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The Matrx Knowledge System

Source → knowledge → answer, one continuous system.

Raw sources flow through a seven-phase pipeline, pass an ingestion gate, and become retained, versioned, traceable knowledge — held in many representations and described by entities, scopes, trust, and lineage. Agents attach anywhere along the way and turn it into cited, accurate answers.

Sources

PDF · scansaudio · videoweb scrapesocialnative filesAPI feeds
ingestion gate

Agent Fabric

Attach an agent at any node or edge of the pipeline. The same agent can take the shape of:

chatbotbuttonformappautomationscheduled jobMCP egress →

Overlays every stage — clean, enrich, retrieve, and answer. The attorney's agent in the worked example runs right here.

Tap any phase to focus it. Phases 1–7 flow top to bottom · the agent fabric overlays every stage.

What the system actually does

Every capability below is a real surface you can open today. Follow the link to see it for yourself.

Bring any source

PDFs, scans, audio, video, web pages, native files. They upload into your file system and get an origin lineage stamp the moment they arrive.

Files

A real extraction pipeline

Extract → clean → segment → embed, run per-stage or as one streamed job. Watch the original PDF, raw text, cleaned markdown, and segments side by side in a 4-pane viewer.

Library

Many representations, one source

The same document is held as text, segments, vectors, summaries, schemas, and indices — each scoped to who's allowed to see it, all tracing back to the original.

Data stores

Entities + a knowledge graph

NER lifts entities and concepts out of your sources and connects them. Explore the graph, click a node, and jump to the exact evidence that put it there.

Knowledge graph

Retrieval you can trust

Semantic + structural + trust-weighted search in a multi-tab Search Lab. Every hit drills down to its provenance — no black-box answers.

Search Lab

Agents on every node

Attach an agent anywhere in the pipeline — as a chatbot, a button, a form, an app, or an automation. The same agent reshapes to the job.

Agents
The ask — what it's all for

One question, resolved end-to-end

Attorney → agent

“Depose Dr. Smith tomorrow. Find where he didn't follow AMA guidelines for John Doe, case #123456, and flag prior injuries (preexisting conditions). Settle the case — hit hard, but get every fact right.”

Verified deposition brief

cited · accurate · traceable

The agent resolves it:

  1. 1Structural filter → case #123456 · Dr. Smith
  2. 2Semantic search → 'AMA deviations' · 'prior injuries'
  3. 3Pull canonical → medical reports (JSON) + AMA guide
  4. 4Verify → trace each fact to its provenance root
  5. 5Apply skill → 'how to depose a doctor'
Try it yourself — a guided run

From a blank file to a pro-level answer

An on-screen wizard walks you through the whole system using the real UI — not a fake demo. You do each step; the system makes something real happen. Tags show what's live today.

LivePartialComing
  1. 1

    Upload a PDF and a CSV

    Partial

    Drop both into your files. The PDF feeds the knowledge pipeline; CSV-as-knowledge ingest is still being built.

  2. 2

    Run extraction

    Live

    Send the PDF through extract → clean → segment → embed and watch it in the 4-pane viewer.

  3. 3

    Build a specialist agent

    Live

    Create a custom agent that pulls a specific piece of data out of the document.

  4. 4

    Run, inspect, fix, re-test

    Live

    Run the agent, see the result, edit the agent to fix issues, run again, finalize.

  5. 5

    Agent battle

    Live

    Race several models side by side — fast + cheap vs. slow + expensive + better — in the comparison arena.

  6. 6

    Automate the extraction

    Live

    Uploaded files auto-schedule for RAG (≈5 min after arrival). Toggle “Process for RAG immediately” in the New menu to skip the wait, or trigger/refresh any file on demand.

  7. 7

    Upload again, watch it run

    Live

    Drop a new document and watch its RAG lifecycle move scheduled → running → indexed right on the file — no manual step required.

  8. 8

    Chat, then save a Note

    Live

    Ask a chat agent a question, then save the answer straight into Notes.

  9. 9

    Edit the note

    Live

    Clean it up and make it better in the notes editor.

  10. 10

    Trigger RAG / NER

    Live

    Process the note for RAG, then hit “Run NER now” right in the note toolbar — entities lift out on demand instead of waiting for the backend batch.

  11. 11

    Visualize the graph

    Live

    Open the knowledge graph and see entities and relationships light up.

  12. 12

    Manual search

    Live

    Run a manual retrieval to confirm you can pull good data back out.

  13. 13

    Build your badass agent

    Live

    Create a top-tier agent wired to your knowledge base.

  14. 14

    Ask the hard question

    Live

    Pose a deep, industry-knowledge question and watch the agent hit your RAG, reason, and answer like a pro — cited and traceable.

The guided wizard that drives you from page to page is on the roadmap. The capabilities it strings together are already real — this page links straight to each one.

See the whole system in motion

Start with a single document and follow it from source to a cited answer.

Open the LibraryRun an Agent Battle
Save anything you find straight to Notes.
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