Building the quality operating system behind a Fortune 500 GenAI platform — the evaluation frameworks, reviewer organization, and governance layer that turned probabilistic output into an enterprise-grade product.
Puntt AI is an enterprise GenAI platform that compresses marketing and packaging asset approvals from weeks to hours for Fortune 500 consumer brands. The product's promise is speed — but its customers are brand and compliance teams whose entire job is catching what's wrong. Every AI output that reaches them carries the platform's credibility with it.
That's the tension every production GenAI program lives inside: probabilistic systems selling deterministic promises. The model doesn't have to be perfect. The program does.
When I took ownership of the quality and operations layer in December 2024, studio accuracy stood at 92.7%. For a consumer app, that's respectable. For Fortune 500 brand-compliance workflows, it means roughly one in every fourteen outputs carries an error a client might see — and enterprise trust doesn't survive that arithmetic for long.
The deeper issue wasn't the number. It was that quality was being earned review by review — dependent on individual diligence rather than a system. Nothing systematically caught model drift before clients did. Reviewer standards lived in people's heads. There was no structured way to turn what reviewers were seeing into what engineering should build next.
Accuracy you can't explain is accuracy you can't repeat. The mandate was to make quality an operating property, not a heroic one.
Multi-tier SOPs, LLM output evaluation rubrics, and prompt governance standards — so "good" was defined on paper, not negotiated per ticket. Every production workflow got an explicit quality bar with evaluation evidence behind it.
A six-person, globally distributed reviewer team producing 900+ tickets a month at 98–100% accuracy — held there by data-labeling quality governance, rater calibration rubrics, inter-rater reliability (IRR) frameworks, and structured annotation calibration loops. The design goal: no per-review supervision, because the calibration system does that job.
Weekly model drift detection, performance review cadences, and recalibration protocols — surfacing output-quality degradation inside the week it appears, not in a client escalation a month later.
A cross-account AI failure taxonomy and structured triage log turning scattered reviewer observations into systematic root-cause analysis — and into precise, actionable engineering handoffs. Reviewer signal became a product input, not an operational afterthought.
OKR cycles, North Star metric tracking, studio health dashboards, and go/no-go quality gates on production readiness — translating model performance into executive-grade ROI narratives, and surfacing risk signals before they touched client delivery.
The model didn't change on the day accuracy crossed 99%. The system around it did.
92.7% only became actionable when it was framed as defect escapes a client would see — a number an executive sponsor could care about. Quality targets without business framing don't get resourced.
IRR and rater calibration feel like bureaucracy until you realize every downstream metric — accuracy, drift, evaluation — is only as trustworthy as the humans defining "correct."
Every point of accuracy gained came after deployment — from monitoring, recalibration, and iteration cadence. Programs that treat launch as the finish line hand their quality curve to entropy.
Client names withheld for confidentiality. Metrics are from internal program tracking at Puntt AI, December 2024 onward. Happy to walk through the detail in conversation.
I'd like to hear what you're building, what's in the way, and where I can help.