Your knowledge, answerable in plain English.
A private knowledge system that answers questions from your own documents, data, and processes, grounded, cited, and always current.
- Typical pilot
- 4 to 8 weeks
- Pricing model
- Fixed scope, fixed price
- Coverage
- USA · UK · Remote-first
- NDA available
- Yes
What is a RAG (retrieval-augmented generation) system?
A RAG system is an AI that answers questions using your own documents and data rather than only its training, it retrieves the relevant passages from your knowledge base first, then generates a grounded, cited answer, so responses stay accurate and traceable instead of hallucinated.
General chatbots make things up because they answer from memory. A retrieval-augmented generation system fixes that by connecting the model to your actual content, policies, contracts, product docs, tickets, wikis, indexing it, and forcing every answer to be built from retrieved source passages with citations. We build private RAG systems on your own data, deployed in your environment, so staff and customers get instant, grounded answers from a single source of truth that updates as your documents do. The output is a knowledge system you can trust and audit, not a black box.
Tell us one thing. See ai & rag systems run.
Add a couple of details, get a personalized result and a live look at the system, then we carry everything into a tailored plan, no retyping.
Before you talk to us, see what ai & rag systems is costing you.
Adjust the sliders. The number on the right is what staying manual costs in a year. Conservative math, defensible assumptions, no signup to see the result.
Skip the audit. Book a 15-minute scoping call.
What's actually painful about ai & rag systems today.
Your organization's knowledge is scattered across documents, drives, tickets, and people's heads. Finding an answer means knowing who to ask or where to dig, and the answer is often out of date by the time you find it.
Generic AI tools don't help, because they don't know your business. They confidently make things up instead of citing your actual source of truth.
What staying manual actually costs.
- 01Hours per week the team spends on ai & rag systems work that could run itself12 to 25Mid-sized teams routinely bleed a full-time role to manual coordination in this area.
- 02How long it takes for a new ai & rag systems initiative to compound8 to 16 wksBecause every change has to be done by hand, then redone by hand next quarter.
- 03Share of ai & rag systems decisions that depend on one person being available60%+When they're on PTO or sick, the work doesn't just slow, it stalls.
What changes once the system runs.
Here's the system that runs ai & rag systems end to end.
- 01Connect sources
Documents, drives, wikis, ticketing systems, and databases are indexed into one searchable layer.
pending - 02Retrieve
Questions are matched against the right sources, the most relevant passages come back first, with citations.
pending - 03Answer
A grounded answer is composed from your sources, not invented, and each claim links to where it came from.
pending - 04Stay current
Sources re-index on a schedule, so the system reflects the latest version of your knowledge, not last year's.
pending
Map a workflow you actually run. See what it costs.
Drag the steps into the order your team actually does them. We show you the hours and dollars the manual version costs, and exactly which steps the system takes off your plate.
Principles
- 01
Outcome, not output
We measure success by hours reclaimed, deals closed, errors removed, not by tickets filed.
- 02
Boring over clever
We pick the most boring, durable technology that solves the problem. Cleverness is a liability.
- 03
Fixed price, fixed scope
No open-ended retainers. Every engagement ships with a one-page scope you can hold us to.
- 04
Build to hand off
We document, train, and hand over. The system is yours to run, modify, or extend without us.
AI & RAG Systems in 8 metros, and counting.
It answers from your knowledge, not the open internet, and it cites where each answer came from, so you can trust and verify it.
We build on retrieval-augmented generation using current language models, vector storage, and connectors to your existing knowledge sources.
Stack details shared during scoping, under NDA.
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Run the audit above, or book a 15-minute scoping call. We'll send back a one-page scope in 48 hours.
Response time
≤ 4 business hours
Coverage
USA · UK · EU
Team
10 engineers · 1 PM