AI and machine learning, in production

Most "AI consultants" will sell you a strategy document. We build models, ship them, and then run them — including the unglamorous parts nobody demos: monitoring, retraining, and knowing when the model is wrong.

What we actually build

All of these are things we have put into production, not capabilities we are describing from a vendor brochure.

• Forecasting and prediction models trained on your own history
• AI phone and chat agents that answer, qualify, and book work
• Retrieval over your documents, so answers come from your knowledge
• Extraction and classification of invoices, forms and email
• Private, on-premise models where data cannot leave the building
• Fine-tuning on your data once there is enough of it to matter

The one nobody else offers: keeping your data in your building

Most AI work sends your data to somebody else's servers. For a practice holding patient records, a firm holding client files, or anyone with a confidentiality problem, that is a non-starter — so the usual answer is "then you can't have AI." We run models on hardware you own, so the capability arrives and the data never leaves. Where the cloud genuinely is better, we will say so and let you choose.

And when not to use it

Often. If a scheduled job moving data between two systems solves the problem, that is cheaper, faster and cannot hallucinate. We would rather give you the boring answer than the impressive one — which is exactly why you should believe us when we do say a model is the right tool.

Common questions