02 / Practice
Custom AI / ML Development
Production systems, not prototypes — retrieval pipelines, agents, evaluated models, shipped into the stack you already run.
/ The problem
Where this work usually goes wrong.
A convincing demonstration is roughly ten percent of the work. The other ninety is evaluation, failure handling, cost control, monitoring and the security review. Teams that skip it ship something that impresses in a board meeting and quietly degrades in production.
/ What we do
The work itself.
- 01
Retrieval-augmented systems over your own documents, tickets and operational data
- 02
Model selection driven by an evaluation harness on your data, not by benchmark tables
- 03
Regression suites so a model or prompt change cannot silently reduce quality
- 04
Deployment, observability, cost ceilings and hand-over documentation
/ What you receive
Deliverables, and how long it takes.
Typically 2–4 months from scope to production hand-over.
A production system running in your environment, with source and infrastructure definitions
An evaluation and regression suite your engineers can run on every change
Monitoring, cost reporting and a written operating guide