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Answer HR, IT, policy and process questions from your handbooks and wikis, in Slack or Teams.
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RAG & Knowledge Assistants
AI assistants that answer from your own knowledge
Retrieval-augmented generation (RAG) lets an AI answer questions from your documents, policies, wikis and databases, citing its sources so people can trust the answer.
- Grounded in your dataAnswers come from your content, not the model's guesswork.
- Sources citedEvery answer links back to the document it came from.
- Permission-awareUsers only see answers from content they're allowed to access.
What we build
Knowledge assistants we build
One search box for everything your team or customers need to know.
Customer help centres
Self-service answers from your docs and FAQs, with smooth hand-off to a person.
Document Q&A
Ask questions across contracts, reports and manuals, and get answers with page references.
Data assistants
Ask questions about your database in plain language and get tables and charts back.
Sales enablement
Instant answers about products, pricing and case studies for sales teams.
Enterprise search
Hybrid keyword and semantic search across Drive, Confluence, Notion, SharePoint and more.
How we work
Accurate answers, by design
Most RAG problems come from messy data and missing evaluation. We fix both.
- Hybrid retrieval. Keyword and vector search combined, with re-ranking for precise results.
- Always up to date. Connectors re-index changed documents automatically.
- Search experience. We've built Elasticsearch-powered search for platforms like Blackwise and AcceptHotels.
Audit your content
Find the sources, formats, owners and access rules.
~1 weekIngest & index
Clean, chunk and embed content with metadata and permissions; keep it in sync.
2–4 weeksRetrieve & answer
Hybrid search, re-ranking and prompts tuned to cite sources and say 'I don't know'.
2–4 weeksEvaluate
A test set of real questions scored for accuracy, before launch and on every change.
Ongoing
Tools & technologies
- Anthropic Claude
- OpenAI GPT
- pgvector
- Pinecone
- Elasticsearch
- LlamaIndex
- LangChain
- Cohere Rerank
- Unstructured
- Python
- AWS
Case studies
Related work
AI · ML 
Moara
An AI-powered research platform that streamlines literature reviews from discovery to synthesis, with an AI agent that summarizes papers, identifies themes, finds gaps and compares findings.
Read case studyAI · ML 
Blackwise
An AI-powered search and discovery platform focused on optimizing results relevance through machine learning while avoiding invasive data collection.
Read case study
AcceptHotels
A custom-built, multi-page, fast-loading, and visually polished online booking platform showcasing properties attractively using API integrations for the Travel and Hospitality industry.
Read case studyWhat is RAG?
Retrieval-augmented generation is a technique where the AI first searches your own content for relevant passages, then writes an answer based on them. It makes answers more accurate and lets the AI cite its sources.
RAG or fine-tuning: which do we need?
For answering questions from changing company knowledge, RAG is almost always the better choice: cheaper, easier to update and able to cite sources. Fine-tuning helps with tone or very specialised formats.
Which content sources can you connect?
PDFs, Word and Excel files, Google Drive, SharePoint, Confluence, Notion, Zendesk, websites, and SQL or NoSQL databases. Custom sources can be added through APIs.
How do you stop the AI from making things up?
We restrict answers to retrieved content, require citations, teach the assistant to say when it doesn't know, and measure accuracy on a test set before every release.
Let's talk