AI/ML Solutions

The reliable wins are document processing, search, classification and forecasting. Extracting data from invoices and contracts, answering questions over your own documents, routing tickets automatically, and predicting demand from historical data all work well today. These deliver measurable time savings without betting the product on model behaviour.

Often yes, though less than people assume is needed. Large language models handle many classification and extraction tasks with a handful of examples rather than thousands. Genuinely custom predictive models need more history. We assess your actual data before proposing an approach, since most failures are data-quality problems rather than modelling ones.

We ground it in your data and constrain what it can say. Retrieval-augmented generation restricts answers to your own documents, structured output enforces valid formats, confidence thresholds route uncertain cases to a human, and every response cites its source so users can verify. Unconstrained free-text generation is the wrong pattern for business systems.

Ongoing model usage is a real operating cost, not a one-off build expense. It scales with request volume and text length. We design for it directly through caching, smaller models for simpler steps, and prompt efficiency, and we forecast monthly cost before development starts so there is no surprise after launch.

Not under the arrangements we deploy. Enterprise API agreements with major providers exclude customer data from training by default. Where data sensitivity requires it, we deploy open models within your own infrastructure so nothing leaves your environment at all.