RAG pipelines, grounded answers and custom models for your data. Every reply cites its source: and what it can't cite, it says so instead of guessing.
“Budget Q3-2026 was approved by the finance commttee on 14 Jun: minutes, item 3.2. Source: board_minutes_2026-06.pdf (p.4). Marketing re-allocation was a separate item and is not approved: I can pull that agenda line if you need it.”
When the off-the-shelf agent pattern isn't enough, we build the intelligence and the system around it: grounded and safe to run inside a business.
Trained and tuned for your data and your schema: the right model for the job, not the one we happen to sell.
Retrieval-augmented knowledge systems that ground every answer. The retrieval is the product: the LLM is the narrator.
Applications that answer and act on live business data, not static dumps.
Web, chat, SMS and WhatsApp: one knowledge core, many surfaces.
Measured before release, not assumed. Every system is evaluated against your real tasks and scored per release.
Human-in-the-loop by design: approval gates, rate limits, schema-validated outputs.
The same grounded core, pointed at the workflows where your people slow down looking things up.
Answer from policies, manuals and tickets: with the page cited, so "trust the model" is never required.
Give operators instant answers from runbooks and past incidents: retrieval shortens every mean-time.
Summarize, compare and draft from your documents: every claim linkable to a source line.
Feasibility first, promises never. Four steps, each with a decision point your team owns.
A working retrieval proof on your real data: what it answers, cites and misses.
Model, retriever, guardrails and evaluation: chosen on cost, not fashion.
Shipped into the stack with annotation and human checkpoints where they matter.
Groundedness scored per release; drift corrected as your documents change.
Bring a knowledge base and a workflow: we'll show how retrieval, not memorisation, decides the answer.
Scope an intelligence solution →