UX and Product Design
Designed for trust,not just speed.
Qlarc is a B2B SaaS platform that turns the AI governance a vendor already has into regulation-mapped evidence a financial-services buyer is legally required to evaluate.
- Readiness as one number the buyer trusts
Qlarc
Most deals don’t die with a no. They die with silence: a buyer’s governance team goes quiet and a $500K–$2M deal freezes with no reason given. It was never a documentation problem, it was a revenue problem.
Lead UX / Product Designer, owning the intake, evidence-mapping, and export flows.
Design every screen for the compliance lead who owns the output: group evidence by strength, let honest partial packs through with gaps flagged, and block any export until a named human signs off.
A response flow a reviewer can approve without a follow-up call. Targets are pre-pilot, not yet measured in production.
Designed for trust, not just speed.
The design thesis
Evidence items mapped across 10 procurement questions in the prototype
Shipped in prototypeTarget time from workflow start to full evidence pack export
Design targetTarget source attribution rate. Every claim linked to API or document evidence
Design targetAutomated send paths. Every export requires named human sign-off before it leaves
Shipped in prototypeQlarc is a vendor-side AI governance procurement platform for mid-sized AI companies selling into regulated financial services. Buyers operating under SR 11-7, the EU AI Act and ECOA require structured, regulation-mapped evidence before they approve any AI system. Vendors have the governance. The buyer cannot verify it. Qlarc is the infrastructure that closes that gap.
A GRC or compliance lead. Not an engineer.
A compliance professional under deadline pressure, assembling evidence across systems they do not control. Every decision was made for this person: guided workflows over complex forms, evidence first, non-technical by default, and vendor control before anything leaves the platform.
A revenue problem in a documentation costume.
Most deals don’t die with a no.
They die with silence.
The buyer’s governance team goes quiet, the purchase order freezes, and a deal disappears. It was never a documentation problem. It was a revenue problem, which set one tiebreaker for every decision: protect the deal, not the feeling of completeness.
Deal value frozen at a single governance review
To assemble one evidence pack by hand, each deal
Reasons the buyer gives before walking away
Discovery ran on interviews with compliance, GRC and procurement leads, alongside validation sessions with three domain experts in model risk and AI governance. The same three patterns surfaced in every conversation.
Reviewers do not negotiate missing evidence. They stop the purchase order, and the vendor rarely learns why.
A partial submission signals that a vendor doesn’t understand its own governance. That reads worse than asking for more time.
Visibility, documentation and translation into the buyer’s language all have to be solved in one motion.
the thing that stuck with me wasn’t the rejections. it was the silence. no feedback, no second chance, the deal just stops moving and nobody says why. and if the vendor never hears the reason, they can’t fix it → they lose the next one the same way. kept coming back to this. the problem isn’t really the missing docs, it’s the silence around them.
How might we move the judgment earlier?
How might we let a vendor see what a buyer will reject, before the buyer ever does?
Resolves in Decision 01Readiness before documentation- Q3Escalation path and human overrideMissing
Why it failsBuyer requires a named escalation owner with a documented hand-off. None on file.
- Q7AI output monitoringMissing
Why it failsNo monitoring cadence or alerting evidence provided.
Connect → Upload → Gap-Fill → Review & Export.
A vendor with critical gaps is routed to a Governance Readiness Report before pack generation begins. Below, the full system, screen by screen.
Two doors, one gate, one pipeline.
Every vendor enters through one of two doors, new or returning. Only the first-time path creates a fresh assessment, and it runs through the readiness gate before anything else. The architecture makes that gate impossible to skip, so every evidence pipeline starts from a qualified baseline.
View the full system architecture diagram FIG 2.0 · expand ⌄
The three decisions that earned the system its trust.
Each one carries the rejected approach next to what shipped, and what I tried first.
Readiness before documentation
Route the vendor to the truth before they can submit a half-built pack.
Qualify the vendor before any pack is generated.
Evidence workflow, or a Governance Readiness Report.
A partial pack becomes a compliance record of unpreparedness.
The gate adds a step before vendors see the product. That step is the product.
The gate first required all three readiness checks to pass, a strict yes/yes/yes. Research showed mid-size vendors don't always have all three in place, so it turned away too many who were otherwise close. I relaxed it to a single qualifying yes, which still routes the unready toward the Readiness Report without shutting capable vendors out entirely.
i was so sure about this one. three checks, all green, or you're not ready. felt like integrity at the time. then a vendor said it flat out in an interview: "nobody has all of this on a good day." sat with that for about a week before i dropped the gate to a single yes. felt like losing when i did it. it wasn't. i'd built a wall where i needed a door.
Source-tagged evidence mapping
Put the proof right next to the claim, at the level where the reviewer is actually reading.
API-verified · doc-extracted · vendor-provided, on every row.
Article-level, drawn from a verified library. Never LLM-generated.
Name, date and document, all verifiable at the point of review.
Reviewers couldn't tell which specific claim was weak within a section.
Every mapped item traces to a specific named source, verifiable at the point of review.
I started with a section-level confidence band. But the reviewer could not tell which exact claim was not strong enough, or where each piece of evidence came from. So I moved the strength and the source onto each individual claim.
The vendor sees exactly what a buyer reviewer will see before any export begins.
SR 11-7 requires independent verifiability, not attestation. A reviewer must check each claim at the point of reading, not by navigating to an appendix. The LLM is used for extraction only; every mapped item traces to a specific source, not generated text. Per-claim tagging makes the screen denser than a polished summary would. That density is what makes it verifiable.
Human review and accountability
No automated send path. Named approval, per section, is the gate.
Named sign-off required on every section before export.
Name, timestamp and version, cryptographically hashed.
An unaccountable AI output, the exact thing the buyer is screening for.
Named approval slows the export down. That slowdown is the accountability the regulation asks for.
A single confirmation modal at the end, one approval checkbox before export. Validators flagged that a single sign-off for a multi-section legal document doesn't meet SR 11-7's attestation standard. Named per-section approval replaced it.
The Procurement
Response Pack.
Regulation-mapped, source-attributed, and built to mirror the buyer's questionnaire format exactly. It ships with a One-Page AI Governance Summary too, for the reviewer who only has thirty seconds to spare.
A pack the buyer can trust, and the vendor can stand behind.
My whole argument is that friction builds trust. I still believe it. But I added three checkpoints to a product whose users are already exhausted and behind. And I designed every one of them for the reviewer at the end.
The person I’m least sure I served is the tired one at the start. The GRC lead opening Qlarc at 6pm, already two weeks late. If I had another month I wouldn’t add a feature. I’d sit behind five of those leads while they hit the gate cold, and count how many quietly close the tab. That count is the thing I’d want to know before I trusted any of this.
if i could rerun one study: 5 vendors, no warmup, just drop them at the gate cold. count how many quit before they ever see the payoff.
kind of scared of that number tbh. which is probably why it’s the one to chase.