AI in production means a language model wired into a real workflow: reading documents, sorting them, drafting replies. It is judged on tasks removed, not on a demo, and always ships with guardrails, logging and a manual fallback.

The most useful AI is rarely the one that impresses in a meeting. It is the one that reads three hundred invoices a month without complaining, or answers the same question asked forty times a day. We start from the task to remove, not the model to use.

What we deliver

Identifying the tasks where automation actually pays
Document processing: extraction, sorting, verification
Assistants wired to your data, not to a generic model
Guardrails, logging and manual fallback when in doubt

How we work

01 · Weeks 1-2

Audit

Understand the business, map what exists, identify risks.
02 · Weeks 3-4

Architecture

Data model, stack, integrations — validated before a single line of code.
03 · Weeks 5 to 12

Build

Short sprints, a demo every two weeks, CI/CD from day one.
04 · Week 12

Go-live

Data migration, team training, a supervised switchover, rollback ready.
05 · From month 4 onwards

Run & automation

We stay: monitoring, fixes, then we automate whatever daily use has revealed to be repetitive.