Knowledge-base assistants
Internal Q&A over policies, manuals, wikis and past tickets, so staff stop searching and asking around.
AI & Machine Learning
RAG systems: AI that answers from your own data
Answers grounded in your documents, with the sources to prove it.
Point an assistant at your manuals, policies and past tickets. It finds the right passage, answers in plain language and links to the source, so every reply can be checked.
An AI assistant that knows your business.
Retrieval-augmented generation (RAG) connects a language model to your own information. When someone asks a question, the system first searches your documents, wiki, tickets or database for the most relevant passages, then asks the model to answer using only those passages — and shows where each answer came from. The model stays general; the knowledge stays yours and up to date.
A RAG system is only as good as its retrieval. We spend most of the effort there: cleaning and splitting documents sensibly, choosing embeddings and hybrid search, re-ranking results, respecting who is allowed to see what, and measuring answer accuracy on real questions from your team before anyone relies on it.
RAG Systems
What we offer
Customer-facing support bots
Answers from your help centre and product docs, with sources and a hand-off to a human when needed.
Document ingestion pipelines
Loading PDFs, Word, web pages, Confluence, Notion or databases, and keeping the index in sync as they change.
Vector & hybrid search
Embeddings, keyword search and re-ranking combined for accurate retrieval, on pgvector, Qdrant, Pinecone or Elasticsearch.
Access control
Answers that respect permissions, so a user only ever sees content they are allowed to read.
Accuracy evaluation
Test questions with known answers, retrieval and faithfulness scores, and dashboards to track quality over time.
When it’s the right fit
- Knowledge is spread across documents that people struggle to search
- Support agents answer the same questions from the same manuals every day
- You need an AI assistant that cites sources, not one that sounds confident
- Your information changes too often to retrain or fine-tune a model
How we deliver
- 01Source mappingWe list the documents and systems to include, who may access them, and collect real questions with correct answers.
- 02Retrieval prototypeIngestion, chunking and search tuned until the right passages come back for your test questions.
- 03Assistant & interfaceAnswer generation with citations, in a web app, Slack, Teams or your own product.
- 04Evaluate & operateAccuracy measured before launch, index updates automated, and feedback from users fed back into tuning.
Technology
Tools we use for RAG Systems
- LangChain
- LlamaIndex
- pgvector
- Qdrant
- Pinecone
- Elasticsearch
- OpenAI
- Python
Frequently asked questions
What is the difference between RAG and fine-tuning?
Fine-tuning changes how a model writes and behaves; RAG gives it facts to work from at question time. For answering questions about your own, changing documents, RAG is usually cheaper, easier to keep current and able to cite sources.
Which document formats can a RAG system use?
PDF, Word, Excel, HTML, Markdown, and content in tools such as Confluence, Notion, Google Drive, SharePoint or your own database. Scanned documents can be included with OCR.
How accurate is a RAG assistant?
We measure it on a set of real questions with known answers before launch and report the numbers. When the answer is not in your documents, the assistant is built to say so rather than guess.
Next step
Tell us what you want to build.
A free 30-minute consultation with an engineer — no obligation, reply within one business day.
Contact us