Solutions/For AI builders/Legible Context

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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AGENTSREASONING LAYERPRIMARY DATA

Make your science legible to any agent

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.

BioBoxGraph Explorer/Primary-data substrate4,318 nodes · 11,902 edgesdownregulatesinhibitsusing the drugfound onparticipates inis marker foracts onfound onMTORRCC Tumor vs NormalApitolisibNCT01437566ClinVar:1800602PI3K-AKTrenal cell carcinomaPerhexilineClinVar:2504243PRIMARY DATA
01Foundation

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.

claude — mcp-planning/biobox-mcp
biobox-mcp
Human
Thinking…
Answer
ERBB4 is the #1 ALS target out of 44,750 genes
#1Rank / 44,750
1.327Score
11Evidence lines
4Categories
Genetic AssociationAnimal ModelscRNA × 5 cell typesBulk RNAseq × 2
Beats SOD1 (#2, 1.274) · FIG4 (#3) · TARDBP (#4) · OPTN (#5) · signal is ALS-specific (rank 22,122 in RCC)
02Provenance

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.

AGENTSREASONING LAYERPRIMARY DATABioBoxReasoning LayerPOST /context/queryrequests8.3 qpsp50 latency41 msgrounding100%Target-ID Agentinternal AI agentqps 3.2connectedSafety Agentinternal AI agentqps 1.1connectedPortfolio Agentinternal AI agentqps 0.7connectedPartner · Astra-Rxpartner AI agentqps 2.4connectedPartner · Gx-Biopartner AI agentqps 0.9connectedGrounded Substrategraph · 4.2M nodesevery query → grounded in primary data
03Interface

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.

NEW EVIDENCEREASONING LAYERDECISIONTrial ResultsnewAssay ReadoutData ReleaseReasoning Layerre-groundingLive rankingTarget ATarget BTarget Cdecision · current
04Freshness

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

GeneDisease
+0.86Net-supported
−1 · contradicted0+1 · supported

Lines of evidence

Genetic association
Differential expression
Pathway centrality
Tissue specificity

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

Agent queryGROUNDDisease contextENRICHMechanismREFINESafety signalGrounded answer

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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