Your AI agent is only as 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.
Why chunking and retrieval fall short
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.
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.
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.
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.
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.
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.
One MCP server between agents and all company knowledge
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.
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 with proof
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.
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.
Proof for every answer. The document, the page and the exact words.
Conflicts in plain sight. When two documents put different facts in the same place, your agent sees both, each with its source.
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 to the map
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.
Native connectors for Notion, Slack, Confluence, Gmail and Outlook. Today, export from them and sync the folder; it works the same way.
How it works in three steps
Get a key. Start on the Free plan: 250 pages a month, no card, no time limit.
Connect your agent. One line for Claude Code, Cursor, VS Code, Claude Desktop or any agent that supports MCP.
Connect your data. Folders, cloud storage, Google Drive, SharePoint, your agent's uploads, or your CRM and databases.
Your data stays yours
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.
Never used for training. Your data is never used to train any model.
Deletion is complete. Delete a document and everything taken from it is deleted too.
Or run it yourself. Need it all inside your own network? Limma runs there as well.