Services · Last updated June 2026

Make your scattered knowledge usable

Blash AI is the artificial-intelligence division of Blash Advisory, a London-headquartered corporate finance and advisory firm specialising in retrieval augmented generation and knowledge systems, serving operators, corporates and funds across the UK, EMEA, the Far East and India.

We connect language models to your private documents, policies and data, so your team gets accurate answers with citations rather than hunting through folders.

What we build
  • Ingestion of your documents, policies and data
  • Hybrid retrieval tuned to your content
  • Answers with citations back to the source
  • Permissioning so people see only what they should
  • Evaluation so accuracy is measured, not assumed
Why it matters

A model that cannot cite its source is a liability in a regulated business. We build retrieval that grounds every answer in your documents and shows where it came from, so the output can be trusted and checked.

  • Accurate answers grounded in your own content
  • Citations to the source for every answer
  • Permissions that respect who can see what
How it actually works

We ingest your content, build retrieval that matches how your documents are written, and require the system to cite its sources. Permissions are enforced so a user only retrieves what they are entitled to see, and evals measure answer accuracy against a known set before go-live.

Built to be governed

Source citations make every answer checkable. Permissions are enforced at retrieval, sensitive content is handled to your policy, and accuracy is measured and reportable. This is retrieval a compliance team can stand behind.

Our method

The Blash Grounded-Retrieval Method

  1. Ingest and structure your private content
  2. Tune hybrid retrieval to your documents
  3. Require citations to the source on every answer
  4. Enforce permissions and measure accuracy with evals
Questions about knowledge and rag
What is RAG, in plain terms?

Retrieval augmented generation. The model answers using your documents, fetched at the moment of the question, and cites where each answer came from, rather than relying on what it was trained on.

How do you stop it making things up?

Answers are grounded in retrieved content and must cite the source. Where the documents do not support an answer, the system says so rather than guess, and accuracy is measured with evals.

Can it respect who is allowed to see what?

Yes. Permissions are enforced at retrieval, so a user only ever sees answers drawn from content they are entitled to access.

What content can it use?

Contracts, policies, reports, knowledge bases, manuals and structured data. We tune retrieval to how your specific content is written.

Ready to build this?

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