The first call already happened, just not with you
Your VC pitch is next Tuesday. You've been preparing the deck. You've been refining the narrative. You've been rehearsing the demo.
What you might not have prepared for: by Tuesday morning, the VC's tooling has already crawled your codebase visibility, your tech press coverage, your hiring patterns, your customer reviews, your public AI claims. More than 75% of VC deal reviews are now informed by AI and data analytics, and over 90% of VCs use AI tools for investment workflows.
By the time you walk in, the VC has a pre-written list of technical questions calibrated to your specific situation. The founders who close strong valuations are the ones who anticipated those questions and have specific, confident answers. The founders who improvise lose the room before the slide deck ends.
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This piece is the 12 questions the VC's tooling and partner are most likely to surface. Each one with a specific answer that compresses the conversation in your favor.
The 12 questions, with the answers that work
1. "Walk me through your technical architecture in 90 seconds."
The first filter. VCs use the answer to judge how clearly you think about the engineering, not the engineering itself. Most founders take 5 minutes; the strong ones take 90 seconds.
The structure that works: data sources → ingestion → core processing → output/UI → operating infrastructure. Five components, one sentence each, name the technology choice and why. If you can't do this in 90 seconds, practice. The polish matters more than people realize.
2. "What does the cost structure of one customer look like at 10x scale?"
VCs probe scalability architecture, security model, and data handling at projected scale. The honest answer requires unit economics modeling. If the answer is "we'd figure it out at scale," that's the answer that signals you haven't.
Be ready with: current per-customer infrastructure cost, projected 10x cost (showing it scales sublinearly or you've thought about the inflection points), and the named optimizations available if cost shape becomes a problem.
3. "Where did your training data come from, and do you have rights to use it?"
The deal-breaker question. If you can't document training data provenance with specific licenses or vendor terms, this is where the deal dies. Train this answer hard.
The strong answer names sources, names licenses or vendor agreements, and notes any limitations. The weak answer talks about "various public sources" and quickly moves on.
4. "How does your model perform against business outcomes, not benchmarks?"
Eval scores don't impress VCs the way they used to. Business-outcome metrics do. Acceptance rate, retention impact, revenue lift, cost-per-successful-interaction. Have one or two of these ready with real numbers.
5. "Who on your team can you not lose?"
The key-person risk question. The honest answer names specific engineers and explains what would happen if any one of them left. VCs price this risk. Founders who haven't thought about it carry it without realizing.
Be honest, but lead with mitigation: documentation, named backups, equity structure that retains. Don't pretend you have zero key-person risk; nobody does.
6. "Show me how you'd respond if your primary LLM vendor doubled their prices tomorrow."
The vendor lock-in question. The strong answer has a named alternative vendor, a documented migration cost in dollars and weeks, and a model abstraction layer that supports the swap.
The weak answer is "we'd figure it out." This is a question your VC's AI tool has already pre-checked against your public technical posts.
7. "What's the biggest assumption in your AI strategy that, if wrong, kills the business?"
The intellectual honesty test. Strong founders have an answer because they've thought about it. They name a specific assumption, why it might be wrong, and what they're doing to validate it.
Founders who answer "we don't really have one" are signaling they haven't pressure-tested their own thesis. VCs notice.
8. "Where does customer data flow, and how is it handled?"
Recent AI startup due diligence puts data handling at the top of the technical review. The answer should be specific: data flow from collection through processing to storage to deletion, with vendors named, consent flagged, retention articulated.
Founders who have done this audit answer in 90 seconds. Founders who haven't take 5 minutes and miss things.
9. "What would I find in your model that would surprise me?"
The bias question, asked indirectly. VCs want to know if you've audited your model for unintended behaviors. The strong answer names a specific bias or limitation you've found and what you did about it.
Diligence tools now flag hidden biases automatically. Founders who pre-empt the finding control the narrative. Founders who don't, get the finding dropped on them.
10. "Walk me through your last technical incident."
What you're being tested on: do you have a post-incident review discipline? Strong founders describe a specific incident, the specific fix, and the architectural change that followed. Weak founders either describe an incident vaguely or claim there hasn't been one.
Claiming zero incidents is worse than describing one. Zero incidents at any meaningful scale signals weak detection, not strong engineering.
11. "How fast can you ship a major change?"
The velocity question. The strong answer cites specific deploys: time from commit to production, number of deploys per week, rollback frequency. Specific numbers project competence.
12. "What's the closest thing to a regulatory headline that could affect your AI?"
EU AI Act enforcement begins August 2025 with fines up to €35M or 7% turnover for prohibited practices. Sector-specific rules are multiplying. VCs are pricing regulatory risk.
The strong answer names the specific regimes relevant to your customers, your compliance posture, and your monitoring approach. The weak answer is "we don't think regulation will affect us." The weak answer is no longer credible.
What changes between strong answers and weak answers
The pattern across the 12: strong answers are specific, numerical, and named. Weak answers are vague, qualitative, and theoretical.
VCs are not testing whether you have the perfect answer to each question. They're testing whether you've thought about each question deeply enough to answer with specifics. A founder who says "we haven't validated that yet but here's our plan to" beats a founder who says "we feel good about that" every time.
How Logiciel fits this conversation
Most founders who reach out to us before a fundraise have a strong product story and a thin technical readiness layer. The architecture works. The diligence-grade answers don't exist yet.
The work we do is the technical readiness audit. We run the 12 questions against your current state, identify the answers that need real engineering work to be defensible, and ship the engineering that closes the gap. The audit produces a 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: 1-2 weeks for the audit, 4-8 weeks for the engineering fixes if any are needed. The investment is small relative to the valuation impact of a clean diligence.
Why Audit-Ready Beats Audit-Survived Every Time
Inside a 120-day remediation that turned three material findings into zero at follow-up.
Call to Action
The 30-minute move
Book a working session with a senior Logiciel engineer. Bring your pitch deck and a current technical overview. We'll walk through the 12 questions against your specific situation and tell you which ones need work before the fundraise opens.
Book the 30-minute VC readiness session →
Frequently Asked Questions
What if we get a question we can't answer well?
Honest beats glib. "We haven't fully validated that yet, but here's our plan to" reads better than a confident but vague non-answer. VCs price both information and integrity.
How early should we run this audit?
Six months before opening the fundraise is ideal. Three months is workable. Less than three months means findings can't always be remediated in time.
What's the most commonly underestimated question?
Training data rights. Founders treat it as a footnote; VCs treat it as deal-defining. Document it carefully.
Can we use AI to help prepare our answers?
For polishing, yes. For substance, no. VCs catch AI-polished answers quickly because they're too smooth and too generic. The substance has to be yours.
Is this different for technical vs. non-technical founders?
Non-technical founders need a strong technical co-founder or CTO in the room. The 12 questions are calibrated for technical depth; a non-technical founder alone struggles to answer half of them well. The pairing matters. --- Sources cited: - 75% of VC deal reviews informed by AI; 90% of VCs use AI tools - VC AI startup due diligence priorities - AI startup data handling diligence - EU AI Act Article 99 penalties
