EvidenceSync uses agentic AI and a pharma-native knowledge graph to connect your evidence record, uncover what is missing, convert each gap into a source-backed action, and measure value through Return on Evidence.
Agentic AI workflows. Governed evidence knowledge graph. GxP-aligned traceability. Every recommendation tied to source.
Evidence teams do not struggle because they lack activity. They struggle because evidence inputs live across documents, systems, teams, meetings, publications, studies, insights, and external sources. Medical Affairs, HEOR, Regulatory, Publications, and Market Access each hold a piece of the record, and the gaps that matter hide between those silos. Before evidence value can be scored, the work has to be aggregated, structured, connected, and governed.
That is what EvidenceSync does, and the loop at its center is where strategy stops leaking.
Evidence gaps are rarely obvious. They hide between plans, studies, stakeholder feedback, publications, congress activity, regional requirements, internal reviews, and external evidence requirements. EvidenceSync connects those signals, identifies where evidence is missing or weak, and translates each gap into a structured action that can be reviewed, assigned, scored, and tracked.
Identify missing, weak, duplicated, or disconnected evidence across objectives, claims, stakeholders, regions, and dissemination plans.
Convert each gap into a recommended next step with source-backed rationale, owner, timing, dependencies, and review pathway.
Compare recommended actions by strategic value, cost, feasibility, timing, and evidence impact.
Route each action through role-based review and record whether it was accepted, rejected, deferred, completed, or converted into an evidence activity.
Teams across the industry agree evidence creates value. Almost no one can put a comparable number on it. Return on Evidence gives evidence leaders a structured way to compare activities, assets, and portfolios once the underlying evidence record is connected and traceable.
Return on Evidence provides a consistent framework for evaluating evidence investment across activities, assets, and portfolios.
ROE incorporates strategic, scientific, stakeholder, execution, timing, and economic considerations to help teams compare evidence opportunities through a common decision framework.
The underlying scoring methodology, normalization model, weighting framework, and calibration approach are reviewed with qualified evaluation teams.
Explore the ROE framework →Board-level view of where evidence dollars create the most defensible value across the book.
Per-asset return, with competitive positioning treated as a first-class input, not an afterthought.
The unit decision: fund this study, this analysis, this publication, this dissemination activity, or redirect the spend.
The workflow stays constant while the role selector reframes the same governed evidence record by persona, queue, permissions, and decision rights.
Controlled workflows, audit trails, role-based review, source traceability, and claim integrity across the evidence lifecycle.
Sensitive platform actions are governed through role-based permissions and controlled authorization across all workflows.
Source-level traceability so a recommendation, action, or claim can be traced back to the underlying evidence record.
Retrieval bounded to the customer evidence base, with source-backed recommendations and review-aware agent outputs.
See your evidence portfolio through a governed gap-to-action and Return on Evidence lens.