“Is this a real system or a prompt wrapper” is the first question
The first thing a sharp technical diligence reviewer checks is whether your AI capability has real engineering behind it or is a single API call to GPT-4o with a nice UI. This isn’t inherently disqualifying — plenty of legitimate, valuable products are thin on custom ML and thick on excellent product integration — but it changes how they evaluate your defensibility and your valuation multiple. Be ready to walk through your actual architecture: retrieval design, evaluation approach, what’s proprietary versus what’s a vendor API, and where your engineering effort has actually gone.
Data: what you have, how you got it, and who owns it
Reviewers dig into your data assets specifically because data is often the actual moat behind an AI product, more than the model itself. Expect questions on: what proprietary data do you have that a competitor couldn’t easily replicate, how was it collected (and do you have clear rights to use it — this matters a lot if any of it is user-generated or came from a data-sharing agreement), and how does your data advantage compound over time as you get more users.
A vague answer here — “we have a lot of data” without specifics on volume, uniqueness, or legal clarity — is a common red flag that gets a deal re-scrutinized or repriced.
Evaluation and quality metrics
Sophisticated technical reviewers will ask how you know your AI feature works — not “does it feel good,” but what’s your evaluation methodology, what metrics do you track, and how do you know quality hasn’t regressed as you’ve shipped changes. Teams with a real evaluation harness (a test set, tracked metrics, a process for catching regressions) demonstrate engineering maturity that stands out. Teams with no answer beyond “we test it manually” signal risk — it suggests quality is unmanaged and could degrade unpredictably as the product scales.
Vendor dependency and model risk
Expect questions on how dependent your core product is on a single AI vendor, and what happens to your product if that vendor changes pricing, deprecates a model, or has a major outage. This isn’t about avoiding vendor dependency entirely — using OpenAI or Anthropic’s APIs is completely normal and expected — it’s about whether you’ve architected reasonable resilience (can you swap models behind an evaluation harness, do you have fallback behavior for API failures) versus having zero contingency if your single point of dependency has a bad month.
Cost structure and unit economics at scale
AI features have a cost structure traditional software doesn’t — API costs scale with usage in a way that server costs for typical CRUD applications don’t always. Diligence will probe your unit economics: what does it cost you in API/inference spend per user or per transaction, how does that scale as you grow, and have you designed cost controls (model routing, caching, rate limiting) or are you facing a nasty cost curve as usage grows. A founder who can produce a clear cost-per-request number and a plan for how it improves with scale looks far more credible than one who hasn’t looked closely at the API bill yet.
Security, compliance, and data handling
For fintech and healthtech specifically, expect deep questions on how customer data flows through your AI pipeline: does it touch third-party APIs, under what data processing agreements, is there audit logging for AI-assisted decisions, and how do you handle the specific compliance requirements of your vertical (SOC 2, HIPAA-adjacent handling, relevant financial regulations). Gaps here aren’t always dealbreakers at seed/Series A, but a credible remediation plan is expected, and “we haven’t thought about it yet” is a worse answer than “here’s our plan and timeline.”
Team and execution risk
Finally, diligence assesses whether your team can actually execute the AI roadmap you’re pitching. If your AI engineering has been built by outside engineering partners rather than a large in-house team, that’s increasingly normal and not automatically a red flag in 2026 — plenty of strong technical due diligence processes now expect a lean core team plus scoped outside expertise rather than a bloated in-house AI department at seed/Series A stage. What matters is whether the system is well-documented, well-architected, and genuinely owned and operable by your core team going forward, not whether every line was written by a full-time employee.
How to prepare
Before diligence starts, get your story straight on architecture (what’s custom vs. vendor), data (what you have and how you got it), evaluation (how you know it works), cost (unit economics), and compliance (what’s handled, what’s planned). A founder who answers these crisply, with real numbers, moves through technical diligence dramatically faster than one improvising answers in the room.
CTA: If you want a pre-diligence technical review of your AI stack before you’re in front of investors, we can help you find the gaps first — nextpak.org, short call. NextPak has been building auditable, production-grade AI systems since 2020.