Dossier

Dossier is built as a desktop-first workspace for document operations teams in finance, healthcare, legal/compliance, and enterprise operations. Instead of stopping at OCR text, the product understands document structure, extracts schema-aligned fields, validates business logic, detects review-worthy risk signals, preserves provenance, and keeps humans in control before export. Dossier is currently being tested with 5+ businesses to validate real-world workflows, manual review reduction, risk triage quality, and readiness for commercial pilots and funding.

Organizations that process sensitive documents are often stuck between manual data entry and black-box OCR or AI services. Traditional OCR can read characters, but it does not fully handle layout, tables, field relationships, confidence, risk signals, or approval workflows. Once AI enters a real operational process, customers also need data control, audit trails, human approval, model cost control, and a clear record of how each result was produced.

Founder / Product Builder / Full-stack Engineer

Dossier uses a local-first architecture with a Tauri/React desktop app, Rust desktop kernel, and Python AI runtime. The product pipeline covers intake, Fast Probe, OCR/layout/table parsing, field extraction, validation, bounded repair loops, risk triage, human review, approval, and export. A provider routing layer can swap or combine OCR, handwriting OCR, layout parsers, table parsers, VLM/LLM providers, and local/cloud models based on privacy policy, latency, cost, confidence, and document type. Agentic OCR in Dossier is governed orchestration with stop policies and auditability, not unbounded autonomous automation.