Technical Due Diligence for Non-Engineers: A Checklist That Surfaces Risk

Technical diligence feels intimidating when you are not an engineer. The risk is not that you will miss a clever bug. The risk is that you will miss the structural problems that show up later as churn, outages, security incidents, or inability to ship fast.

The goal of technical diligence is to answer a simple question: is the product built in a way that can scale to the next milestone without accumulating fatal debt?

This checklist focuses on questions you can ask and evidence you can request. It is designed for pre-seed through Series B.

What Good Technical Diligence Looks Like

• Clear system boundaries and ownership (who owns what).
• Evidence of disciplined shipping (how changes go to production).
• Basic security hygiene (who can access what, and how secrets are managed).
• Honest limits (where it breaks at 10x usage and what the plan is).

The Non-Engineer Checklist

1) Architecture and Scalability

• Walk me through the product as a diagram. What are the core components?
• What is the single biggest scaling bottleneck right now?
• What happens if usage grows 10x in 12 months?
• Which parts are third-party dependencies, and what happens if they fail?

2) Shipping and Quality

• How often do you ship to production?
• What is the release process? Who approves changes?
• Do you have automated tests? Which areas are covered and which are not?
• How do you handle incidents and postmortems?

3) Reliability and Observability

• Do you have monitoring dashboards? What metrics do you watch daily?
• How do you detect outages, performance regressions, and data issues?
• Do you have backups? When was the last restore test?
• What is the uptime expectation, and what is the actual track record?

4) Security Basics

• How do you manage secrets (API keys, database passwords, tokens)?
• Who has production access, and how is access reviewed?
• How is customer data encrypted in transit and at rest?
• What is your incident response plan? Who owns it?

5) Data and Privacy

• What data do you store, and for how long?
• Can customers export and delete their data?
• Where does training data come from (if AI is involved)?
• What is the policy for logging sensitive data?

6) Team and Execution Risk

• Who is the technical leader? What have they shipped before?
• What is the hiring plan for the next 2 engineers and why?
• What tech debt would you pay down if you had 2 extra weeks?
• What are you not proud of in the codebase?

Evidence You Can Ask For

You do not need to read code to ask for concrete artifacts:

• A high-level architecture diagram (even if it is a whiteboard screenshot).
• A recent incident postmortem (or a writeup of a major bug and the fix).
• A list of top dependencies and what would break if they changed pricing or terms.
• A short demo of monitoring dashboards (errors, latency, uptime).

AI-Specific Notes

If the company is AI-heavy, layer in model and data diligence. This guide pairs well with our AI/ML diligence framework:

AI/ML Startup Diligence for Non-Technical VCs: A Practical Framework

How To Avoid False Confidence

Technical jargon can create a false sense of safety. Two habits help:

• Ask for examples. If they say they have monitoring, ask to see the dashboard.
• Ask for tradeoffs. If everything sounds perfect, it usually means the risks are not being named.