Twelve weeks from kickoff
to production.
Here's exactly what happens.
Things go wrong on every AI project. Honest agencies say so. This page tells you what the process looks like — including the parts that break.
Week by
week.
We dig in before we write a line of code.
We dig into your data, your existing systems, and your actual goal — which is often different from the brief. We come back with an architecture, a model approach, and a build plan with weekly milestones.
This is also where we find the problems. Bad data quality, unclear success metrics, unrealistic scope. We rescope in week 2 if needed. We don't pretend these don't exist and surface them in week eight.
Progress · week 1–2 of 12
Working software every Friday. Not slides.
Iterative weekly demos. You see working software every Friday — not a progress report, not a status update, but a thing you can actually use. We deploy to staging continuously.
Models get trained, evaluated, retrained. The frontend takes shape. By week 8 you're using a near-production version internally. If we're off-track, you know by week 4 — not week 11.
Progress · week 3–10 of 12
Production deployment. You get the keys.
Production deployment. Monitoring set up. Documentation written. Your team trained on the system. We hand over the keys — source code, trained models, deployment scripts, monitoring dashboards.
But we're still on call. Maintenance is a feature, not an upsell. The retainer is available if you want continued coverage. Our number is in the handover doc either way.
Progress · week 11–12 of 12
Hard limits.
No exceptions.
Everything you
need to own it.
Working AI product in production
Not a prototype. Not a demo. Running on real infrastructure, handling real load.
Source code — yours, fully
Complete repo. No dependency on our infrastructure. No lock-in.
Trained models and weights
Every model we trained is handed over. You can retrain, fine-tune, or replace.
Deployment scripts and infra config
Docker files, CI/CD pipelines, environment configs — everything to redeploy independently.
Documentation
Architecture overview, API reference, runbooks for common failure modes.
Monitoring dashboards
Model drift alerts, error rates, latency. Configured and live from day one of production.
Handover call
A session with your team walking through every part of the system. Recorded if useful.
Our number, in case it breaks at 2am
Optional retainer for ongoing coverage. Mandatory response if it's something we built wrong.
The first call is free. Thirty minutes.
We'll tell you honestly whether the project is a fit, what the architecture looks like, and what the risks are. No pitch if we can't help.