Kief Studio

Knowledge management and semantic search

Automate

Search that understands the question, not just the keyword.

A knowledge system is search over your own documents and data that understands the question and shows where the answer came from. It is for teams whose answers already exist in shared drives, wikis, tickets, and email, but take too long to find. Kief Studio builds retrieval with source citations on top of solid data engineering and hosts it for you. One engagement indexed 121 million records with sub-second search.

What we do

  1. Gather the sources and decide who is allowed to see what.
  2. Clean and index the content so search understands meaning, not just keywords.
  3. Return answers with the source document attached so anyone can check them.
  4. Host it, keep the index current, and respect the access rules you set.

Who it is for

Teams that answer the same internal questions over and over, and businesses with years of documents nobody can search well.

When to look elsewhere

A chatbot that answers without showing its sources. If you cannot check an answer, you cannot trust it.

What we can stand behind

  • 121 million records indexed with sub-second search.
  • No shared hosting. Dedicated infrastructure.
  • Security is a byproduct of good engineering.

Knowledge systems: common questions

  • Is this a keyword search box?
    No. It understands what the question means, finds the relevant passages even when the wording differs, and shows the source so you can check the answer yourself.
  • Will people see documents they should not?
    No. The system follows the access rules you set, so a person only gets answers drawn from documents they are already allowed to read.
  • What scale can it handle?
    One engagement indexed 121 million records with sub-second search. That is the public number we can stand behind, and most business document collections are far smaller.
Knowledge base

Read the long version

  • Knowledge Systems

    Search that understands meaning, not just keywords. Answers grounded in your actual data.

  • Data Engineering

    Making sense of messy, real-world data at scale.

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