AI that knows your business — grounded in your documents and data, backed by sources your team can verify, and integrated into the tools you already use.
We build retrieval-augmented systems over your knowledge and wire them into the places work actually happens.
Fixed-scope packages tailored to your custom requirements. We scope to a defined outcome and quote a fixed price before we start — no open-ended hourly billing. Larger or multi-phase builds are milestone-based.
The LLM agent layer we build for RAG systems is the architecture behind our production voice agent at Battery Smart — resolving live queries against driver, battery, and station APIs with zero human handoff for in-scope calls.
Retrieval-augmented generation. Instead of relying on what a model memorised in training, the system retrieves the relevant passages from your own documents and data at question time, then answers from those — with citations back to the source.
By grounding every answer in retrieved source material, showing citations, setting guardrails on what it will answer, and running evaluations against real questions before it goes live. When it can't ground an answer, it says so or escalates.
We choose per project based on your accuracy, latency, cost, and data-residency needs — and we keep the architecture provider-flexible so you're not locked to one vendor.
Yes — that's usually the point. We embed drafting, summarising, and Q&A inside your CRM, help desk, or internal apps so people use it where they already work.
Most pilots stall on the unglamorous parts: retrieval quality, evaluation, and monitoring. We build those in from the start, which is the difference between a demo and something you can put in front of customers.
Book a call — bring a real document set or workflow, and we'll sketch the retrieval architecture on the spot.