Why investors should care now
Enterprise buyers are tightening procurement. Regulators are codifying expectations. The companies that win are those that treat evidence as a first-class product output.
Two anchor points matter for VC diligence:
1) The EU AI Act hard-requires evidence for high-risk systems. It mandates technical documentation and logging capabilities for high-risk AI systems, and it includes meaningful penalties for non-compliance.
2) Banks already operate under model risk management expectations. In the United States, the Federal Reserve’s supervisory letter SR 11-7 sets out guidance on Model Risk Management, including concepts such as validation, ongoing monitoring, outcomes analysis, and governance.
If a startup is selling into regulated or risk-sensitive environments, “we will add governance later” is not a plan. It is a valuation haircut waiting to happen.
If evidence is manual, it will not exist when it matters.
Audit evidence by design, defined
Audit evidence by design means the product and its operating model continuously generate the artefacts that buyers, auditors, and risk teams ask for. The core idea is simple: do not make evidence a manual process.
Manual evidence fails in three predictable ways:
- It is created too late, usually in response to an incident or a sales blocker
- It is incomplete and cannot be reproduced
- It cannot survive a model update, a prompt change, or a workflow refactor
EvalOps makes evidence systematic.
EvalOps is a sales accelerant, not a compliance tax.
The EvalOps evidence stack (what “good” looks like)
Below is a practical way to think about EvalOps as an investor. You are not buying perfection, you are buying a system that can prove it is under control.
| EvalOps artefact | What it proves | What you should be shown in diligence |
|---|---|---|
| Evaluation suite | Performance is measured, not asserted | A versioned test set, pass/fail thresholds, and clear business KPIs |
| Release gates | Changes do not silently degrade outcomes | A documented promotion process (dev to staging to production) with regression checks |
| Traceable logs | Actions and decisions are reconstructable | Event logs that link inputs, model versions, retrieval sources, and outputs |
| Technical documentation | Risk teams can assess the system | A living document set that reflects the current system, not last quarter’s build |
| Monitoring and incident workflow | Failures are detected and managed | Alerts, runbooks, and examples of past incidents and remediation |
| Human oversight design | High-impact actions are controlled | Clear approval points and escalation rules aligned to business risk |
The point is not that every company must look like a bank on day one. The point is that the architecture must make this achievable without a rebuild.
The EU AI Act makes evidence a technical requirement
If a company’s roadmap includes regulated domains, diligence should assume the evidence burden will arrive sooner than planned.
In the EU AI Act, high-risk AI systems must be supported by technical documentation, and they must allow for automatic recording of events (logs) over the lifetime of the system. Separately, the Act sets administrative fines that can reach EUR 35,000,000 or 7% of total worldwide annual turnover for certain infringements.
The practical investor takeaway is direct: a startup that cannot produce buyer-ready documentation and logs is structurally mispriced for a regulated go-to-market.
SR 11-7 makes “we validated it” a question with teeth
If the buyer is a bank, “model risk management” is not a nice-to-have. It is a process expectation.
SR 11-7 is not a generic AI paper. It is a supervisory letter on guidance for Model Risk Management. That framing matters, because it shifts diligence from “does the model look accurate?” to “is there a governance and validation operating model that can be sustained?”.
For a VC, this changes the diligence posture:
- Validation is not a one-off exercise performed before launch
- Ongoing monitoring is not a dashboard, it is a decision process
- Governance is not a policy document, it is enforcement and accountability
How EvalOps becomes a product feature
The best companies package their evidence into a buyer-facing capability:
- A “trust centre” that exposes relevant controls and artefacts
- A one-click export of logs and documentation scoped to a deployment
- A model and prompt change history that maps to evaluation results
- Clear explanations of oversight points for high-impact actions
This does two things in enterprise sales:
- It reduces the time spent in security and risk review
- It increases buyer confidence without requiring bespoke assurances
In other words, EvalOps compresses sales cycles and reduces post-sale risk.
VC diligence checklist (use this as a filter)
Ask for evidence, not narratives:
- Show me the evaluation suite and the last three regression runs
- Show me how you promote a model or prompt change into production
- Show me how you reconstruct a single high-impact decision end-to-end
- Show me the logging schema and retention policy
- Show me the incident workflow, including who can halt the system
- Show me the current technical documentation, and when it was last updated
If the team cannot demonstrate these, the risk is not “they are early”. The risk is “they are building a system that will not survive scrutiny”.
Conclusion: evidence is the moat
Audit evidence by design is not compliance theatre. It is an engineering discipline that creates technical certainty for buyers and investors.
The thesis is simple: in the next cycle of AI investing, the winners will not be the teams with the most impressive demo. They will be the teams who can prove their systems are governed, monitored, and defensible as they scale.
Sources
- EU AI Act (official text, including logging requirements and penalties): https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689
- Federal Reserve SR 11-7 (Supervisory Letter on guidance on Model Risk Management): https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm
- NIST AI Risk Management Framework 1.0 (NIST AI 100-1): https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
- ISO/IEC 42001:2023 (AI management system standard overview): https://www.iso.org/standard/81230.html
