SpectraDocs: AI Document Intelligence Agent
A precision-focused AI agent that reads, extracts, and standardizes data from unstructured financial documents at enterprise scale.
- Client:: A leading financial services enterprise in India
- Solution Type: AI Agent for Document Intelligence
- Use Case: Large-scale automation of KYC, audit, and regulatory financial documents
Problem
Financial institutions deal with high volumes of complex, unstructured documents every month—KYC forms, AUM reports, bank statements, and tax filings. These documents vary across banks and formats, creating manual workload, compliance risks, and slow turnaround.
Objective
To build an AI Agent that automates this process end-to-end with:
- 90% field-level extraction accuracy
- Audit-readiness and traceability
- Integration into existing enterprise systems
- Minimal manual intervention
AI Agent Architecture
1) OCR & Preprocessing Layer
- Tools Used: Azure Cognitive Services OCR + OpenCV
- Converted scanned PDFs/images into machine-readable text
- Preprocessing enhanced clarity, removed noise, and corrected layouts
2) LLM-Based Field Extraction
- Tools Used: GPT-4 via OpenAI API + LangChain
- Zero-shot and few-shot prompting to extract fields: PAN, IFSC, AUM, income headers, balance line-items
- Multi-turn prompting for ambiguous field names
3) Vector Similarity for Schema Normalization
- Tools Used: pgvector (PostgreSQL) + OpenAI Ada Embeddings
- Tokens embedded using Ada model
- Vector similarity mapped inconsistent labels to standardized schemas (e.g., "Tot Bal" ≈ "Total Balance")
- Adaptive across varying document formats
4) Validation & Confidence Scoring
- Regex logic + statistical thresholding
- Documents with >85% confidence passed automatically
- Others routed to human review
- Output formatted into standardized JSON schema for downstream audit systems
Deployment & Integration
| Category Details | Details |
|---|---|
| Deployment | Python FastAPI microservices + Docker |
| Internal Integrations | SharePoint, SAP, Google Sheets |
| Monitoring Tools | Prometheus + Grafana |
Business Results
| Metric | Result |
|---|---|
| Document Throughput | 50,000+ documents/month |
| Manual Effort | 70% reduction |
| Accuracy | >90% field-level extraction |
| Turnaround Time | From 2–3 days → under 30 minutes |
| Compliance | Seamless audit + regulatory integration |
Document Types Processed
- Individual & corporate KYC forms
- Bank statements (ICICI, HDFC, SBI, HSBC)
- Mutual fund AUM reports
- Balance sheets and income tax filings (scanned + embedded PDFs)
Tech Stack Summary
| Layer | Tools Used |
|---|---|
| OCR & Preprocessing | Azure OCR, OpenCV |
| LLM-Based Extraction | GPT-4 (OpenAI API), LangChain |
| Vector Matching | OpenAI Ada Embeddings, pgvector |
| Validation | Regex logic, Confidence Thresholding |
| Backend | Python FastAPI, Docker |
| Monitoring | Prometheus, Grafana |
| Integrations | SharePoint, SAP, Google Sheets |