AI & Machine Learning Engineering
Your model works in a notebook. We make it work in production.
Most machine learning projects die in the gap between the research notebook and the live product. The model performs well in testing, and then it has to serve real requests at real latency against real data, with monitoring and a rollback path — and the work stalls. The data scientist has finished their job and the engineering team has inherited something they didn't design.
Closing that gap is the work we do most. We take trained models and build everything around them that makes them a product: inference APIs that handle concurrency, reproducible data pipelines, containerised deployment, and the monitoring that tells you when accuracy starts to drift before a customer does.
Model deployment and serving
Inference endpoints built in FastAPI for concurrent production traffic, not demo loads.
Inference optimisation
Batching, caching, and model-loading strategy to bring latency and compute cost down.
Production data pipelines
Preprocessing and feature pipelines that behave identically in training and serving, so results don't quietly diverge.
MLOps
Containerised deployments, model versioning, automated retraining, and drift monitoring.
Integration
Connecting the model to the product surfaces and internal systems that consume it.
- Do you build models from scratch, or only deploy existing ones?
- Both, though the deployment work is where we add the most value. If you have a dataset and a defined problem, we can handle model development as well — but many teams already have a model that works and need the engineering around it.
- What if our model isn't accurate enough yet?
- Then production is the wrong next step, and we'll say so. We'd rather scope a shorter evaluation engagement than build infrastructure around a model that isn't ready.
Work directly with the experts responsible for your project.
No sales hand-off. Start a conversation about your Zero Trust, SASE, or OT/ICS environment — or the software, cloud, and data platforms behind it.