The agent records the datasets, protocol version, and consent basis behind a piece of analysis, keeping patient level data inside the controlled environment.
Solutions · Life Sciences & Healthcare
Replaces · Opaque pharma data lakes · Unauditable trial pipelines
Verifiable AI for life sciences.
Patient data sovereign. Clinical evidence verifiable.
Clinical and pharmaceutical decisions demand provenance, yet the underlying data sits in opaque lakes and unauditable trial pipelines. nOS links drug-interaction data, trial evidence, and regulatory submissions with full provenance, keeping patient data sovereign while making clinical evidence independently verifiable. Trace Labs collaborates with partners including BSI and Oxford PharmaGenesis on AI-ready, verifiable medical knowledge.
Decision traces enshrinedDrug interaction findings, full lineage Clinical trial context and amendments Regulatory submissions with reasoning Adverse event triage decisions
What gets captured
Most relevant memory layers
Working, Shared & Verifiable
Working memory captures private agent reasoning. Shared memory is where multi-party context coordinates. Verifiable memory is where decisions are enshrined.
HIPAA
Aligned data flows
EU AI Act
Audit-ready by design
Deployment & partners
BSI · Oxford PharmaGenesis · Healthcare consortia
Real enterprise data, processed in production, with decision traces enshrined as Knowledge Assets in the OriginTrail DKG.
The problem
The case for verifiable decisions in life sciences & healthcare.
Clinical evidence is only as good as its provenance. A submission rests on data that moved through collection, cleaning, analysis, and interpretation, often across a sponsor, a CRO, and several sites. Regulators expect attributable, contemporaneous records, and reviewers ask what a given conclusion was based on.
Introducing AI into that chain raises the bar rather than lowering it. If an agent summarizes safety signals or drafts a submission section, the organization must be able to show which sources informed the output and under what governance, especially as the EU AI Act brings obligations for high risk uses in health.
How it works
How a decision becomes verifiable.
Findings and evidence that collaborators are entitled to see move into shared memory, so sponsor, CRO, and site work from one lineage rather than reconciled extracts.
Interpretation and inclusion decisions record their reasoning, including the criteria applied and any exception approved by a named reviewer.
The trace is enshrined in the DKG so evidence lineage can be verified by a regulator or partner without moving the underlying patient data.
What changes
What this changes in practice.
The change is that evidence lineage becomes continuous rather than reconstructed at submission time. Instead of a team assembling provenance for a dossier under deadline, each analytical decision already carries its basis, which shortens the gap between science and a defensible regulatory narrative.
It also makes AI usable in places where it currently stalls. Teams hesitate to let agents touch regulated evidence because they cannot show how an output was reached. When the agent records its sources and governance as it works, the barrier moves from principle to configuration.
FAQ
Questions about life sciences & healthcare.
Does patient data leave our environment?
No. Working memory and private context never leave your infrastructure. Only the commitments and metadata you choose to publish are recorded, which is what makes lineage verifiable without transferring personal data.
How does this relate to HIPAA and GDPR?
nOS is designed so that personal data stays under your control and disclosure remains your decision. That is the condition both regimes care about; the verifiable layer holds evidence about decisions, not the clinical records themselves.
What does the EU AI Act change for us?
It brings documentation, traceability, and human oversight obligations for higher risk uses. Decision traces address the evidentiary part directly, because the rationale is captured when the decision is made rather than reconstructed for an audit.
Who is Trace Labs working with in this sector?
BSI and Oxford PharmaGenesis are named among the collaborations, alongside healthcare deployments where decision traces are enshrined as Knowledge Assets in the OriginTrail DKG.
Can this support a regulatory submission directly?
It supports the evidence behind one. nOS does not produce regulatory documents; it makes the lineage of the data and decisions inside them verifiable, which is the part reviewers probe.
How does this apply to real world evidence?
Real world evidence draws on heterogeneous sources with uneven provenance. Recording which sources informed a conclusion, and under what governance, is what allows the conclusion to be defended later.
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Power up your business with the OriginTrail DKG.
Create Knowledge Assets, build shared context graphs, enshrine decision traces. Open-source infrastructure. Community support. Zero cost.
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The full Network Operating System.
Custom agent frameworks (Hermes, OpenClaw, LangChain, Claude). Custom data pipelines. Custom integrations with your enterprise systems. Dedicated infrastructure and support.