AI and machine learning development that reaches production.
Most AI work does not fail at the model. It fails at everything around it: no evaluation set, no monitoring, no retraining path, and an inference bill nobody forecast. We do AI development and MLOps as one job — the model, the API around it, and the operational loop that keeps it honest after launch.
When teams call us
- The prototype works in the demo, but shipping it safely keeps slipping every sprint.
- The model is live and nobody can say whether it got better or worse last month.
- The LLM feature works, and the inference bill tripled without anyone noticing.
What the work covers
LLM integration and RAG pipelines
Retrieval, chunking, prompt orchestration and vector storage designed around your actual documents — with a measured answer quality baseline, not a demo that looked convincing once.
Model development and fine-tuning
Feasibility check first: we say plainly when a smaller model, a rules engine, or an off-the-shelf API is the better answer. When training is justified, we build the dataset, the training loop, and the evaluation harness together.
Production infrastructure for ML
The serving API, autoscaling, batching, caching and the deployment pipeline — so a new model version is a routine release rather than a project.
Evaluation, monitoring and retraining
Automated evaluation on every change, drift and latency monitoring, and explicit retraining triggers. This is the part that decides whether the system still works in month six.
Inference cost control
Model routing, caching, quantisation and spend guardrails with alerting, so cost scales with usage instead of surprising you at the end of the quarter.
What you get
- A production API with autoscaling, logging and rollback
- An evaluation suite that runs on every model change
- Monitoring dashboards for quality, latency and spend
- Documented retraining triggers and a runbook to follow them
- A walkthrough with your team so they can run it without us
Related work
Questions we hear
Have an AI project that needs to reach production?
Start the conversationYes — that is the most common starting point. We take an existing model or notebook and build the serving layer, evaluation and monitoring around it, without retraining anything unless the evaluation shows it is necessary.
Not always. Many LLM use cases need retrieval over documents you already have rather than training data. The audit phase establishes which of the two you are actually looking at before anything gets built.
By measuring cost per request from day one and designing around it: routing simple requests to smaller models, caching aggressively, and setting hard spend alerts. Cost is treated as a design constraint, not a post-launch surprise.
We say so during the audit, before you have paid for a build. A deterministic pipeline that always works beats a model that works 80% of the time more often than the market admits.