Custom enterprise RAG systems that answer from your own documents and data, and cite the source of every answer.
The six we build most often, and any other system your knowledge needs.
One place for your team to ask across your wikis, shared drives, tickets and databases, with every answer citing the page it came from.
Support and help-desk assistants that answer customers or staff only from your own content, and hand over when the answer isn't there.
Questions answered across contracts, policies, manuals and reports, with the tables, scans and diagrams in them read too.
Search and answers built into your own app or SaaS product, each working on one customer's data and nothing else.
RAG on your own servers or in your private cloud, with open models, so your documents never leave infrastructure you control.
The knowledge your AI agents look things up in, with the same index and the same permissions, so an agent acts on facts.
Six steps, from a sample of your documents to a system your team trusts with its questions.
Your sources, real questions and access rules, with a test set of the answers to hit.
Documents parsed, scans and tables included, chunked and indexed with their permissions.
Search, reranking and answers that cite their sources, inside your app.
Scored against the test set, with the failures listed and a clear go or no-go.
Deployed in your environment with single sign-on, access control and monitoring.
The index kept fresh, answers reviewed, and cost and accuracy tracked.
Your sources, real questions and access rules, with a test set of the answers to hit.
Documents parsed, scans and tables included, chunked and indexed with their permissions.
Search, reranking and answers that cite their sources, inside your app.
Scored against the test set, with the failures listed and a clear go or no-go.
Deployed in your environment with single sign-on, access control and monitoring.
The index kept fresh, answers reviewed, and cost and accuracy tracked.
The parts we build into a RAG system, set for your documents and who reads them.
PDFs, scans and spreadsheets read with their tables and layout intact, not flattened into text.
Split by section and clause, so a passage keeps its heading and never stops mid-sentence.
Exact words and meaning searched together, then the best passages ranked to the top.
Each person gets answers only from documents they can already open in the source system.
Each answer names the documents and passages it came from, one click from the original.
When the answer isn't in your documents, it says so and points to who might know.
Real questions with their right answers and sources, run against every change before it ships.
New and changed documents indexed as they change, and deleted ones removed from answers.
Run in your cloud, on your servers or air-gapped, wherever your data is allowed to live.
Starting something new with AI, or adding it to software you already have.
An AI MVP with the features that matter most, to launch and learn.
An AI product designed, built and launched in full, by one team.
AI engineers join your team, or a dedicated team runs the work.
AI built into the systems you already run, working with your data and permissions.
A demo or proof of concept made reliable, secure and measured, ready for real users.
Hosted and monitored, with models evaluated and updated as your data changes, agreed with you.
Custom software development across industries: web, mobile and AI applications, built and security-audited by one team.
We also build software for insurance (insurtech), government (govtech), energy and media. If your industry is not listed, we can still build for it.
Three products of our own, designed, built and kept running by the team that would build your RAG system.
Retail · SaaS
A point-of-sale system that keeps sales, stock and receipts together on till, tablet and phone.
Travel
A travel booking app whose AI assistant finds flights, hotels and trip ideas, through to a confirmed booking.
AI
An AI assistant for customer support and sales that answers questions and guides buyers.
What teams ask before they let an AI answer from their documents, answered plainly.
Most likely. We connect to where your knowledge lives — Google Drive, SharePoint, Confluence, Notion, help desks, databases and file shares — and read PDFs, Word and PowerPoint files, spreadsheets and scans. Where a system has an API we sync from it; where it doesn't, we work from regular exports.
RAG looks up the relevant passages in your documents when a question is asked, and has the model answer from them, citing them. Fine-tuning changes the model itself, which suits a style or a format but doesn't keep facts current or show where they came from. For answering from your knowledge RAG is usually the right choice, and when it isn't, we'll say so.
It answers only from the passages it retrieved, and cites them. When nothing relevant is found, it says the answer isn't in your documents instead of guessing. And every release is scored on a set of your real questions, so a change that makes the answers worse doesn't ship.
Yes. Permissions are carried over from the source systems and checked at retrieval, so a passage someone couldn't open in SharePoint or Drive never reaches their answer. Access follows your single sign-on, and changes when it changes.
Yes. We deploy RAG on-premise, in your private cloud or in an EU region, with open models such as Llama and Mistral where documents can't go to an outside provider. The vector database and the index stay inside your network.
Claude, GPT, Gemini or open models such as Llama and Mistral; Pinecone, Weaviate, Qdrant or pgvector; LlamaIndex or LangChain; Unstructured and Docling for parsing, Cohere Rerank for reranking and Ragas for evaluation. We choose by testing on your documents, and none of them locks you in.
Before we build, we collect real questions, each with the right answer and its sources. Every change is scored on them — is the right passage found, does the answer keep to it, is it cited — and the score decides whether a change ships.
The index stays in sync with the sources: new and edited documents are indexed again, and deleted ones disappear from answers. How often is set per source, from every few minutes to nightly.
Collecting the documents and questions and building the evaluation set takes one to two weeks, a prototype on your documents two to six, and production — access control, sync and monitoring — a few weeks more. The estimate sets out the timeline before any work starts.
Building depends on the sources, the formats and the access rules; after a first call we send a written estimate. Running cost is mostly hosting the index and the model's usage per answer, which we estimate before we build.
