Teams run into scalability issues when they make AI ingest directly from raw CCDA and FHIR exports,” said Xiang Li, PhD, Chief Technology Officer of SEQSTER. “The clinically meaningful signal is often a small fraction of the total record. Our data refinery distills that complexity into structured longitudinal data so AI can reason over the full record, rather than spend tokens parsing noise and hit context window limits.
Enterprise-ready harmonization engine delivers AI-ready clinical data for faster cohort discovery, trial screening, feasibility analysis, and ongoing study execution.
SAN DIEGO — February 19, 2026 — SEQSTER PDM, Inc. (“SEQSTER”), the leading healthcare technology company and the data connection, collection, and orchestration layer for patient health data, today announced the launch of 1-Click Data Refinery™, an enterprise-grade data harmonization engine designed for pharmaceutical companies, contract research organizations, and healthcare enterprises. The solution transforms raw, patient-consented EHR data into clean, structured, AI-ready patient records that support rapid model training, real-time inference, and production-scale deployment.
Life sciences companies are investing heavily in AI to speed up trials and improve decisions, but many efforts fail to scale because the real constraint is not the model, it is the data. Clinical data from EHR systems, even in FHIR or CCDA formats, is built for record exchange, not analysis. It is often bloated, repetitive, and inconsistent across vendors, with the meaningful clinical insight buried inside technical markup. Without converting that raw data into clean, consistent, high-signal patient datasets, AI programs become costly, slow to deploy, and difficult to scale across large populations.
SEQSTER’s 1-Click Data Refinery™ addresses this challenge by refining and orchestrating clinical data at the source. The platform normalizes and deduplicates raw EHR records across health systems and clinical notes, producing longitudinal, patient-centric data representations that AI systems can immediately consume. This enables organizations to deploy AI faster, reduce data engineering overhead, and improve confidence in AI-driven outputs used in regulated clinical environments.
As AI becomes ubiquitous, advantage no longer comes from having better models—it comes from whether your systems are grounded in reality. In healthcare, that reality lives in messy clinical data that was never designed for machines to reason over. Interoperability makes data movable, not meaningful. SEQSTER is working at exactly that fault line: turning lived clinical experience into data AI systems can actually understand. This kind of infrastructure is a prerequisite for moving from pilots to real production and impact,” said Sean White, Ph.D., CEO of Inflection AI.
The unique product offering is SEQSTER’s data readiness at scale, built on more than 10 years of production experience refining real-world CCDA and FHIR across diverse EHR environments. 1-Click Data Refinery converts patient-consented records into harmonized longitudinal patient representations, with the structure and provenance needed for reliable retrieval and inference.
“Teams run into scalability issues when they make AI ingest directly from raw CCDA and FHIR exports,” said Xiang Li, PhD, Chief Technology Officer of SEQSTER. “The clinically meaningful signal is often a small fraction of the total record. Our data refinery distills that complexity into structured longitudinal data so AI can reason over the full record, rather than spend tokens parsing noise and hit context window limits.”
