Your AI demo impressed the board.
Production is where it’s dying.
I take one stuck or flaky AI initiative from demo to dependable production in 90 days — shipped, instrumented, and cost-controlled. I’m Mayur Sethi: an operator who builds and runs production AI systems with my own P&L on the line. You get working Human+AI systems. Not a deck.
95% of AI initiatives never make it past pilot. The reasons are boringly consistent.
MIT found only ~5% of enterprise AI pilots produce real financial gains. Gartner expects 40%+ of agentic AI projects to be cancelled by 2027 — on cost and reliability, not ambition. The gap between an impressive demo and a dependable product is an engineering and operating discipline. Most teams have neither the time nor the scar tissue to build it.
Reliability nobody owns
The demo works in the meeting and fails on real load. Edge cases, hallucinations, and silent regressions land on whoever shouted last.
Costs rising without a ceiling
Inference spend creeps toward 20%+ of revenue for AI-first products. No per-call cost visibility, no budget owner, no kill switch.
No evals, no quality bar
“It seems better” is not a release criterion. Without an eval harness and human-in-the-loop review, every change is a gamble.
Leadership flying blind
Is this a 3-week fix or a 3-quarter rebuild? When nobody can answer, the initiative stalls — and the team quietly loses the room.
Two steps. Both fixed fee. Both ship working software.
No six-month discovery. No strategy deck you could have written yourselves. The audit de-risks the build; the build ships the outcome.
AI Production Readiness Audit
A full map of one stuck AI feature — where it breaks, what it costs per call, where it won’t scale. Not a paper exercise: I ship a real fix inside the audit.
- — Failure-path map of one AI feature, end to end
- — One shipped win: the worst failure path made dependable, inside 3 weeks
- — A plain-language definition of “production-ready” for your feature
- — Eval + quality bar your team keeps forever
- — Prioritized 90-day build roadmap: reliability, cost, scale, data plumbing
Fee fully credited toward the Launchpad. And if it’s truly a 3-quarter rebuild, you’ll know in week one — and you can stop there.
90-Day AI Launchpad
The audit’s roadmap, executed. I start on day 1 with access, context, and the failure map already in hand — no warm-up, no rediscovery.
- — Production-hardened AI feature, live with real users
- — Eval harness + human-in-the-loop review loop, wired into releases
- — Per-call cost instrumentation with budget ceilings and alerts
- — Observability: you see what the AI did and why, every time
- — Handover: your team runs it without me
Day-by-day journey below ↓
Most clients don’t want the discipline to leave when I do. Many keep me on as their Fractional Head of AI — ongoing ownership of evals, cost control, and build/buy/kill decisions across their AI surface. That’s a conversation we have during the work, once you’ve seen how I operate — not a pricing page.
I do my best work for a narrow kind of company.
A strong fit if you are…
- — A funded or profitable B2B company — typically $10M+ ARR or Series B and beyond
- — Already shipped (or attempted) AI features — and now stuck on reliability, cost, or scale
- — Knowledge-work heavy: B2B SaaS, agencies, services firms with real workflows to automate
- — Ready to give an operator real access — code, data, and an hour a week of decision-maker time
Not a fit if you need…
- — A dev shop to build your first product from scratch — I harden and ship what exists
- — A strategy deck for the board — I ship systems; the deck writes itself afterward
- — Help before you have revenue or users — pre-revenue teams have cheaper options
- — Someone to own AI forever without your team learning it — I hand over, always
Day 1 starts where the audit ended. Running.
By the time the Launchpad begins, the failure map is drawn, access is live, the first fix has already shipped, and the roadmap is prioritized. There is no discovery phase — there is only the build. Your team sees progress weekly, not at the end.
First sprint scoped to the highest-ROI failure path. No discovery phase.
Measured against the eval bar set in the audit.
Behind a flag. Real traffic, controlled blast radius.
Quality scores decide what ships — not vibes.
Per-call economics visible. Load proven, observability live.
Instrumented, cost-controlled, yours.
Agencies bill hours. Consultants write decks. Operators are accountable for what runs in production.
I’m not a dev shop with an AI page, and I’m not a strategy firm that hands you a roadmap and leaves before the hard part. I run AI-first businesses with my own P&L — the same systems, evals, and cost discipline I sell are the ones keeping my companies alive.
As CRO of a healthcare services company, I rebuilt operations AI-first — 8+ FTE functions now run by 2 people + 12 AI agents. Opex down 40%.
EcomDataIQ, my own AI analytics platform: live on GCP with paying customers, $10M+ of client revenue under management.
At DataStax I built the IBM partnership from zero to ~30% of ARR — the strategic thesis behind the ~$1.6B IBM acquisition.
I co-founded Life Sutra: multi-million revenue in two years, selling through Amazon, Walmart, and Macy's.
How I work — the operating principles
- Evals before features. If you can't measure quality, you can't ship changes. The harness comes first.
- Cost is a product requirement. Every AI call has a price; every feature gets a ceiling and an owner.
- Humans in the loop, by design. The goal is dependable Human+AI systems — not unsupervised magic.
- Narrow first, then wide. One initiative, shipped and measured, beats five pilots in flight.
- Your team owns everything. Code, evals, dashboards, decisions. I make myself unnecessary.
Agency vs. consultancy vs. GoldenArc
| Dev agency | Strategy firm | GoldenArc | |
|---|---|---|---|
| Deliverable | Billable hours | A deck | Working production systems |
| Incentive | More hours | More scope | Fixed fee, shipped outcome |
| First proof | Sprint 6+ | Never runs | A real fix in week 3 |
| Runs AI in production with own P&L | Rarely | No | Yes — currently, daily |
Stanford Executive AI Product Management
Google Generative AI Leader
Mayur Sethi
Twenty years across enterprise tech — and one consistent pattern: it ships. I built 0→1 products at HP and HCL (the SAP-as-a-Service blueprints I co-designed now generate $1B+/year for HCL). I delivered at enterprise scale at Cognizant, growing a single engagement from $90M to $280M ARR for clients like JP Morgan and BlackRock. I sold to the Fortune 500 at Hitachi and Veritas — ~$60M from 17 new accounts, 200% YoY partner growth. And I positioned DataStax for its ~$1.6B IBM acquisition by building the IBM alliance from zero to ~30% of ARR.
Then I came back to building. Today I run production AI with my own money on the line: a live SaaS platform on GCP with paying customers, multi-agent systems, custom models, and human-in-the-loop evaluation frameworks. I close the gap most AI teams can’t — taking AI from impressive demo to dependable, cost-controlled production.
The questions CTOs actually ask
Who actually does the work?
Me — hands-on, in your codebase, alongside your team. No bait-and-switch to a junior bench. Where specialist depth helps, a small vetted network plugs in under my direction.
What do you need from us?
Repo and infra access, a data sample, and one hour a week with a decision-maker. If you can't give an operator access, you're not ready for an operator.
What if it really is a 3-quarter rebuild?
Then you'll hear that in week one, in plain language, with the evidence — and you can stop at the audit. You keep the map, the eval bar, and the shipped fix either way.
Which stacks and models do you work with?
Model-agnostic by principle: OpenAI, Anthropic, Gemini, open-weights — whatever the evals and unit economics justify. Deep hands-on with GCP (Cloud Run, BigQuery), comfortable across AWS and Azure.
You'll leave the first call knowing whether this is a 3-week fix or a 3-quarter rebuild.
Thirty minutes, no pitch theater. Bring your stuck initiative; leave with an honest read on what it takes. The audit is a fixed fee scoped on that call — a fraction of one senior AI hire, with the first fix shipped in three weeks.
Every note is read by me, not a funnel. — Mayur Sethi