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Sailesh Dahal

I'm Sailesh.I help teams put AIinto production.

Most AI projects die between the demo and the deploy. I work on the part after the demo: the retrieval that has to be right, the evals that gate the release, the guardrails and the audit trail, the cost per request. I've shipped it into regulated compliance review, customer operations on WhatsApp, and a wearable-backed health assistant.

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How I work with teams

Four shapes. Most engagements start with the first.

  1. 01AI opportunity auditTwo weeks inside your product and your team’s workflows. You get a ranked list of where AI actually pays — with the cost, the failure modes and the eval you would need for each — and an honest list of where it does not.
  2. 02Build the first integrationOne workflow, taken all the way to production: retrieval, prompts, tool calls, an eval suite that gates deploys, cost and latency budgets, and a human-in-the-loop path for the cases the model should not decide alone.
  3. 03Agents and internal workflowsAgents that touch your real systems through typed tools and MCP servers, with permissions, audit trails and a kill switch. Built so the interesting part is your data, not the plumbing.
  4. 04Make your product legible to AIStructured data, markdown twins, llms.txt and in-page agent tools, so assistants describe your product correctly instead of guessing. This site is the reference implementation.

Want the concrete version — RAG, agents, evals, self-hosted inference? Example briefs, cheapest to deepest →

Example implementations

Open any of these and check it — nothing here is a screenshot of a prototype.

Products I've built

Shipped, live and open to inspection. Cards marked LaunchBox were built at the studio I co-founded; the rest I built outside it.

Recent writing

All 10 posts →

Series

Open source

Repositories I wrote.

Merged into other people's projects