Faster, better-informed decisions
The next decision starts from what the organisation already learned, found in seconds instead of reconstructed over days.
Knowledge retrieval and search
Knowledge retrieval combines semantic search with retrieval-augmented generation (RAG) so your organisation can find what it already knows, in seconds, with sources attached.
What is retrieval-augmented generation?
Retrieval-augmented generation (RAG) is a technique that pairs a search step with a language model: the system first retrieves the most relevant passages from your documents, archives, audio or structured data, then generates an answer based only on what it retrieved. Semantic search makes the retrieval work on meaning rather than keywords, so 'notice period' also finds 'termination terms'.
The value is verifiability. Every answer points back to the passages it was built from, which turns scattered information (old reports, meeting recordings, wikis nobody maintains) into a knowledge base you can trust.
We build retrieval systems on your real data, measured on your real questions, so the answer to 'do we know this already?' takes seconds instead of hours.
What is AI knowledge retrieval used for?
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Document archives
Years of reports, contracts and correspondence made searchable by meaning rather than filename.
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Audio and video
Meetings, podcasts and recordings transcribed and indexed, so a spoken decision is as findable as a written one.
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Structured data
Questions in plain language answered from your databases and systems, with the underlying records attached.
business value
Why should I use AI knowledge retrieval?
Shorter onboarding time
New colleagues become productive in days because the archive answers their questions with sources attached.
Reduced compliance effort
Audit questions get answered with the source passages attached, not with a week of searching.
More value from existing data
Reports, recordings and archives you already paid for stop being write-only and start doing work.
faq
Frequently asked questions
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Does RAG work with our on-premise or private data?
Yes. Retrieval runs against your own storage, and the language model only sees the passages retrieved for each question. Data stays where it is, and nothing is used to train public models.
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What kinds of sources can be indexed?
Documents, wikis, email archives, meeting recordings and structured databases. Audio and video are transcribed first, so spoken content becomes as searchable as written content.
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How accurate are the answers?
Every answer cites the passages it came from, so accuracy is checkable rather than taken on trust. We measure retrieval quality on your real questions before launch and keep monitoring it after.
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How is this different from the search we already have?
Keyword search finds documents that contain your words. Semantic retrieval finds passages that mean what you asked, and RAG composes an answer from them, with sources attached.
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