Leveraging AI to Identify Suitable Candidates for Clinical Trials Based on Patient Data
Business Challenge: A large pharmaceutical company conducting multiple clinical trials faced challenges in identifying suitable candidates from a vast pool…
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Syntes AI unifies clinical records, omics data, lab systems, trial platforms, and regulatory workflows so teams and agents can reason and act with full scientific context.
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Pharma teams have more data than ever—but decisions still rely on manual validation and disconnected context.
Data lives across EHRs, trial systems, and documents—but isn’t connected
Teams spend time validating data instead of acting on it
Critical decisions stall because context must be rebuilt each time
Models are improving—but without connected, governed data, results are unreliable.
AI answers lack full context across systems and relationships
Teams double-check outputs, slowing down adoption
Black-box reasoning creates regulatory and operational risk
Syntes connects your systems into a live, structured view that AI and teams can actually trust.
Data, documents, and relationships unified into one operational model
Every answer grounded in real data with full traceability
Context is assembled before reasoning—not reconstructed after
Insights only matter if they lead to execution. Most systems stop short.
Detect issues across trials, patients, and operations in real time
Trigger workflows and actions across systems
Govern every step with approvals, policies, and audit trails
Unify clinical phenotypes, genomic variants, lab results, and trial data into one contextual graph for advanced AI analysis.
Agents traverse molecular, patient, and historical trial relationships to identify patterns and surface statistically relevant connections.
Every insight ties back to source datasets, pipeline versions, documents, and relationships, supporting publication and regulatory review.
Connect enrollment, safety signals, site data, protocol deviations, and lab results into a unified operational state.
Agents simulate impact and enforce policy before modifying data, triggering workflows, or generating regulatory artifacts.
Structured, semi-structured, and unstructured research data coexist in a unified knowledge and context graph.
Agents reason over real relationships—gene ↔ pathway ↔ phenotype ↔ compound ↔ outcome—not isolated records.
Full lineage, version history, and traceable reasoning support FDA submissions and audit processes.
Two-way connectors augment existing data lakes, warehouses, and bioinformatics pipelines without disruption.
Business Challenge: A large pharmaceutical company conducting multiple clinical trials faced challenges in identifying suitable candidates from a vast pool…
Business Challenge: A pharmaceutical company faced significant challenges in accelerating its drug discovery and development processes. Traditional methods were time-consuming…
The Challenge Prior authorization and referral workflows in healthcare are complex because they rely on data spread across multiple systems.…
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