Machine learning that earns its place in production.

Machine learning engineering

From demand forecasting to anomaly detection and document ranking, we build models that solve a specific, measurable problem – along with the software that keeps them working.

What you get

• Problem framing and baseline models
• Model development, evaluation and error analysis
• Deployment as APIs, batch jobs or embedded components
• Monitoring for data drift and degrading performance

LLM applications

Large language models are useful when they are grounded in your own data and wrapped in careful engineering. We build assistants, search and document workflows that are evaluated, not just demoed.

What you get

• Retrieval-augmented generation (RAG) over internal documents
• Agents and tool-using workflows
• Structured extraction from contracts, reports and forms
• Evaluation of quality, cost and latency

Data platforms

Good models need reliable data. We design and build the pipelines, integrations and backends that collect, clean and serve it.

What you get

• Data pipelines and scheduled jobs
• Integrations with APIs and third-party systems
• Backends and internal tools
• Data modelling, quality checks and documentation

Advisory

Sometimes the most valuable outcome is a clear answer. We run short, focused engagements that help you decide what to build – and what not to.

What you get

• Feasibility studies and scoping
• Technical due diligence
• AI and ML strategy and roadmaps
• Reviews of existing models and pipelines