Getting an AI system from a promising demo into production, where it touches revenue and real customers and can no longer fail quietly, is a different discipline. Jay Sharma has practiced that discipline twice: for 300 million people at Indeed, and alone, as a founder with paying customers. Depilot makes it available to companies that are done experimenting.
Somewhere in your company is a pilot that impressed everyone in March and hasn't shipped since. It performs beautifully in a sandbox. No customer has ever touched it. Nobody will kill it, because it works, more or less. Nobody will ship it, because no one can say what happens when it is wrong at volume.
The pattern behind MIT's number is remarkably consistent, and it has almost nothing to do with which model anyone chose. Four habits, over and over:
The assistant that can't act. It chats. It cannot read the CRM, write to the ticketing system, or touch the order flow. Impressive in a meeting; useless on a Tuesday.
Launches on faith. No evaluation harness, no defined bar a change must clear, no way to know whether last month's update made things better or quietly worse.
One bot for everything. The general-purpose company assistant is almost always the wrong shape. What survives contact with production: narrow agents, specific workflows, a human somewhere in the loop.
Ownership by committee. AI lives with an innovation team, three vendors, and a steering group. Pilots multiply. Nothing ships.
What breaks the stall is not another pilot. It is judgment: someone who has shipped this before, looking at what you have and putting one of three words on it, in writing.
Wrong abstraction, no data path, no owner. Ending them early is the cheapest decision in all of AI. We put it in writing and say it to your face, in week two rather than quarter three.
The common verdict. A stalled pilot usually sits three or four specific blockers from shipping: evaluation, integration, governance. The report names yours and puts them in order.
Occasionally the foundations are sound and the hesitation is instinct rather than evidence. Then the job is speed: launch gates, guardrails, a kill switch, so the system stays trustworthy at ten times the traffic.
Six questions from the first ten minutes of a real audit. Answer honestly; nobody is watching, and the verdict updates as you go.
Is any AI system you've built handling real work in production today — not a sandbox, not a beta?
Can your AI read from and write to your systems of record (CRM, ERP, ticketing) — or does it only chat?
Could anyone on your team prove, with data, whether your AI got better or worse last month?
Is there a defined bar an AI change must clear before it ships — and a kill switch if it misbehaves after?
Does a single senior executive own AI outcomes — not a committee, not "innovation"?
If your AI vendor tripled prices tomorrow, could you swap models without rebuilding?
Awaiting answers…
Six questions, no email required. The stamp appears when you finish.
Fixed scope and fixed fee, agreed before work starts. Every engagement is delivered by the person whose name is on the door.
Two to three weeks inside your pilots: architecture, data paths, evaluation coverage. Each pilot receives a written verdict with the reasoning shown, plus a readiness scorecard and a 90-day sequence for the survivors.
The flagship. An evaluation harness, defined launch gates, behavioral guardrails, and a kill switch, installed and running, with your team trained to operate them. Modeled on the machinery built at Indeed to decide what ships to 300 million people.
For product companies putting agents into the product itself. Agent design, human-in-the-loop mechanics, build-or-buy decisions, and an architecture record your engineers can execute without us in the room.
A senior operator inside your leadership team two or three days a week: roadmap, hiring bar, org design, architecture calls, board reporting. Two seats exist. The six-month minimum is deliberate; nothing real happens faster.
AI oversight for boards, and per-deal diligence for investors who need to know whether a target's AI claims survive a look at the actual architecture. They often don't, and it is better to learn that before wiring the money.
Twenty-five years, condensed to what shipped. Context on any of these, gladly, on a call.
people served by the AI matching and launch-governance systems built at Indeed. The governance machinery still decides what ships there.
reduction in bad matches after LLM + RLHF matching went live. Recommendation conversion rose by half.
in annual revenue carried through a hard regulatory deadline at Amazon Canada, on a catalog-wide ML compliance program.
agentic AI shipped to production. Once with a 300-person org behind it, once entirely alone. Both have paying customers.
Software engineer to engineering manager. Nine years building systems where being wrong costs money.
Took Proofhub, a SaaS product, from nothing to $2M in annual revenue. Product, engineering, and sales in one seat.
Fraud detection at PayPal's transaction volume. Tripled engagement on Bing's knowledge graph. Ran payments for one of Asia's largest travel marketplaces: 90+ local payment methods, cross-border rails in five countries.
Founded the technology org and grew it past a hundred people. ML compliance across millions of listings. Launched BNPL at marketplace scale.
LLM + RLHF matching for 300 million people, and the governance system that still gates every AI release. Also switched off a channel earning $24M a year because it was quietly damaging the marketplace. The replacement performed better on every measure that mattered.
A live agentic fintech product with paying customers, designed, written, and operated end to end with AI agents, by one person. Depilot is the practice built from what both scales taught.
Bring the stuck pilot or the roadmap. You'll get a plain reading of where you stand and whether an audit is worth your money. Some callers need one. Plenty don't, and we say so.
Two to three weeks of evidence: architecture, data paths, evaluation coverage, governance. Every pilot leaves stamped, with reasoning you could hand to your board unedited.
Harness, gates, guardrails, kill switch, running in your stack and operated by your team. Where it fits, a fractional seat follows, until you no longer need one.
You work with Jay. There is no bench and no handoff after the sale. This limits how many clients Depilot can take, which is the point.
Engagements end with something running. A harness, a gate, a system your team operates. Documents describe the work; they aren't the work.
Prices are fixed and quoted up front. Nobody here bills you for thinking slowly.
Bad news arrives early. If the right answer is to kill the project, you hear it in the first two weeks, while it's still cheap.
The practice runs on AI agents. Research, drafting, analysis, with the judgment kept human. Ask to see how it works; it doubles as a demo.
Jay Sharma wrote his first production code at Bloomberg in 2000 and spent the next twenty-five years as the person difficult systems got handed to. He founded Amazon Canada's technology organization, grew it past a hundred people, and carried five billion dollars of revenue through a regulatory deadline that had no extension in it.
At Indeed he ran search and recommendations for 300 million people. His teams shipped the LLM matching that cut bad matches by ninety percent, and built the evaluation and launch-governance machinery that still decides what ships there. He also switched off a channel earning twenty-four million dollars a year because it was quietly damaging the marketplace. The replacement made better matches and more money. That decision is this firm in miniature.
In 2025 he left to test a private conviction: that one operator with disciplined AI agents could build what used to take a team. One More Million, a live fintech product with paying customers, is the result. Depilot is the practice built on what both scales taught him.
"Most AI advice fails in the gap between a demo that impresses and a system you'd bet revenue on. I've crossed that gap twice. The crossing is the practice."
Thirty minutes with someone who has shipped what you're attempting. You'll leave knowing why it stalled and what shipping would take. That holds whether or not we ever send you an invoice.