DMI Document AI

Government agencies manage rising volumes of unstructured documents: case files, regulatory submissions, applications, and supporting records. Legacy systems and manual workflows extend timelines and limit responsiveness. DMI Document AI combines large language models, workflow automation, and human-in-the-loop validation to convert those documents into searchable, structured, actionable data, so teams reach decisions faster with higher accuracy. The platform runs in three modules. DocuChew handles OCR-free ingestion and extraction across text, tables, and images, including handwritten and degraded records. DocuRun applies blueprint-driven workflows in natural language, with confidence-based human review and continuous learning from corrections. DocuGen uses patent-pending weighted retrieval to generate long-form, source-aware outputs such as reports, SOPs, summaries, and compliance documentation. DMI Document AI is built on FedRAMP-aligned AWS services and deploys directly into the customer AWS environment. Data stays inside the customer boundary, with no shared tenancy, no external model training, and no black-box processing. Encryption at rest and in transit, auditability, PII protection, and controlled human review support audit readiness. Reported results from early implementations: up to 50 percent faster cycle times, more than 90 percent data extraction accuracy, and 200-plus page documents generated in about 45 minutes. DMI modernized the U.S. Army Human Resources Command personnel records system serving over 9 million records, with reported outcomes including 3.5 million dollars in reduced costs and a 425 percent increase in annual releases. Use cases: claims and case processing, records management modernization, contract and grant processing, knowledge-based document generation, and document intake and routing