AI Development

We start with a short feasibility pass looking at whether the outcome is genuinely predictable or generative from the data you already have. If a rules-based script or a simpler integration solves it more reliably, we'll tell you that instead of building a model you don't need.

Mostly Claude and OpenAI for generative work, and PyTorch for custom predictive models — but we pick based on your data sensitivity, latency needs, and budget rather than defaulting to one vendor. We architect so swapping the underlying model later isn't a rebuild.

No. For most projects we ground the model in your data at query time (retrieval) rather than fine-tuning on it, which keeps your information out of any provider's training set. When fine-tuning does make sense, we use private endpoints and walk through data handling with you before anything starts.

Grounding it in retrieved facts instead of relying on memory, constraining outputs where correctness matters, and running an evaluation set against known-correct answers before launch — then tracking accuracy in production, not just at demo time.

A focused prototype against real data usually takes two to four weeks, so you can see whether it actually works before committing further. A production-grade feature with monitoring and retraining typically runs eight to twelve weeks depending on scope.

No. We hand off documented pipelines and can retain ongoing monitoring and retraining as a support engagement, but everything is built so an in-house engineering team could pick it up without us if that's what you'd rather do.