▤ Capability

MLOps, LLMOps & Inference

Deploy, serve and scale models reliably.

Discuss MLOps / LLMOps

We build the delivery pipeline for models and prompts: CI/CD, model registry, feature and prompt versioning, monitoring and drift detection, and high-throughput self-hosted inference on GPUs when you need privacy or cost control.

Typical use cases

Challenge. Data can’t leave your environment, or API costs are too high at volume.

What we build. Open models served with vLLM/TGI on autoscaling GPU clusters, with batching, quantisation and an OpenAI-compatible API.

Challenge. Models are deployed by hand from notebooks.

What we build. A standard path from experiment to production: pipelines, registry, automated deployment and monitoring.

Challenge. Model quality silently degrades in production.

What we build. Data and prediction monitoring with alerting and retraining triggers.

Examples

Use cases with MLOps / LLMOps.

Manufacturing & Logistics Visual defect detection on the line Detect defects consistently from camera images at production speed. Image & VisionMLOps / LLMOps Open → SaaS & Technology Move from API to self-hosted model Cut inference cost and keep data private with a fine-tuned open model. Fine-tuningMLOps / LLMOps Open → SaaS & Technology Ticket routing without per-call LLM costs Millions of support messages classified by a distilled in-house model instead of a large LLM API. AI ResearchFine-tuningMLOps / LLMOps Open →

Next step

Let's talk about your AI system.

A free 30-minute call with an Engagement Lead or AI Architect. You'll leave with a clearer view of options, risks and cost, whether or not we work together. Your case doesn't need to fit any box on this site; just tell us what you're facing.

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Keep exploring

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