Intelligent Document Processing
for the Division of Water Resources.
AI-powered completeness review for environmental permit applications. Upload an applicant's submittal and receive a regulator-grade findings report — every required field, every supporting document, every Tennessee rule citation — checked in minutes, not hours.
- Permit types supported
- 0
- Pre-loaded regulatory checks
- 0
- Tennessee rule chapters indexed
- 0+
18 DWR permit programs and forms, end-to-end.
Coverage of every permit and form on the RFI #32701-26-619 nForms list, each shipping with a pre-loaded regulatory checklist derived from Tennessee Rules Chapter 0400-40, Chapter 0400-45, the TDEC Design Criteria, and EPA NPDES guidance.
From application package to findings report in a single workflow.
Submit the application package
Drop one or many documents — NOI forms, SWPPPs, engineering reports, site maps, supporting evidence. PDF and image formats supported.
Identify permit type and documents
Claude classifies the overall permit type and each individual document, so the right regulatory checklist is loaded automatically.
Run completeness against TN regulations
Every required field and supporting document is checked against Tennessee Chapter 0400-40 rules and the relevant permit-specific design criteria.
Deliver findings with rule citations
Reviewer-ready report with pass / warn / fail for every requirement, the precise regulatory citation, and the page reference in the source document.
Purpose-built for regulator-grade rigor.
Not a wrapper around a generic chatbot. The pipeline is engineered around how DWR completeness reviewers actually work — checklist-driven, citation-supported, and conservative about confidence.
Native PDF understanding
Documents are sent directly to Claude with native PDF support — no brittle OCR pipeline, no third-party parsing layer between the source file and the model.
Two-model architecture
Claude Sonnet handles document classification and field extraction at scale; Claude Opus performs the deep regulatory reasoning required for completeness analysis.
Tennessee rules in pgvector
Rule chapters 0400-40 and 0400-45, TDEC Design Criteria, and EPA NPDES guidance are chunked, embedded with Voyage voyage-3-lite, and stored in Neon Postgres with pgvector for retrieval-augmented reasoning.
Structured checklists per permit type
Each of the 8 supported permit types has a hand-curated checklist of CRITICAL / MAJOR / MINOR requirements with regulatory citations, fed to the model alongside the application data.
Cross-document validation
When multiple documents make up one application, the system checks consistency between them — site acreage, outfall coordinates, signatory identity — and surfaces every mismatch.
Real-time streaming pipeline
Findings stream back to the reviewer as they're generated. There is no wait-then-reveal — every step of parse → classify → extract → analyze → report is visible as it happens.
AI automation and intelligent document processing for the public sector.
AutoFlowOps designs and ships production AI systems for government and enterprise clients — with an emphasis on retrieval-augmented architectures, document intelligence, and PostgreSQL-backed data platforms.
Recent portfolio includes AstroPath — a multi-institutional pathology database architected with Johns Hopkins and published in Science — and a series of municipal and state-agency engagements applying the same RAG and IDP patterns to regulatory, procurement, and constituent-services workflows.
Submitted in response to RFI #32701-26-619
This live demonstration accompanies our written RFI response. It is intended for hands-on evaluation by DWR staff. Upload a representative application package on the Analyze page to see the system in action, or jump straight to the Sample Report for a pre-generated example.
Findings reference Tennessee Rules Chapter 0400-40 and Chapter 0400-45, and TDEC Design Criteria where applicable. Regulatory determinations remain with DWR reviewers — this system supports first-pass triage, not final action.