation, the conversation moves on. Most diligence calls have one or two of these. They're not deal-breakers.
The substantive finding: the gap exists, the founder didn't know about it, and addressing it requires real work. The VC may proceed but the valuation moves down. Sometimes a deal closes anyway. Sometimes it doesn't.
The deal-breaker: training data rights issues. IP exposure on model architecture. Compliance violations that surface in the cross-reference. The deal dies. The founder spends three months explaining to other VCs why the previous deal didn't close.
The seven-item self-audit prevents most of the second and third outcomes. The cost is roughly a week of focused work. The benefit is the valuation conversation you wanted to have.
How Logiciel fits this conversation
Most founders who reach out to us about AI audit are 60-90 days from a fundraise and want to know what the diligence will find before the diligence happens. They have an AI product, some traction, and a growing sense that the technical surface has accumulated risk they haven't audited.
The work we do is the pre-diligence audit. The seven items, plus the deeper engineering review (audit trail, eval harness state, vendor inventory, security posture). We deliver the report your future VC's technical partner will produce, except we deliver it to you first, with named fixes you can ship before the diligence call.
Typical engagement: one week of focused work, ending with a report that says exactly what we found, what's fixable in the next 30 days, and what isn't.
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Call to Action
The 30-minute move
Book a working session with a senior Logiciel engineer. Bring your AI product overview and your current pitch deck. We'll walk through the seven items and tell you which one is most likely to surface a finding in your specific case.
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Frequently Asked Questions
We're not raising for another six months. Is this premature?
It's the right window. Most audit findings take 30-90 days to remediate. Starting now means the findings are fixed before the diligence rather than during it.
We're already in diligence. Is it too late?
Depends on what the VC finds. Issues that surface during diligence usually result in valuation adjustment, not deal collapse, but the adjustment is real. If you can pre-empt findings by raising them yourself with the fix in flight, you preserve valuation. The window is short.
What about acquihires and strategic acquisitions, not VC deals?
Same framework. Acquirers run the same diligence, often more thoroughly. The seven items apply to any transaction where someone is buying into your AI capability.
How is this different from a SOC 2 audit?
SOC 2 covers process controls. The AI audit covers AI-specific surfaces (training data, model behavior, prompt injection, vendor lock-in). They overlap on data handling and rarely substitute for each other.
What's the most common deal-killing finding?
Training data rights. Founders who can't document the rights to their training data, or who used scraped data without clear license, face the most binary outcomes. This is the one item to audit first if you only have time for one. --- Sources cited: - 75% of VC deal reviews informed by AI; 90% of VCs use AI tools - VC due diligence priorities: data quality, model architecture, talent, IP - AI startup data handling diligence priorities
