Expose your primary data and encoded reasoning as a grounded reasoning layer agents query directly, with every answer traceable to the evidence beneath it.
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Grounded in primary data
Agents reason over the underlying results, so answers reflect the evidence rather than a confident guess.
Every claim carries provenance
Each answer traces to the data and reasoning that produced it, ready for scientific and regulatory scrutiny.
One interface, any agent
Internal and partner AI agents draw on the same grounded context, with no bespoke plumbing per model.
Built on the data beneath the literature
BioBox grounds context in a knowledge graph of primary results, so agents draw on evidence your team can open up and trust, not abstracts and hearsay.
Answers that show their work
Every claim an agent returns carries its evidence trail, so a reviewer can follow any conclusion back to the result that supports it.
One reasoning layer, any agent
Your own and your partners' AI agents query the same grounded context through a single clean interface, instead of re-plumbing for every model.
Current as the science moves
As new results land around the world, the context updates, so your agents never reason on ground that has already shifted.
Built on the data beneath the literature
BioBox grounds context in a knowledge graph of primary results, so agents draw on evidence your team can open up and trust, not abstracts and hearsay.
Answers that show their work
Every claim an agent returns carries its evidence trail, so a reviewer can follow any conclusion back to the result that supports it.
One reasoning layer, any agent
Your own and your partners' AI agents query the same grounded context through a single clean interface, instead of re-plumbing for every model.
Current as the science moves
As new results land around the world, the context updates, so your agents never reason on ground that has already shifted.
Programmable scientific reasoning
Score associations from a perspective you define.
BioBox turns how your experts weigh evidence into a quantifiable, traceable score of association, grounded in your own ontology and data.
Grounded in your ontology and data
Reasoning runs over a custom ontology and the real-world evidence already in your graph: your entities, your relationships, your results, never a foundation model's generic priors.
A scientific perspective, encoded
Experts define which lines of evidence count and how much they weigh. The same perspective is applied the same way to every question, and can be revised without unpicking the rest.
A quantified, defensible score
Every module returns one signed score of association that decomposes back into the evidence behind it. Defensible to a scientist, not just plausible to a reader.
Association score
Target Prioritization
Lines of evidence
Every score decomposes into the evidence, supporting and contradicting, that produced it.
Powering Agentic Science
Your knowledge stays sovereign, and unmistakably yours.
Your scientists' judgment lives in reasoning modules grounded in your own ontology and data, and any AI co-scientist reasons against those instead of raw context. So your differentiated knowledge never dissolves into someone else's model. It stays sovereign, governed, and yours to compound.
The model navigates. The science adjudicates.
Ontology-licensed path
An agent composes reasoning modules along a path the ontology licenses. Each one adds a layer of grounded evidence, so the answer is built up, never guessed.
Modules, not raw context
Each reasoning module anchors two concepts in your ontology, carries rich descriptions of what it means and when it applies, and traces to the curated evidence that justifies it. It captures not just what something is, but why it reads that way.
Agents do what they are good at
The agent reads intent and decides which modules to activate and in what order. Adjudication stays inside the modules, so swapping models, or rerunning the same one, returns the same grounded answer.
Composed for the question
Modules chain along paths the ontology licenses. Broad questions recruit complementary modules from related domains; a safety read narrows through tightly scoped, reinforcing ones. The composition adapts while the grounding stays fixed.
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See BioBox on your hardest decision
A working session with our team, mapped to one of your active discovery programs.
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