Method
Five phases. Each one has deliverables you keep and an exit criterion you can check. Measurable value, or we tell you early.
We sit with the people who run the operation and map the process end to end: where work queues up, what it costs, who touches it. We are looking for the constraint and the money attached to it. If there are three candidate problems, we rank them by expected return, not by how interesting the technology is.
Before promising results, we test whether your data can carry the model. We profile coverage, quality, history depth and leakage risk on real extracts, not on a slide about your data lake. This is the phase where weak projects should die, and where we make sure they do.
We build a working pilot on your data and evaluate it against the success metric agreed in Discovery. The metric is predefined and fixed before we start: no moving goalposts, in either direction. You see real performance on your real data before funding production.
We deploy into your stack, next to your team. Integration, security review, load testing, rollback paths: production engineering, not a notebook handed over as slides. Your engineers are in the loop from the first deployment so the system is owned, not orphaned.
Models drift and processes change. We instrument the system so drift is caught by a dashboard, not by a customer. We stay on watch after launch, or hand over a monitoring setup your team can run on its own.
Cost, hype and data security. Named up front, answered straight.
Senior engineering is not cheap, and we will not pretend otherwise. What we do instead is cap your exposure. Each phase is a separate decision with its own budget, and the first two phases exist precisely to kill weak projects before they get expensive. You never fund production on faith: by the time you commit real money, you have seen the model perform against the agreed metric on your own data.
Most AI initiatives stall before production, and some problems do not need AI at all. If a regression, a heuristic or a well-built spreadsheet solves your problem, we will say so, because our track record only survives on systems that work. The predefined PoC metric is how we keep ourselves honest: hype has nowhere to hide from a number agreed before the code exists.
We work under NDA from the first conversation. Wherever possible the work runs inside your infrastructure and your cloud accounts. Data leaves your environment only when the audit strictly requires it, access is least-privilege and named, and extracts are deleted on exit. We do not train models on your data for anyone else. If your industry carries specific compliance requirements, we design to them in the Data Audit phase, not after.
You bring the bottleneck. We bring a senior engineer, not a salesperson.
You describe the operation and where it hurts. Plain language, no preparation needed.
We ask about the process, the data you keep, and what a win would be worth per month.
You get an honest read on the spot: worth a PoC, not yet, or no. And if it is no, what we would fix first instead.
No deck, no obligation. If we are not the right team for your problem, we say so and, when we can, point you to who is.
One session with a senior engineer. We'll tell you whether AI pays for it, and what it takes to ship.
Antenor 