Your AI agent is onlyas good as what it finds.

Limma reads your company’s documents and data, builds a structured map of every detail in them, and gives your agent exactly what it asks for: the complete answer, with proof.

The problem

Chunking and retrieval were built for reading, not for answering completely.

Your documents get split into fragments and fetched by similarity. That is a good way to find something worth reading. It is not a way to be sure you found everything, and the gap widens with every source you connect.

~20,000 fragments8 returnedIllustrative of the mechanism, not a measurement. Connect ten times more and the eight does not change.
  1. 01

    Similarity is not a filter

    A fragment that reads like the question is not a record that satisfies it, and nothing downstream can tell the two apart.

  2. 02

    Meaning breaks at the boundary

    A fact stated across two fragments survives in neither. The split is made long before anyone knows what the question will be.

  3. 03

    The slice stays the same size

    Connect ten times more data and your agent still receives a fixed handful of fragments. Coverage falls as you grow.

  4. 04

    Nothing reports the remainder

    No part of the answer says how much was never looked at, so an incomplete result and a complete one arrive looking identical.

  5. 05

    It still has its place

    Retrieval is the right tool for open-ended reading. It is not enough on its own when the answer has to be whole.

The layer

One MCP server between your agents and everything your company knows.

Limma reads every source you connect, once, and holds it as one structure your agent can query. Ask the same question of fifty documents or fifty thousand and the answer is the whole set, with a count, not whatever ranked highest.

~20,000 checked1,240 fit, all returnedThe same set, the same question. Nothing was sampled, and the count is part of the answer.
  • One server, every source

    Documents, spreadsheets, email, cloud folders and your business systems arrive through a single MCP endpoint. Your agent connects once.

  • Scale does not thin the answer

    Adding sources adds to what gets checked, not to what gets missed. The returned set is whatever fits, however large the corpus.

  • Every value keeps its source

    Each one links back to the document, the page and the exact words it came from, so an answer can be checked rather than trusted.

  • It stays current

    Add a source and it joins. Change one and it updates. Delete one and everything it contributed comes out.

Reading

Every detail is lifted out of the sentence it sits in.

This is one paragraph of one supplier record, and the four details Limma takes out of it. The same thing happens to a ticket, a policy, a CV or a spreadsheet row.

acme_msa.pdf · p.84 · clause 12.1

Northwind Ltd operates from Hamburg, Germanyregion and employs 312employees staff across two sites. The current agreement runs to 1 March 2026renews. Following the September audit the supplier was marked approvedstatus for all categories.

  1. Everything, with a count

    Ask which suppliers lapse before March and you get all 12, with the number, not the five fragments that happened to rank highest.

  2. Proof for every answer

    The document, the page and the exact words.

  3. Conflicts in plain sight

    When two documents put different facts in the same place, your agent sees both, each with its source.

  4. Honest about gaps

    If something is not on the map, a document could not be read, or a match cannot be proven, Limma says so instead of guessing.

Connect your data

Everything you connect feeds the same map.

Limma keeps it in sync and sends only what changed, so an unchanged file or record is never processed twice.

  • Files and folders

    Point Limma at a folder and it keeps the map in step with it, including folders Dropbox, OneDrive or Google Drive keep on your computer.

    PDF · scanned PDF · Word · Excel · PowerPoint · email · CSV · JSON · HTML · Markdown

  • Cloud storage

    Read with your own credentials. Limma never stores them.

    Amazon S3 · Azure Blob Storage · Google Cloud Storage

  • Google Drive and SharePoint

    Sync a Drive folder or a SharePoint or OneDrive for Business library directly, read-only.

    Docs, Sheets and Slides arrive as Word, Excel and PowerPoint

  • Databases and business systems

    Each row becomes a record on the map and each column a detail your agent can look up, filter and count. Re-sync and only changed rows update.

    CSV, Excel or JSON exports on Limma's cloud · PostgreSQL, MySQL and SQLite read-only inside your own network

Coming next

Native connectors for Notion, Slack, Confluence, Gmail and Outlook. Today, export from them and sync the folder; it works the same way.

See everything you can connect →

How it works

Three steps, then ask.

  1. 1

    Get a key

    Start on the Free plan: 250 pages a month, no card, no time limit.

  2. 2

    Connect your agent

    One line for Claude Code, Cursor, VS Code, Claude Desktop or any agent that supports MCP.

  3. 3

    Connect your data

    Folders, cloud storage, Google Drive, SharePoint, your agent's uploads, or your CRM and databases.

Your data

Your data stays yours.

  1. Read on our servers, not sent away

    Your documents are read by an AI model running on Limma's own servers, never sent to OpenAI or Anthropic.

  2. Never used for training

    Your data is never used to train any model.

  3. Deletion is complete

    Delete a document and everything taken from it is deleted too.

  4. Or run it yourself

    Need it all inside your own network? Limma runs there as well.