Agents that answer from your documents, tickets, and records, with citations, so answers can be checked.
02
Workflow automation
Agents that take action in your tools, not just produce text about them.
03
Evaluation and guardrails
Test sets, refusal behaviour, and monitoring, so you know when quality moves rather than hearing it from a customer.
04
Integration
Connections to the systems the work actually lives in, with permissions respected end to end.
What you get
Four things that exist when we are done.
01
An evaluation set that measures quality on your real tasks
02
Cited answers, so every claim can be traced to a source
03
Cost and latency measured per interaction, not estimated
04
A model-agnostic architecture you can move to a newer model
Where this shipped
Logistics / Enterprise
Deployed confidentially for enterprise partner
A white-label AI operations assistant surfacing relevant context from fragmented internal knowledge bases, cutting hours of manual research per shift and routing queries to the right team automatically.
A general assistant has no access to your systems and no accountability for being wrong. What we build retrieves from your data, cites what it used, respects your permission model, and is measured against a test set for the tasks you actually care about.
No. We build on retrieval rather than fine-tuning by default, and we use providers and configurations that exclude your data from training. Where a client requires it, the whole system can run in their own cloud.
Ground it, constrain it, and measure it. Answers come from retrieved sources with citations; the agent is instructed to refuse rather than guess when retrieval returns nothing useful; and an evaluation set catches regressions before your users do.
Ongoing cost is model usage plus hosting, and it scales with how much you use it. We measure cost per interaction during the build so you have a real per-user figure before committing, not an estimate.
