Custom AI Agent Development

Not through us. We use the providers' API tiers, which do not train on submitted data by default, keep retrieval content in storage you control, and document exactly what is sent to which provider so you can review it against your own policy.

Three things, together: answers are grounded in retrieved content with citations; the prompt and tooling make 'I don't know, let me hand this over' a first-class outcome; and a graded evaluation set catches regressions before they reach users. We also report the measured failure rate rather than claiming there is not one.

Whichever fits the task, cost and latency budget — usually Claude or GPT-class models, sometimes a smaller model for classification steps. We keep the model behind an abstraction so switching is a configuration change, not a rebuild.

Token and infrastructure costs are yours and depend on volume. We estimate them during scoping from the actual prompt and retrieval sizes, instrument real cost per conversation after launch, and will tell you when a cheaper model or a cache would do the same job.

Usually yes — anything with an API. Common integrations are CRMs, helpdesks, databases, spreadsheets and messaging platforms, either directly or through n8n. Where an MCP server gives cleaner, reusable access, we will say so.