Problem

Predoc addresses the fragmented, manual process of assembling complete patient histories. Healthcare organizations often rely on staff to chase records through fax, phone, email, health information exchanges, and direct provider requests, then interpret and reconcile PDFs, scans, imaging, lab results, pathology, and other unstructured documents. The result is expensive administrative work, incomplete charts, delayed appointments and treatment decisions, and sometimes unnecessary repeat testing. In The Oncology Institute’s case, each new patient required about 40 minutes of preparation and up to five days to retrieve records, while 65 full-time employees were dedicated to medical-records work.

The clearest “killer” use case is specialty-care intake, particularly oncology referrals, where clinicians need prior imaging, pathology, labs, molecular reports, and treatment history before making a safe plan. Predoc’s oncology case study reports that retrieval time fell from five days to 18 hours, preparation time from 40 minutes to under 10 minutes, and record completeness before appointments reached more than 95%; the organization estimates roughly $2 million in annual savings.

Product / Service

Predoc is an AI-native health information management platform delivered partly as a fully managed service. It retrieves records from connected digital sources such as HIEs, EHR and imaging networks, pharmacies, and other data providers, while also handling direct requests to non-connected facilities and fax- or PDF-based workflows. Its AI and machine-learning pipeline extracts clinical information using OCR and clinical parsing, maps concepts to standard vocabularies, removes duplicates, and organizes the results into a curated, longitudinal patient record.

The output is designed to fit into existing operations rather than replace the customer’s EHR. Predoc can deliver complete records through APIs, parquet files, or integrated EHR and operational workflows, producing queryable, normalized data for care delivery, analytics, AI applications, and clinical research. For research organizations, the same infrastructure is used to retrieve full histories and accelerate trial screening and enrollment; for providers, it reduces chart-chasing work and puts usable records in front of clinicians before appointments.

Market

Predoc competes in health information management, medical-record retrieval, clinical data curation, and healthcare interoperability—specifically the B2B infrastructure layer between fragmented provider data and the clinical or research workflows that need it. The company positions itself against basic HIEs, digital-only point solutions, and retrieval-only vendors by combining broad record acquisition with AI parsing, normalization, curation, and workflow delivery. Possible alternatives identified in company-directory coverage include Health Gorilla, Datavant, and 1upHealth, while Reveleer and Wisedocs are adjacent competitors in automated medical-record retrieval, review, and clinical-data abstraction, although their primary customer segments and product scopes differ.

Predoc is clearly post-revenue rather than pre-revenue. Fortune reported that the company generated approximately $500,000 in revenue within six months of starting, had about 35 customers by September 2025, and in many cases replaced customers’ internal records teams with an outsourced service. It also raised $30 million in combined seed and Series A funding in 2025, led by Base10 Partners, with participation from Northzone, ENIAC Ventures, ERA, and healthcare-focused angels. Customer-reported and company-reported evidence includes the oncology network’s $2 million annual savings and claims of substantially faster retrieval and chart review, but Predoc has not publicly disclosed a full revenue run rate or comprehensive customer list.