Technology · Case study
Midmarket Financial Products company
ML models died on the way to production. Six PoCs in two years; none in front of customers.
The context
A midmarket financial-products company had run six machine-learning proofs of concept in two years and put none of them in front of customers. The models worked in notebooks and died on the way to production. There was no platform to evaluate, deploy, observe or roll them back safely.
At a glance
- Sector
- Technology
- Headline result
- 5
- Service lines
- AIAI-Powered Digital Engineering
What we did
The approach.
- 01
Built the MLOps platform
We stood up an owned MLOps platform on the company's existing cloud footprint, with no new vendor and no lock-in, and with evaluation, observability and rollback baked in.
- 02
Made shipping repeatable
A standard path from notebook to production turned model deployment from a bespoke ordeal into a repeatable, governed release.
- 03
Instrumented for trust
Drift detection, eval gates and observability mean a model that misbehaves is caught and rolled back before customers feel it.
- 04
Transferred ownership
The company's team now ships models themselves on the platform we built and documented with them.
The outcome
What changed.
5
AI models in production in under 8 weeks
6→0
stalled PoCs cleared off the backlog
Owned
platform on existing cloud, no lock-in
More work
Other engagements.
Let's talk
Want an outcome like this?
A senior engineer on the call from day one. Two weeks to a written architecture and a working reference build.