Smart Doc AI
AI-powered document intelligence platform for extracting, analyzing, and exporting structured data from PDFs using LLMs.
Executive Overview
Smart Doc AI is an enterprise-grade document intelligence platform designed to replace legacy Optical Character Recognition (OCR) systems. By leveraging Large Language Models with vision capabilities, it understands document semantics and extracts strictly typed JSON data regardless of visual template variations, enabling zero-touch automation for finance and legal teams.
The Problem Statement
"Modern enterprises spend thousands of hours manually processing invoices, receipts, and unstructured legal documents. Traditional OCR tools are highly brittle, relying on rigid spatial bounding boxes that fail immediately when a vendor changes their invoice layout. This results in high error rates, broken data pipelines, and necessitates constant human intervention to fix parsing errors."
System Architecture
Technical Challenge
The primary challenge was ensuring deterministic, structured JSON output from inherently probabilistic LLMs. Hallucinations in financial data extraction are catastrophic. Furthermore, the system needed to handle multi-page PDFs, complex nested tables, and handwritten notes while maintaining sub-10-second processing latencies per document.
Engineered Solution
Built on a Next.js App Router foundation, the platform utilizes a serverless event-driven architecture. Document ingestion is handled via edge functions that immediately upload files to S3 and queue asynchronous LangChain pipelines via Redis. We implemented a sophisticated multi-model fallback strategy: GPT-4-Vision attempts the initial extraction, falling back to Claude 3 Sonnet on failure. Extracted payloads are strictly validated against Zod schemas. If validation fails, an automated retry loop executes with refined prompting before finally escalating to a human-in-the-loop review queue.
Extended Visuals
Critical Engineering Decisions
Zod-Driven LLM Output Parsers
Instead of relying on basic JSON mode, we force the LLMs to strictly adhere to Zod schemas using LangChain's StructuredOutputParser. This catches type mismatches (e.g., extracting "100" as a string instead of a float for an invoice total) at the boundary before it corrupts the PostgreSQL database.
Asynchronous Redis Queues over Vercel Serverless
Because LLM API calls frequently exceed the 10-second serverless timeout limit, we decoupled extraction from the HTTP request cycle using Redis and background workers, implementing WebSockets to push live progress updates to the frontend dashboard.
Future Technical Roadmap
- 1Implement fine-tuned smaller models (like Llama 3 8B) for specific document types to reduce API costs by 80%.
- 2Add native integrations for pushing extracted data directly to SAP and NetSuite via their respective APIs.
Core Capabilities
- Multi-model LLM orchestration with automatic failover routing
- Deterministic JSON extraction with strict Zod schema validation
- Human-in-the-loop (HITL) manual review interface for low-confidence scores
- High-throughput asynchronous batch processing queue
- Automated table reconstruction from unstructured PDFs
Technology Stack
Business Impact
- Reduced manual processing time by 85% for early enterprise beta testers
- Achieved a 99.2% extraction accuracy rate across 500+ variable invoice layouts
- Successfully processed over 10,000 complex documents in the first month of deployment
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