Peter Gassner built Veeva around life sciences software; its new agent will configure EDC and data-quality systems from study protocols, with early access planned for December.
By [RuntimeWire Staff](https://runtimewire.com/author/runtimewire-staff)
· Published
Primary source: [PR Newswire](https://www.prnewswire.com/news-releases/new-veeva-study-builder-agent-to-configure-clinical-studies-in-as-little-as-one-day-302888436.html)
Why it matters #
Veeva is putting AI inside a specialized clinical-data workflow and tying it to customer standards and its existing EDC product. The one-day claim remains unsubstantiated by customer results, while the December early-access program will test whether speed translates into reviewable, production-ready study configurations.
Veeva Systems (Veeva) says its new Study Builder Agent can configure a clinical study in as little as one day, turning a protocol into forms, visits, edit checks and data-quality listings for its clinical software. Veeva announced the product on September 24th; early-adopter access is planned for December 2026.
Peter Gassner, who co-founded Veeva in 2007, built the company around a specific bet: life sciences needed cloud software designed for its own work and constraints. After studying computer science at Oregon State University, he worked on database technology at IBM, held technical leadership roles at PeopleSoft and joined Salesforce before starting Veeva. The new agent extends that industry-specific approach into a task that clinical data teams have traditionally configured study by study.
The release quoted Drew Garty, Veeva Clinical Data's chief technology officer. Garty spent much of his career at PAREXEL, where he worked on electronic data capture and risk-based monitoring before joining Veeva. His background is in the operational problem Study Builder Agent targets: building and managing the systems that collect and check clinical-trial data.
Standards are the product strategy
Study Builder Agent is designed to work from a clinical-study protocol and configure Veeva EDC, the company's electronic data-capture product, alongside Veeva DQS, its Data Quality System. Veeva says the agent will generate EDC forms, study visits and edit-check rules, create CQL-based listings in DQS, and automate testing, including generating test data.
The company says the agent will reuse each customer's existing standards and use the CDISC Unified Study Definitions Model, or USDM, to make builds more consistent. That puts the emphasis on controlled configuration rather than asking a general-purpose chatbot to draft trial materials. For sponsors with established standards already represented in Veeva's systems, the potential benefit is less repetitive setup and fewer deviations from those standards. That is the product's stated logic; the announcement does not provide comparative results showing how much time or rework it saves in practice.
The headline promise of a study configured in a day also needs a denominator. Veeva's release does not specify what counts as a completed build, how complex the study is, how much human review remains, or how the one-day result compares with a customer's existing process. The figure is a company claim, not a reported customer result.
Veeva plans to deliver the agent as a Claude Cowork plugin, installed from a Veeva-managed GitHub repository. The release says it will be part of Veeva EDC and require no additional license. That packaging ties adoption to Veeva's existing clinical-data platform: the initial value proposition is for organizations already using EDC, rather than a system-independent tool competing to replace their study software.
A faster build still has to be a defensible build
Clinical-study configuration is more consequential than filling in a template. The rules and forms determine what information is collected and which data-quality issues are surfaced during a trial. Veeva says its coordinated, standards-based process is intended to produce audit-ready configurations, but the announcement does not describe the validation controls, review steps or audit-trail details behind that claim. Those details will shape how readily clinical teams can put generated configurations into regulated work.
That is where Veeva's standards-first positioning could matter. Electronic data capture is an established market with products from Medidata, Oracle, IQVIA and others. Veeva's pitch is that the agent works inside its own EDC and DQS products and builds from customer standards, rather than offering protocol-to-document generation as a standalone service. The announcement provides no head-to-head performance data against competing systems, so the differentiation is currently in the workflow and integration Veeva describes.
Veeva reported $3.1953 billion in fiscal 2026 revenue and 1,552 customers, giving it a large installed base across life sciences software. Those company-wide figures do not establish how many customers use the specific EDC and DQS products targeted here, or how many will test the agent. Veeva's release names no early adopters.
Gassner's original industry-cloud thesis made specialized workflows a foundation of Veeva's business. Study Builder Agent applies that thesis to a labor-intensive configuration step and adds AI to the company's existing clinical products. The next proof point will be whether early adopters can get a reliable, reviewable build from a protocol in the promised time, under the standards and oversight their studies require.