How to Evaluate Your ML Model Before Series B Due Diligence

Series B due diligence has an AI problem. Investors have learned from the first wave of AI startups that a high accuracy number on a company-provided test set tells them almost nothing about real-world model performance. Technical due diligence on AI companies now routinely includes questions that most founding teams are not prepared for.

What Investors Ask About Your AI Model

Based on the due diligence questions we have helped Series A and B companies prepare for, the standard questions are:

Performance validation:

• What is your model’s performance on a holdout test set not used during training?
• How does your model perform on the specific customer use cases that drive your revenue?
• What is the performance baseline you are comparing against?

Bias and fairness:

• Has your model been evaluated for demographic bias? Which protected characteristics were tested?
• If your model makes decisions that affect individuals, what is your bias monitoring methodology?
• Has anyone outside your team validated the bias evaluation?

Robustness and reliability:

• How does the model perform on out-of-distribution inputs?
• What happens at edge cases? What is the failure mode when the model is wrong?
• How does model performance degrade as production data drifts from training data?

MLOps and monitoring:

• How do you detect when model performance has degraded in production?
• How quickly can you retrain and redeploy when drift is detected?
• What is your rollback procedure if a new model version underperforms?

Why Most Series A Companies Fail This Scrutiny

The typical Series A AI company has:

• An internal test set created by the same team that built the model (subject to selection bias)
• A single accuracy metric reported at the aggregate level (no subgroup analysis)
• No formal bias evaluation
• Monitoring that tracks infrastructure health but not model quality
• No documented rollback procedure that has been tested

None of this is negligence - it’s the natural state of a company moving fast to build a product. But it creates a due diligence vulnerability.

How to Prepare

Six months before your Series B process:

1. Commission an independent model validation. Get an external evaluation of your model performance on a test set you didn’t construct. The word “independent” matters - investors discount self-reported benchmarks.

2. Run a bias audit. Even if your product doesn’t involve protected characteristics directly, understanding your model’s performance across demographic subgroups is now a standard due diligence question.

3. Document your monitoring. Can you show, concretely, how you would detect a model degradation in production and how you would respond?

4. Establish a performance baseline. What were you replacing? How much better is your AI than the previous approach?

The deliverable investors want to see:

A model validation report from an independent third party - covering methodology, test set composition, evaluation metrics across subgroups, bias findings, and a performance comparison to baseline.

Book a free scope call to prepare your model validation documentation before your fundraising process starts.