Multi-Modal Document Parsing Pipeline
Constructed high-precision optical OCR and structured entity extraction pipelines capable of normalizing handwritten telemetry and engineering diagrams.
Case study · Energy & Utilities
Automating Complex Multi-Party Grid Maintenance Verification with Human-in-the-Loop AI
Architected an automated document verification and compliance orchestration system for transmission substation maintenance logs, reducing audit cycles from 21 days to under 4 hours.
ContextMajor Energy & Power Generation Conglomerate4 Months Engagement
The challenge
What was making the work difficult.
Field engineers generated tens of thousands of handwritten and scanned inspection sheets monthly. Compliance auditing required certified safety engineers to cross-reference each sheet against regulatory standards, creating severe backlogs and delaying critical repairs.
The approach
Confidence-Scored Human-in-the-Loop Routing
High-confidence extractions (>98%) are auto-approved into the regulatory compliance database; borderline or anomalous records are immediately routed to senior auditors with highlighted discrepancies.
Immutable Cryptographic Audit Trail
Every parsing decision, human edit, and timestamped sign-off is logged into an append-only verifiable audit record.
Reported outcomes
Results are described from the engagement context. Client details are anonymized where confidentiality requires it.
88%
Autonomous Processing
Routine maintenance inspections verified and filed with zero manual touchpoints.
21d → 4h
Audit Cycle Turnaround
Cycle time reduced by over 99% while increasing audit compliance coverage.
100%
Regulatory Audit Traceability
Full lineage and cryptographic provenance available on-demand for statutory audits.
Tools used in this engagement
Python · FastAPI · Next.js · TypeScript · PostgreSQL · Tailwind CSS · AWS KMS
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