The Evidence Layer
for AI Science.
Turn your organization’s collective knowledge into a discovery engine.
Ask any AI and it answers from the literature. Ask Omic and it answers from everything your company has ever learned.
One platform,
five layers.
Connectors pull in what your organization knows. The evidence layer keeps it as claims with sources. Agents and compute do the science. You reach all of it from the tools you already use.
How it works ↗- 01
Where you work
The same fabric behind every door.
via Omic MCP- Omic Discover
- Claude & Claude Code
- Codex
- Gemini
- ChatGPT
- REST & Python API
- 02
Agents
AI scientists, built and shared.
- Literature investigation
- Data analysis
- Hypothesis & experiment design
- Synthesis & writing
- Agent builder
- Agent library
- 03
Compute
Experiments that actually run.
- Computational builders
- Notebooks & pipelines
- Compute routing
- Your discovery models
- 04
Evidence layer
Claims that know their sources.
AttestDB · open source- Claims & frames
- Provenance on every write
- Cascade retraction
- Corroboration
- Time travel
- 05
Connectors
Everything your organization already knows.
- Lab results & experiment logs
- ELN / LIMS
- Literature
- Clinical trials
- Chats & threads
- Databases & data lakes
Ask where you already work.
Answer from everything you know.
Should we take Compound K into IND-enabling studies?
Same question. Same model.
Your knowledge.
- 01
It read your notebooks, not just the literature.
Team A’s 2022 result was never published. It was in the fabric, so it was in the answer.
- 02
It knew what was never followed up.
Claims, sources and gaps are linked. The missing model B experiment was visible, so it ran it.
- 03
It wrote the result back.
Team B’s next question starts from Claim K, not from zero. That is how your science compounds.
Published,
with the work attached.
Every result Omic reports comes with its sources, its code and what it does not show.
All research ↗