QLARC: Closing the AI procurement evidence gap for enterprise vendors
A vendor-side AI governance tool that helps mid-sized companies win regulated enterprise deals — by translating internal AI practices into buyer-ready evidence packs.
Three gaps. One blocked deal.
In financial services, AI governance reviews aren't a best practice, they're a legal gate. SR 11-7 requires model risk documentation. ECOA and FCRA mandate bias testing evidence. A vendor who can't produce this on demand doesn't get rejected, they never hear back.
of vendors can't see their own AI across teams and third-party services.
to produce governance docs that live in engineering, not in auditable formats.
lost per deal when good governance can't be translated into what buyers need.
How might we help vendors prove the governance they already have, in a format buyers can actually evaluate?
Every existing tool was built for a different problem.
No existing tool helps a mid-sized AI vendor when an enterprise buyer asks for governance evidence. Qlarc sits alone in that corner.
Built for SOC 2 — not AI governance evidence
Built for GDPR/CCPA — can't map to SR 11-7
Internal ops only — not buyer-readable evidence
No regulatory traceability — treats AI like any RFP section
Vision
Close the accountability gap in AI procurement.
Mission
Mid-sized vendors win regulated enterprise deals on the strength of their AI, not the size of their legal team.
Values
Evidence over assumption. Human in the loop, always. Precision over speed.
Each step resolves one gap.
The Qlarc workflow maps directly to the three gaps that block deals — Visibility, Documentation, and Evidence Translation — plus a named human approval gate.
Connect
Qlarc reads what AI is actually running — model, version, region — without touching customer data.
The result: a trusted, buyer-ready Evidence Pack — built in hours, not weeks.
Priced against the cost of the problem it solves.
Annual SaaS, three tiers from $5K to $75K, priced around procurement frequency. Incorporated as a Public Benefit Corporation — we sell governance accountability, so we're structurally accountable too.
The questions we started with. The answers we built.
The case study opened with three unresolved risks every enterprise buyer asks. Each one shaped a specific product decision.
Who owns the training data? Where is it stored?
Are AI decisions discriminating against protected groups?
When AI fails, who is responsible?
Signals are read, never retained — the vendor's data never becomes a model input.
The API signal and the document have to agree before a claim ships.
The sign-off is named and timestamped — accountability is structural, not stated.