Product / Discover

Omic looks for
what follows.

A scientist arrives with a task, not with a dashboard to browse. Discover keeps that directed workflow and injects derived intelligence at the moment of the decision, rather than asking anyone to go looking for it.

Not “we found a related document”

A related document is the reader's problem to interpret. Omic states the conclusion that follows, what is new about it, which sources contributed, what conflicts with it, and which experiment would discriminate between the possibilities. Then it gets out of the way.

What continuous discovery is made of

  • Scientific state changes

    New results, publications and decisions land in Attest with their provenance. That is the trigger surface everything else watches.

  • Derived insights

    Conclusions that follow from combining evidence nobody had combined. Today they come out of an investigation someone started.

  • Autonomous investigation

    Roadmap

    An agent takes a derived insight further without being asked, and reports back with evidence.

  • Cross-project discovery

    Roadmap

    Evidence from one program reaching a decision in another, subject to the permissions on the claims involved.

  • Prioritization

    Roadmap

    Ranking what to look at first by what it would change, not by how recently it arrived.

  • Recommended experiments

    A protocol with a null hypothesis and pass and fail criteria, plus the result that would change the conclusion.

    See the evidence

Three doors, one fabric

Ask in Discover, in Claude, or in ChatGPT — Omic connects as an MCP server. Adoption does not require anyone to move to a new tool. Chat stays useful; it just is not what the product is.

In the workflow

A scientist arrives with a task.
The insight meets them there.

Omic app · Oncology / Compound KInvestigation started
You

Should we take Compound K into IND-enabling studies?

Simulated walkthrough · fictional compound and data
The stack

Everything your company knows,
in one place AI can use.

Your notebooks, assays, papers and threads come in. Every claim keeps the source it came from. AI scientists read it, analyze it and run the experiments — and you reach all of it from the tools you already use.

The evidence layer ↗
  1. 01

    Where you work

    The same answers in every tool you already use.

    via Omic MCP
    • Omic Discover
    • Claude & Claude Code
    • ChatGPT
  2. 02

    Agents

    AI scientists you can build, share and reuse.

    • Literature investigation
    • Data analysis
    • Hypothesis & experiment design
  3. 03

    Compute

    Experiments that actually run.

    • Notebooks & pipelines
    • Compute routing
    • Your discovery models
  4. 04

    Evidence layer

    Every claim knows where it came from.

    AttestDB · open source
    • Claims & frames
    • Provenance on every write
    • Cascade retraction
  5. 05

    Connectors

    Everything your organization already knows.

    • Lab results & experiment logs
    • ELN / LIMS
    • Chats & threads