Clinical Trial Intelligence
for Patient Enrolment
Turn fragmented
records Into evidence backed decisions
in minutes
Uses complex clinical documents to build structured patient narratives and assess protocol eligibility, delivering faster, standardised, auditable, and scalable enrolment decisions.
01
Faster Enrolment Decisions
Reduces manual patient review from hours to minutes by automating document processing and eligibility assessment, accelerating enrolment timelines across complex, late-stage clinical studies
02
Protocol-Driven
Eligibility Assessment
Assesses each patient against full protocol inclusion and exclusion criteria using structured clinical data, improving consistency, reducing variability, and lowering ineligible enrolment risk.
03
Complete Traceability
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04
Scalable Trial Operations
Enables sponsors and CROs to process higher patient volumes across multiple studies without proportional increases in manual review effort, oversight, or headcount growth.
ABOUT US
Syntriqa was created from a successful collaboration between Peraison & CG Oncology (NASDAQ: CGON), combining deep data & analytics expertise with specialist clinical AI.
Built on proven technology foundations, Syntriqa enables faster, more consistent & scalable patient enrolment decisions across complex clinical studies.
WHY Syntriqa?
Designed for Complex Clinical Trials
Syntriqa improves the outcomes sponsors and CROs care about most: faster enrolment, stronger cohort quality, and a lower-cost operating model.
01
Speed
Automates document ingestion, patient structuring, and eligibility assessment, moving complex enrolment decisions from hours to minutes.
02
Quality
Standardises profiling and eligibility assessment against full protocol criteria, linking every recommendation to source evidence to reduce variability and strengthen auditability.
03
Cost
Enables leaner teams to manage higher patient volumes by reducing manual review effort, lowering screening costs, and avoiding ineligible enrolment.
The engine behind Syntriqa
Industry-leading data engineering
Robust pipelines ingest and normalise the messiest real-world clinical data, from scanned PDFs, handwriting and tables to lab reports and EDC feeds, into a structured, queryable medical narrative. Every value carries full lineage back to its source, so the data is both reliable and defensible.
Biotech-tuned AI, not off-the-shelf models
Our models are tailored to clinical language, oncology concepts and complex document types. They understand disease-specific detail such as grade, stage, prior lines of therapy and treatment response, preserve clinical meaning during extraction, and map evidence directly to protocol inclusion and exclusion criteria.
Engineered for the pivotal stage
Traceability, auditability and data governance are built in from the ground up, meeting the standards that registrational, late-stage trials require.