Our model works in a notebook but not in production — can you help?

That is our bread and butter. We turn experiments into reliable services with testing, monitoring and safe rollouts.

Do you support our existing cloud?

Yes — AWS, GCP, Azure or on-prem. We work in your environment and hand over infrastructure as code.

Does MLOps apply to LLM apps too, or just trained models?

Very much both. Prompt versioning, evaluation suites, output guardrails and cost monitoring are the LLM equivalents of classic MLOps — and skipping them causes the same production surprises.

Can you fix an existing system another team built?

Yes. A short audit maps what exists, what is fragile and what is missing, then we harden it in place — no rewrite required unless the audit honestly justifies one.

How do we know it's working?

Because you can see it: dashboards for quality, drift, latency and cost, alerts before users notice, and an evaluation gate that blocks bad releases automatically.

MLOps & AI Deployment Services

Most AI projects do not fail at the model — they fail at everything around it. MLOps is the difference between a demo and a dependable product.

The problem it solves

An AI system that worked at launch quietly degrades: data drifts, usage patterns shift, a model update changes behaviour, costs creep. Without the operational layer, you find out from users. With it, you find out from a dashboard — before they do. (This gap is the villain in half the stories in why AI projects fail.)

What we put in place

Pipelines. Training, retraining and deployment as code — versioned, repeatable, reviewable. For LLM systems: prompt and configuration versioning with the same rigour, because a prompt edit is a deploy whether you treat it as one or not.

Evaluation as a release gate. A test suite of real cases that every model, prompt or pipeline change must pass before it ships. Regressions get caught in CI, not in customer tickets.

Monitoring and drift detection. Live tracking of accuracy proxies, output quality, latency and cost per request, with alerts wired to the person who can act. When the world changes under the model, you know that week — not at the quarterly review.

Serving that scales sensibly. Zero-downtime rollouts, instant rollback, autoscaling tuned to your traffic, and cost visibility per feature so finance is never surprised.

How we deliver it

Everything arrives as infrastructure-as-code in your cloud — AWS, GCP, Azure or on-prem — documented and handed over in working sessions with your team. No proprietary platform, no dependency on us to operate it. Some clients keep us on retainer for the operational watch; others take the keys entirely. Both are the deal working as intended.

We also do this for systems we did not build: a short audit of your existing AI stack (what is fragile, what is unmonitored, what will hurt first), then hardening in place. It pairs naturally with an ongoing custom AI development engagement, but it stands alone too.

If your AI works in a notebook and wobbles everywhere else, talk to us — we reply within one business day.

MLOps & Deployment