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It begins by defining your scientific worldview: the Ontology.

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Connecting multi-modal data across teams.

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Transforming expert scientific reasoning into models that think like you.

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Powering humans and AI systems to make better decisions in drug discovery.

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Reasoning lens
DecisionOS · Decision ReportREF · BX-7F2A Live
Programs
24
active
Decisions
1,840
logged
Hypotheses / wk
12×
throughput
Provenance
100%
traceable
Reasoning throughput+38%
Evidence by modality
scRNA
Bulk
Prot
Epig
Gen
Target ranking · UC
TYK2
0.92
JAK1
0.81
STAT3
0.74
IL23R
0.66
Decision confidence
87%
4 frameworks · fully traceable

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.

Across the discovery lifecycle

One reasoning layer, every portfolio decision

BioBox is built for R&D teams de-risking critical discovery decisions, and for the leaders who need clear visibility into the data and reasoning behind them, now legible to every team and the agents they deploy. From the first target to the final in-licensing call.

Early stage

  • Target evaluation
  • Indication selection
  • Disease-relevant pathway analysis
  • Biomarker identification

Late stage

  • Target Product Profile assembly
  • Internal asset prioritization
  • In-licensing competitive analysis

Solutions

Put BioBox to work on a specific decision

See the reasoning layer applied to the calls your teams make every day, each with the data foundation, scoring, and evidence trail behind it.

All solutions

FAQ

Questions, answered

The essentials on how BioBox works and what it takes to get started.

Still have questions? Book a briefing
What exactly is BioBox?
BioBox is reasoning infrastructure for pharma: the medium where your R&D stores its differentiated knowledge and makes it legible to AI. It unifies your scientific data and decision logic into one system, so faster, explainable, and repeatable decisions get made across R&D by your teams and the agents they deploy.
Is BioBox an agent or a foundation model?
No, and that's the point. We don't build or sell the agent or the model. BioBox is the infrastructure beneath them: the medium where your differentiated knowledge lives and becomes legible, so you can point the best internal or external AI agent at the work and have it reason on your frameworks and data.
What data does BioBox work with?
Processed, multi-modal scientific data: multi-omics, assay data, ELNs, literature, and internal knowledge bases, unified into one governed foundation through your ontology, so every layer reasons over the same source of truth.
What is decision provenance?
Every conclusion traces back to the evidence and reasoning that produced it. The reasoning layer plus decision tracing make each decision explainable and repeatable, a defensible trail that holds up in any room.
How do teams onboard?
In three layers: connect your multi-modal data foundation, encode your reasoning, and work in decision-ready interfaces. From there, every decision compounds into institutional memory that makes the next one faster and better grounded.
Is our data secure?
BioBox is built enterprise-grade for the most data-sensitive organizations in drug discovery. See our Trust Center for security, privacy, and compliance details.

See BioBox on your hardest decision

A working session with our team, mapped to one of your active discovery programs, from ontology to decision provenance.

Book a briefing