SaaS diligence asks "is the company solvent and the product usable." AI diligence asks "can this vendor defend the decisions their model makes on your behalf." Most teams ask only the first.
An AI vendor is a delegated decision-maker. The diligence has to cover not just the company and the UI, but the model's behavior, the audit trail, the data handling, and the exit. Skip those and you're underwriting risk you can't see.
The Questions That Actually Separate Vendors
Term-sheet diligence applied to AI: the questions that expose the gap between a vendor who ships responsibly and one who ships and hopes.
- What does the audit trail look like? Can you export it?
- Where does your data go, who trains on it, and what's the retention?
- What's the model lineage — can they explain a decision six months later?
- What happens to your data and workflows if you leave?
The Conduct Test
A vendor's conduct shows in what they'll put in writing. "We don't train on your data" should be a contract clause, not a sales call promise. "You can export every decision" should be a feature, not a roadmap item. If it's not in the contract or the product, it doesn't exist.
The Three Failures Diligence Should Catch
One: the vendor trains on your data by default. Two: there's no audit trail you own — when a regulator asks, you'll have nothing. Three: no exit path — your workflows and data are hostage to the renewal.
How FACTA Thinks About It
FACTA's enterprise work is built on outcomes and ownership. The same diligence a buyer should run on an AI vendor, we run on ourselves: the workflow, the audit trail, the ownership handoff, and the exit. If a vendor can't answer those, the pricing doesn't matter.
Conclusion
AI vendor diligence is a delegated-decision audit. Ask the questions that expose whether they can defend what they ship — before you sign, not after the first incident.
About FACTA
FACTA helps startups and growth-stage teams turn AI into production systems that keep running — not demos that impress once.
We design the architecture around the parts that actually break under real usage: tooling you own, credentials you control, failover, cost controls, observability. The boring infrastructure that keeps a system alive after launch.
Led by Matías Baglieri and Carolina Fogliato, we focus on one thing:
AI leadership that builds. Not just advises.
Send us a vendor you're evaluating.
We'll give you the diligence questions that actually separate a defensible AI vendor from a demo. Related: AI governance for startups.
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