AI agents behind the work
AI agents grounded in connected evidence
Specialized AI agents gather, connect, and analyze biomedical evidence behind the scenes. The Epistemic Knowledge Graph keeps the sources and relationships connected for scientific review.
The AI agents operate behind our scientists today. Clients receive finished strategic deliverables—not unfinished AI responses.
Specialized agents work across connected evidence while scientists inspect the sources, assumptions, and conclusions.
More than a general-purpose AI response
General-purpose models are useful for drafting and summarizing. Strategic intelligence also requires a structured evidence base, source traceability, maintained analysis, and accountable scientific review.
General-purpose workflow
- Built around a prompt and response
- Evidence depends on supplied context
- Structure is recreated for each task
- The user evaluates the response
Epistemic AI
- Evidence organized as reusable entities, relationships, and source records
- Analysis grounded in the scoped Epistemic Knowledge Graph
- Provenance carried into analysis and review materials
- Finished competitive landscapes, TPPs, conference intelligence, and related strategic deliverables
- Life-science experts inspect evidence and own conclusions
Data Sources & Provenance
Epistemic brings together the regulatory, clinical, scientific, intellectual-property, company, and market evidence required by the agreed scope. Source provenance is retained so scientists can inspect the record behind the analysis.
Regulatory
FDA, EMA, and global regulatory filings
Clinical
ClinicalTrials.gov and global trial registries
Scientific
PubMed, biomedical journals, and conference abstracts
Intellectual Property
Global patent databases
Commercial
Company filings, press releases, and market data
Conferences
Major medical conference proceedings and abstracts
The Epistemic Knowledge Graph
The Epistemic Knowledge Graph organizes biomedical evidence
around the entities and relationships that matter to a
strategic question. A
knowledge graph
represents assets, targets, indications, trials, companies,
outcomes, publications, and source records as connected
evidence rather than isolated documents.
That structure helps analytical workflows compare like with
like, follow relationships across sources, and reuse relevant
evidence when a subsequent scoped question requires it.
AI agents behind scientists
Specialized AI agents gather and normalize evidence, connect
entities and events, support comparisons, and prepare
structured analysis across the defined scope.
Our life-science experts frame the analytical approach,
inspect sources, challenge gaps and assumptions, interpret the
record, and remain accountable for the finished work.
Clients choose how involved they want to be. The agents
operate behind our scientists rather than as a client-facing
self-service product today.
Traceability for review
Strategic claims can be accompanied by the source records,
evidence registers, assumptions, and gaps needed to understand
how the conclusion was formed. The exact review materials are
defined with the engagement.
Provenance is preserved as evidence moves through analysis and
scientific review, making the record behind the work easier to
inspect and maintain.
From question to strategic deliverable
An engagement begins with the intended decision, scope,
deliverables, level of involvement, and refresh needs.
Epistemic then gathers, connects, and analyzes the required
evidence.
Life-science experts review the record and shape the
conclusions. Clients receive the finished analysis through
dashboards, reports, executive decks, or structured data, with
managed refresh available on an agreed cadence.
Start with the strategic deliverable you need today. The longer-term platform vision is to make the connected evidence behind each answer reusable, so subsequent questions become faster to address and more possibilities become practical to explore.