MLOps & Model Operations
Evaluation infrastructure, observability, hallucination and regression tracking, continuous retraining.
Why we work in this area
Most AI projects die at the demo stage. The model works, the presentation goes well, then production begins, the data shifts, performance quietly degrades and nobody notices.
This area is the technical substance behind our claim of "end-to-end development". Delivering a model is not the end of the work; it is the beginning of monitoring.
What we work on
- Evaluation infrastructure: versioned test sets that run automatically for every model
- Observability: continuous tracking of input distribution, output quality, latency and cost
- Hallucination and regression detection; comparing quality across version transitions
- Data and concept drift detection, continuous retraining pipelines
- Optimising inference cost and latency
A technical assessment for your AI project
Your project's feasibility, risks and timeline are assessed in a technical consultation.