Epistemic AI

Name: Epistemic AI
Brand: Epistemic AI

The next generation search engine for the life sciences. Explore and discover from hundreds of sources with AI.

Est 2018|Valuation $24M|Raised $11M|10 Ref Customers

LinkedIn 2K|Website|FTEs 29|HQ New York, New York, US

MarketsResearch Firms

01Competitive Landscape

| Aspect | Epistemic AI | Causaly (Life Sciences) | Clarivate Cortellis | |---|---|---|---| | Starting Price | Not public (quote-based; projects + annual subscription) | Not public (demo/enterprise sales) | Not public (enterprise pricing varies by modules) | | Typical Use Cases | Strategy and competitive intelligence deliverables for drug development (TPPs, landscapes, conference intelligence) with emphasis on evidence traceability and expert validation | Scientific search and knowledge-graph-centric reasoning tailored for life sciences and R&D workflows | Broad life sciences intelligence across drugs, trials, and companies; often used by larger organizations needing comprehensive datasets | | Key Strength | Emphasis on generating executive-level strategic deliverables, multi-agent automation, and provenance for evidence-backed outputs | Deep scientific search and knowledge-graph-centric reasoning, with modules for pipeline intelligence | Breadth of content, established enterprise procurement footprint, and wide dataset coverage | | Key Weakness | Limited public third-party reviews; security posture and enterprise features not publicly documented; pricing opacity | Pricing opacity; buyers must validate accuracy/coverage for their niche; may be more R&D-focused than strategy deliverables | Can be expensive/complex; UX and implementation vary by module; AI features may be uneven across suite | - Narrative analysis - Epistemic AI vs Causaly - Both vendors describe evidence-backed outputs using knowledge graphs and automated workflows. - Differentiation may come down to primary workflows: Epistemic AI emphasizes strategic deliverables (TPP/CI/conference packs) with a services-led delivery model, while Causaly emphasizes scientific search and R&D workflows. - Epistemic AI vs Cortellis / Citeline / IQVIA class tools - Traditional platforms tend to compete on dataset scale and coverage breadth, institutional adoption and compliance maturity, and familiarity to large enterprise buyers. - Epistemic AI positions itself as producing synthesized strategic deliverables more quickly than manual processes; buyers should validate such claims through pilots and sample deliverables. - Market share and adoption trends - No reliable public market share data was found for Epistemic AI. - Adoption evidence in official sources is primarily anonymized (for example, references to a large pharmaceutical company), which is directional but not independently verifiable.

01Competitive Landscape

Aspect	Epistemic AI	Causaly (Life Sciences)	Clarivate Cortellis
Starting Price	Not public (quote-based; projects + annual subscription)	Not public (demo/enterprise sales)	Not public (enterprise pricing varies by modules)
Typical Use Cases	Strategy and competitive intelligence deliverables for drug development (TPPs, landscapes, conference intelligence) with emphasis on evidence traceability and expert validation	Scientific search and knowledge-graph-centric reasoning tailored for life sciences and R&D workflows	Broad life sciences intelligence across drugs, trials, and companies; often used by larger organizations needing comprehensive datasets
Key Strength	Emphasis on generating executive-level strategic deliverables, multi-agent automation, and provenance for evidence-backed outputs	Deep scientific search and knowledge-graph-centric reasoning, with modules for pipeline intelligence	Breadth of content, established enterprise procurement footprint, and wide dataset coverage
Key Weakness	Limited public third-party reviews; security posture and enterprise features not publicly documented; pricing opacity	Pricing opacity; buyers must validate accuracy/coverage for their niche; may be more R&D-focused than strategy deliverables	Can be expensive/complex; UX and implementation vary by module; AI features may be uneven across suite

Narrative analysis
- Epistemic AI vs Causaly
  - Both vendors describe evidence-backed outputs using knowledge graphs and automated workflows.
  - Differentiation may come down to primary workflows: Epistemic AI emphasizes strategic deliverables (TPP/CI/conference packs) with a services-led delivery model, while Causaly emphasizes scientific search and R&D workflows.
- Epistemic AI vs Cortellis / Citeline / IQVIA class tools
  - Traditional platforms tend to compete on dataset scale and coverage breadth, institutional adoption and compliance maturity, and familiarity to large enterprise buyers.
  - Epistemic AI positions itself as producing synthesized strategic deliverables more quickly than manual processes; buyers should validate such claims through pilots and sample deliverables.
Market share and adoption trends
- No reliable public market share data was found for Epistemic AI.
- Adoption evidence in official sources is primarily anonymized (for example, references to a large pharmaceutical company), which is directional but not independently verifiable.

Aspect

Epistemic AI

Causaly (Life Sciences)

Clarivate Cortellis

Starting Price

Not public (quote-based; projects + annual subscription)

Not public (demo/enterprise sales)

Not public (enterprise pricing varies by modules)

Typical Use Cases

Strategy and competitive intelligence deliverables for drug development (TPPs, landscapes, conference intelligence) with emphasis on evidence traceability and expert validation

Scientific search and knowledge-graph-centric reasoning tailored for life sciences and R&D workflows

Broad life sciences intelligence across drugs, trials, and companies; often used by larger organizations needing comprehensive datasets

Key Strength

Emphasis on generating executive-level strategic deliverables, multi-agent automation, and provenance for evidence-backed outputs

Deep scientific search and knowledge-graph-centric reasoning, with modules for pipeline intelligence

Breadth of content, established enterprise procurement footprint, and wide dataset coverage

Key Weakness

Limited public third-party reviews; security posture and enterprise features not publicly documented; pricing opacity

Pricing opacity; buyers must validate accuracy/coverage for their niche; may be more R&D-focused than strategy deliverables

Can be expensive/complex; UX and implementation vary by module; AI features may be uneven across suite