Services

MLOps that keeps models running.

We take models from notebook to production — inference APIs, versioning, monitoring and CI/CD — so they stay fast, observable and reliable long after they ship.

The gap between a trained model and a dependable production service is where most AI projects quietly stall. We close it: packaging models behind clean inference APIs, versioning them, watching their accuracy and latency in the wild, and giving you a path to ship a new version without holding your breath. The interesting part of machine learning starts after the model works.

Where we help

Inference APIs and serving infrastructure for cloud or edge
Model versioning, registries and reproducible builds
CI/CD for models — automated tests and evaluation gates before deploy
Monitoring for latency, cost, accuracy and data drift in production
Cost and performance optimisation via batching, caching and quantisation
Model export and acceleration with ONNX for portable, faster inference
Rollback, canary and shadow-deploy strategies for safe releases
On-premise and local-model deployment where data residency demands it

Example builds

Custom models into service. Training a custom neural network is half the work; the other half is serving it reliably, watching it, and shipping improvements safely. We build the path that gets a trained model into production and keeps it healthy there.

Models on your own hardware. Our privacy-focused CRM runs language models entirely on-premises — which means owning the deployment: serving, resourcing and updating models inside the client's environment rather than calling out to an API.

A pipeline that runs daily. StockTrack isn't a one-off model run — it's a standing pipeline that ingests, processes and summarises new data on a schedule, with the reliability that turns a clever idea into a service people depend on.

How we engage

We meet you wherever your model is — a notebook, a half-built API, or something fragile already limping along in production. A short sprint gets it deployed cleanly behind a versioned, monitored endpoint, then we add the CI/CD and observability that make future changes routine rather than risky. We favour boring, well-understood infrastructure over novelty, because models are quite enough novelty on their own. See our services for how engagements run.

Common questions

Can you deploy a model we already trained?: Absolutely — that is a common starting point. We take an existing model and stand it up behind a reliable, versioned inference service with monitoring, so it stops being a notebook artefact and becomes a dependable part of your stack.
How do you know when a model degrades?: We monitor inputs and outputs in production for data drift and accuracy changes, alongside latency and cost. When the live distribution moves away from training data, you find out early — before users do.
Cloud or on-premise serving?: Both. We deploy to your cloud of choice or run models on-premises where data residency or confidentiality requires it, and design the serving layer around that constraint from the start rather than retrofitting it.

AI & machine learning LLM applications Computer vision

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Where we help

Inference APIs and serving infrastructure for cloud or edge
Model versioning, registries and reproducible builds
CI/CD for models — automated tests and evaluation gates before deploy
Monitoring for latency, cost, accuracy and data drift in production
Cost and performance optimisation via batching, caching and quantisation
Model export and acceleration with ONNX for portable, faster inference
Rollback, canary and shadow-deploy strategies for safe releases
On-premise and local-model deployment where data residency demands it

Example builds

How we engage

Common questions

Can you deploy a model we already trained?

Absolutely — that is a common starting point. We take an existing model and stand it up behind a reliable, versioned inference service with monitoring, so it stops being a notebook artefact and becomes a dependable part of your stack.

How do you know when a model degrades?

We monitor inputs and outputs in production for data drift and accuracy changes, alongside latency and cost. When the live distribution moves away from training data, you find out early — before users do.

Cloud or on-premise serving?

Both. We deploy to your cloud of choice or run models on-premises where data residency or confidentiality requires it, and design the serving layer around that constraint from the start rather than retrofitting it.