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.
Four shapes. Most engagements start with the first.
01
AI opportunity audit
Two 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, plus a list of where it does not.
02
Build the first integration
One 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.
03
Agents and internal workflows
Agents 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.
04
Make your product legible to AI
Structured 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.
Marketing compliance for finance and iGaming. Ingest the policy documents, extract enforceable rules from them, then review copy, images, video and audio against those rules, with every verdict linked back to the clause it came from, with a full audit trail.
An agentic AI nutritionist paired with a 24/7 wearable, and the company I co-founded. Log a meal by photo or voice, and get advice grounded in live biometrics plus everything the model remembers about you, all end-to-end encrypted. Alpha since August 2026.
AI agents running customer operations, deployed in six African markets. My share is the backend and the integration layer they act through: WhatsApp, Shopify, HubSpot, Zendesk, and the payment rails those markets actually run on.
One inbox for WhatsApp, Instagram, Facebook, email and website chat, with AI that replies, qualifies leads and routes conversations, wired into order systems, stock and CRM so it acts rather than only answers.
Nepal's home-services marketplace with 50+ services across Kathmandu, Lalitpur, Bhaktapur and Pokhara. Customer app, provider app and the booking platform behind them.
Wedding-day scheduling for the people running the day: build the running order, lock what cannot move, then share it as a link, text, image or PDF. I built the scheduling and sharing side over nearly two years, 40 merged pull requests and 124 commits.
Connected fitness: a Pilates reformer paired with a mobile app for instructor-led classes, with offline playback for a studio with bad wifi. Hardware, firmware boundary and app.
