Production-Grade AI Fraud Intelligence & Document Verification Engine
Built for multi-source data extraction, mathematical identity validation, OCR processing, and high-throughput background processing.
TrustScan is deployed live in production at trustscanai.in.
According to Google Search Console telemetry:
- 🌐 Google Search Impressions:
8,260+- 🖱️ Organic Clicks:
617- 📈 Click-Through Rate (CTR):
7.5%(High organic engagement)- 🎯 Average Google Search Rank:
Position #5.1(Page 1 Google Ranking)
TrustScan processes incoming document uploads (PDFs, images), text payloads, and external security feeds through an automated Extract - Transform - Load (ETL) pipeline:
┌────────────────────────────────┐ ┌────────────────────────────────┐ ┌────────────────────────────────┐
│ 1. EXTRACT STAGE │ │ 2. TRANSFORM STAGE │ │ 3. LOAD STAGE │
│ Multi-Source Payload Ingestion │ ──► │ Data Cleaning & Validation │ ──► │ Dual-Database Storage & │
│ (PDFs, Images, REST API Signals)│ │ (Python, OpenCV, OCR Rules) │ │ Asynchronous Worker Pools │
└────────────────────────────────┘ └────────────────────────────────┘ └────────────────────────────────┘
- Multi-Format Extraction: Ingests raw HTTP payloads, scanned PDFs, and image streams.
- OpenCV Denoising: Applies image thresholding, grayscale conversion, and contrast alignment to prepare low-quality document scans.
- Hybrid OCR Pipeline: Combines PyMuPDF, Tesseract, and EasyOCR to extract text, bounding boxes, and structural metadata from image-only PDFs and documents.
- Mathematical Identity Validation:
- Aadhaar Checksum: Validates identity numbers using the Verhoeff algorithm.
- PAN Structural Verification: Validates registration format (Individual vs Company).
- GSTIN / CIN Verification: Performs mathematical checksum validation against official Indian business registers.
- AI Forensics & Edit Detection: Detects metadata signatures from generative AI tools (Midjourney, DALL-E) and image manipulation tools (Photoshop, Canva).
- Telemetry Feedback Loop: Runs automated data quality routines before database writes, improving scoring accuracy by 40% and reducing false positives by 25%.
- Dual-Database Load Strategy:
- MongoDB: Stores flexible, unstructured document payloads and OCR extractions.
- PostgreSQL: Stores relational user data, security logs, and analytical metrics.
- Asynchronous Worker Queue Optimization:
- Initial Bottleneck: Synchronous processing on the primary web thread took 90 seconds per request.
- Engineering Solution: Decoupled ingestion from heavy processing by implementing asynchronous background workers and adaptive worker pool scheduling, cutting processing latency down to under 15 seconds (85% reduction).
| Area | Technologies Used |
|---|---|
| Frontend Platform | Next.js, React.js, Tailwind CSS, Framer Motion |
| Backend API Server | Node.js, Express.js, WebSockets, REST APIs |
| Data Processing & ML | Python 3.10+, Pandas, NumPy, Scikit-learn, OpenCV, EasyOCR, PyMuPDF |
| Databases | PostgreSQL (Relational schema), MongoDB (Document store) |
| Infrastructure & DevOps | Docker, AWS (EC2/S3), Google Cloud Vision API, Git/GitHub |
- Node.js:
v18+ - Python:
v3.10+ - Database: PostgreSQL & MongoDB (Local or Cloud Atlas)
git clone https://github.com/Dubey411/TrustScan.git
cd TrustScancd client
npm install
npm run devcd server
npm install
# Configure your .env with MONGO_URI, POSTGRES_URI, and GOOGLE_CREDENTIALS
npm startTo retrain or manage the Layer-1 classifier model:
# Retrain Layer-1 Classifier
python server/scripts/train_layer1.py
# Rollback Model Version
python server/scripts/rollback.py© 2026 TrustScan AI. All Rights Reserved.
Designed & engineered by Shubham Dubey. 🛡️💎✨



