We build knowledge assistants that answer from your own documents and data — with sources — and wire them into the tools you already run on. If it has an API, we can build on it.
A general AI knows a little about everything. Yours should know everything about you.
01How RAG works
Retrieval-augmented generation looks up the right passages in your own material first, then writes the answer from them. Current, specific and checkable.
PDFs, docs, policies, product data, tickets, emails, your website — whatever holds the answers.
Cleaned, split into meaningful passages and tagged with source, date and permissions.
Stored as searchable meaning (embeddings) plus keywords, so both phrasing and exact terms match.
Each question pulls only the most relevant passages — the ones this user is allowed to see.
The model writes a clear answer from those passages and cites exactly where it came from.
02What we build
Six common starting points. Most clients begin with one and grow from there.
Staff ask in plain English and get answers from handbooks, SOPs, contracts and past projects — with the source linked.
A website or inbox assistant grounded in your real policies, prices and product data. Hands off to a human when it should.
Search thousands of files by meaning, compare versions, pull clauses and summarise long reports in seconds.
Answers that quote the exact rule and section, so teams in regulated work can check before they act.
Draft proposals and replies from your case studies, pricing and past wins — on-brand and accurate.
Combine knowledge with API actions: look up an order, raise an invoice, book a slot, update the CRM.
03APIs & integrations
Payments, accounting, CRM, e-commerce, messaging, ads, data feeds and your own internal systems. We've built on many of these for our own live products — so we know where the sharp edges are.
04How we build
The difference between a demo and a system your team relies on every day.
We use the right model for the job — Claude, Grok, open-weight models, or a mix — and can switch as the market moves.
Your index, logs and data live on infrastructure you control. No lock-in to a platform that can change its terms.
Every reply shows what it was based on. If the knowledge isn’t there, it says so instead of guessing.
We test against a set of real questions before launch and keep scoring answers, so quality is tracked over time.
05Questions
What clients usually ask before a knowledge or integration build.
Retrieval-augmented generation. Instead of relying on what an AI model happened to learn in training, it first looks up the relevant passages in your own material, then answers from those. The result is current, specific to your business and traceable to a source.
Grounding answers in retrieved sources and showing citations cuts invented answers dramatically, and we instruct the assistant to say "I don’t know" when your material doesn’t cover a question. We also test it on your real questions before it goes live.
By default we host the index and application on infrastructure you control, respect per-user permissions at retrieval time, and use model providers under terms that exclude training on your data. Sensitive setups can run on open-weight models entirely in-house.
If a service has a documented API — or even a reliable export — we can usually integrate it: payments, accounting, CRM, e-commerce, messaging, analytics, ad platforms, databases and your own internal systems.
A focused knowledge assistant on a defined set of documents is typically live in two to four weeks. Larger multi-source systems and agent workflows are scoped in phases so you see value early.
Fixed-price builds scoped after a short call, plus an optional monthly plan for hosting, monitoring and keeping the knowledge fresh. No per-seat licences.
06Start here
A 30-minute call is enough to map your sources, the systems to connect and a fixed-price first phase.