AI Document Processing

HiMat Technology builds ai document processing as production software—not slide decks. Based in Chennai and working remote-first, we align discovery, implementation, and launch with your existing Next.js, React, and Node.js teams where they exist.

Document AI shines on repetitive formats—invoices, contracts, intake forms—when you measure field-level accuracy and route low-confidence extractions to reviewers.

We combine deterministic parsers with model-assisted extraction, store originals in your bucket policy, and never send full archives to models when a redacted snippet suffices.

We treat customer data as tenant-scoped: production credentials live in your secret stores, access is role-based, and prompts or embeddings are stored in separate collections or namespaces so test traffic never mixes with live user records.

Human review is built into high-impact paths—draft replies, extracted fields, or agent actions can queue for approval before they reach customers or downstream finance systems. We log who approved what and retain those records according to your policy, not an undefined default.

We do not claim third-party certifications on your behalf. Instead we document data flows, retention windows, and subprocessors you approve, then implement technical controls—encryption in transit, least-privilege API keys, and optional PII redaction before text is sent to leading LLM providers.

Engagements start at /connect or /schedule with a short discovery call. We return written scope, stack choices limited to tools we actually operate (including MongoDB, OpenAI API, and Pinecone where retrieval applies), and timelines you can plan around.