Most AI projects die in the gap nobody demos.
The demo works. Then real customers arrive — and nobody planned for what it costs to run, what happens when it's wrong, or who fixes it at 2 AM. We build that half.
Six things that decide whether a prototype survives real customers. On most projects, four of them have no owner.
Funded companies and established businesses.
Your AI initiative works in a notebook and stalls before it reaches a customer. You have real data, real constraints, and a team that doesn't need to be taught what good looks like — it needs people who have shipped this before. We work with clients across the US, EU, and India.
Three ways to work
with us. No menu.
Know in two weeks whether this is worth building.
One to two weeks, paid, fixed fee, 50% upfront. We go deep on your data, your constraints, and your users, then hand you the architecture and the build plan — with cost and latency budgets attached. The fee is credited against implementation if we go on to build it.
Start with a Sprint →We build the version customers actually touch.
Embedded with your team, from architecture to deployed: LLM applications, RAG over messy real data, agents with typed tools, voice, on-device inference. Every one of them ships with evals that catch a wrong answer before a customer does, and a named owner for each way it can fail.
Senior judgment without a full-time hire.
Ongoing technical leadership for teams navigating AI adoption: architecture review, build-vs-buy, hiring the right engineers, and saying no to the wrong roadmap. You keep ownership of the decisions; we make sure they are the informed ones.
Four steps. Each one
ends in something you keep.
Conversation
A 30-minute call and a short written scope. We say plainly whether this is a fit and what it would cost.
Find out what it takes
Paid, fixed fee, 50% upfront. Your data, constraints, and users, examined properly.
Build
Embedded with your team. Weekly deployable increments, evals in CI from day one.
Handover or run
Your engineers take it, or we stay on retainer. Either way, it is documented and monitored.
Small and senior
by design.
No layers, no bench, no juniors learning on your budget. The person who designs your system is the person who writes it. Founder-led delivery: 17 years of shipping, 5 companies, one of them taken through Series A as CTO.
An agent prototype that impressed the board and failed with customers.
A sales-research agent worked in demos and broke on real accounts: 40-second responses, no evals, silent tool failures, unbounded token spend.
Two-week Sprint, then seven weeks embedded: decomposed the agent into typed tools, added a 300-case eval harness in CI, cached retrieval, set a per-request cost ceiling, put failures on a dashboard their support team reads.
Shipped to all customers in 9 weeks. Their own engineers now own it.
RAG over fifteen years of inconsistent contracts and scans. Layout-aware extraction, citation-backed answers, and a review queue for low-confidence output — because wrong answers cost more than slow ones.
Voice control shipped to real users on their own machines. Quantized models, a warm audio pipeline, and no network dependency — the same work that produced Neo.
Entity resolution and matching across tender data from 90+ markets, serving a live product. Batch to streaming, with quality gates that stopped bad data reaching customers.
Tell us what
you're building.
A paragraph is enough. Vishesh reads every one and replies himself, usually within two business days.
Occasional notes on what we're building and what broke. No cadence, no funnel.