Concordare Raises Seed Funding for Protocol Digitization
Concordare Trials closed an undisclosed seed funding round led by Surface Ventures on August 26 to support its clinical trial protocol digitization platform. FinSMEs reports that Techstars, Innovation Works, Gaingels, and individual investors also participated. The New York company uses AI to extract data from protocol PDFs into a structured Clinical Trial Protocol Model, followed by human review before downstream documents and configurations are generated.
Concordare Trials has closed an undisclosed seed funding round led by Surface Ventures, with participation from Techstars, Innovation Works, Gaingels, and individual investors. The New York company converts static clinical trial protocol PDFs into a structured, interoperable digital format for use in downstream clinical operations systems, according to FinSMEs and Dealroom.
FinSMEs reports that the proceeds are intended for product development, team expansion, and growth of Concordare's site partnership network. The company is led by founder and CEO Zach Sawaged, whose prior work spans clinical research sites, sponsors, and technology vendors, according to Dealroom.
From PDF protocol to structured trial data
Concordare's Concordare Suite ingests protocol PDFs and converts them into its proprietary Clinical Trial Protocol Model, or CTPM. Dealroom describes CTPM as a system-agnostic source of truth for a clinical trial, rather than a document-format conversion layer alone.
The company uses AI specifically for protocol extraction, while human reviewers validate the resulting model before it is used to generate system configurations and study documents, according to FinSMEs. This human-review stage is material in clinical operations, where protocol requirements drive visit schedules, eligibility criteria, procedures, and other data that must remain consistent across study systems.
Concordare claims that manual configuration against long, non-standardized protocols costs an estimated $25 million per study and that its workflow can reduce study activation time by six to eight weeks. Those figures are company estimates reported by Dealroom and Digital Health Funding.
Investors cite broader workflow potential
Surface Ventures co-managing partner Gyan Kapur said, "By digitizing clinical trial protocols at an early point in the process, Concordare has the opportunity to further digitize the entire clinical trial value chain." He added that this could help trials run faster with fewer errors and help treatments reach patients sooner, according to Digital Health Funding.
Digital Health Funding reports that Rutgers University uses the platform across multiple teams and that Concordare is contracting with additional institutions nationwide. The sources do not disclose the round size, valuation, customer count, or technical measures of extraction accuracy.
For clinical data and ML teams, the distinction between extraction and a validated structured model is consequential. Comparable document-intelligence deployments commonly require strong provenance, review workflows, and schema governance because an extracted field is only operationally useful when its source, interpretation, and downstream use can be audited. Concordare's reported human-in-the-loop workflow places the product in that broader pattern of applying AI to unstructured clinical documents while retaining review controls before data reaches operational systems.
Key Points
- 1Concordare raised an undisclosed seed round to convert clinical trial protocol PDFs into structured, interoperable data for downstream study operations.
- 2Its workflow uses AI for extraction and human review before configuration generation, combining document intelligence with validation controls for clinical workflows.
- 3Comparable clinical document automation systems depend on provenance, schema governance, and review workflows before extracted data can support regulated operations.
Scoring Rationale
This is a seed-stage funding event in a practical AI application area, clinical trial operations. The reported human-reviewed protocol extraction workflow is relevant to teams building reliable document-intelligence systems, but the funding amount and independent performance evidence were not disclosed.
Sources
Public references used for this report.
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