Concordare Details Funding for Digital Trial Protocols

Clinical research has spent years digitizing the systems around a study while leaving the protocol itself as a document that each system and institution must reinterpret. Concordare Trials is financing a different control point: turn the protocol into structured data once, require human validation, and let the downstream operation build from the same model.

The New York company disclosed a funding round on August 25, 2026. Surface Ventures led the financing, with Techstars, Innovation Works, Gaingels, and individual investors participating. Concordare did not disclose the amount, valuation, or formal round label.

The capital is intended for product development, team expansion, and growth of Concordare's clinical-research site network. The broader implication is less about adding another AI feature to the trial stack and more about whether sites and sponsors can stop rebuilding the same study logic across every document, database, schedule, and workflow.

What Concordare Announced

The August financing backs a company founded by Zach Sawaged, Concordare's CEO and a clinical-research operator who spent roughly a decade across sites, sponsors, and technology vendors. In an interview with NYU Stern's Berkley Center for Entrepreneurship, Sawaged said the idea grew from four years at NYU Langone's Perlmutter Cancer Center, where teams repeatedly rebuilt protocol logic into calendars, checklists, and operational documents.

Concordare's own financing chronology requires care. The company announced an undisclosed pre-seed close in February 2026, while the August release describes a funding round without explaining whether it is separate financing, an extension, or a fuller disclosure of the earlier close. No reliable primary source supports assigning an amount, a Seed label, or a cumulative funding total to the current announcement.

The current Concordare team includes founding engineers Nathan Maher and Swapnil Shah, founding account executive Samantha Rapp, head of product Dave Galinski, clinical operations specialist Aysha Salter-Volz, and product and clinical strategist Alex Butler, MD. Concordare does not list a CTO, so the leadership record should not promote an engineer into a title the company has not published.

How the Clinical Trial Protocol Model Works

Concordare's Apta engine ingests a protocol PDF and produces a draft Clinical Trial Protocol Model, or CTPM. The company describes the CTPM as a typed, versioned schema containing visit schedules, conditional branches, assessment matrices, endpoints, adverse-event rules, and other trial logic, with fields aligned to CDISC standards where applicable.

The CTPM workflow keeps people in the approval loop. A study team reviews the extracted model, confirms items such as visit windows and ambiguous criteria, and signs off before Concordare generates downstream artifacts. That distinction matters in a regulated workflow because a plausible but incorrect field can travel farther than an obvious blank.

Once validated, the CTPM can generate protocol-aligned source documents, EDC structures, CTMS configurations, billing grids, visit calendars, milestone trackers, and recruitment funnels. Concordare's thesis is that an amendment can update a shared structured model rather than forcing each team to begin another manual translation exercise from the new PDF.

Why the Protocol Layer Matters

Most clinical software categories address a particular stage or system: data capture, trial management, patient recruitment, payments, or site operations. The protocol sits upstream of all of them, carrying the rules that determine what a study does, when it does it, and what evidence it collects.

That makes protocol interpretation an infrastructure problem hiding inside document work. A sponsor may define the study once, but sites, vendors, and software teams can each recreate the logic in their own format. Every handoff consumes time and creates another place for requirements to drift, particularly when an amendment changes a visit, threshold, procedure, or dependency.

Concordare is trying to sell consistency without demanding that institutions replace every system they already use. The commercial appeal is clear: extract once, validate once, and generate several operational outputs. The commercial difficulty is just as clear because hospitals, sponsors, and research sites must trust the model enough to let it shape high-stakes work while preserving their own controls.

What Concordare Has Proved So Far

Concordare says Rutgers University uses the platform across multiple teams and that the company is contracting with additional institutions nationwide. The company also says the workflow can shorten site activation by 6-8 weeks. Those claims are meaningful signals of adoption and value, but they remain company-reported rather than independently established results across a large customer base.

In July, Concordare published details from what it called a third-party structured quality assessment of Apta on one real protocol. The strict raw results covered 3,171 scored elements: 3,159 were correct, 12 were present but wrong, and 82 were missing, producing 99.62% precision, 97.47% recall, and a 98.53% F1 score.

Publishing the denominator and error classes makes the claim more useful than a standalone accuracy percentage. The public note does not identify the evaluator or establish performance across a broad library of therapeutic areas and protocol structures, however, so the disclosed result is a serious early proof point rather than a finished validation record.

The Investor Logic and the Operating Obligation

Gyan Kapur, co-managing partner at Surface Ventures, framed the investment around digitizing the protocol early enough to create value for both sponsors and sites. That logic puts Concordare in an attractive position if its model can feed several downstream systems, because value could compound across documents and configurations rather than stop inside one application.

The new capital gives Concordare room to deepen the product, hire around a demanding workflow, and expand institutional partnerships. It also raises the standard of proof. Buyers will want accuracy across more protocol types, transparent human review, amendment traceability, integration reliability, enterprise security, and evidence that shorter setup does not shift risk into a less visible part of the operation.

The financing amount may be undisclosed, but the obligation is specific. Concordare must make one digital representation survive different institutions, systems, security reviews, and clinical edge cases without becoming a shared source of error.

What This Signals for Clinical Trial Software

The most durable AI products in regulated industries may be the ones that make an existing source of truth more usable instead of asking customers to trust a new layer of generated advice. Concordare applies AI narrowly to extraction, keeps human approval between the model and the operational output, and makes the structured protocol the reusable asset.

That architecture does not remove the need for EDC, CTMS, site, sponsor, or vendor systems. It asks those systems to begin from a common interpretation of the study. The protocol has always governed the trial; Concordare's next stretch of work will show whether the handoffs around it can finally operate from the same record.