The Challenge: Unstructured Data Bottleneck
A large-scale logistics company processes millions of shipping and customs documents daily. These documents are often unstructured PDFs or scanned images, requiring costly manual review, classification, and data entry. The lag in this process created a bottleneck in their supply chain, leading to compliance risks and delayed shipments. The existing data pipeline had a high latency rate for unstructured data processing.
DevoxLabs' AI Engineering Solution
DevoxLabs developed a proprietary AI data pipeline built on a robust Node.js backend.
Document Ingestion & Pre-processing: Implemented an optimized pipeline for ingesting documents and converting them into high-quality text embeddings.
Vector Store Integration: Utilized a Vector Store to index and retrieve relevant content from historical documents rapidly, providing context for the AI model.
Gemini API for Classification and Summary: Integrated the Gemini API to automatically classify documents and summarize key entities.
Observability: Built custom dashboards to monitor model performance, latency, and error rates in real-time.
Measurable Outcomes
Operational gains from the AI-driven ingestion pipeline.
| Metric | Before DevoxLabs | After DevoxLabs | Improvement |
|---|---|---|---|
| Data Processing Latency | 10 minutes per batch | 6 minutes per batch | 40% Reduction |
| Manual Error Rate | 4.5% | 2.0% | 55% Reduction |
| AI Classification Accuracy | N/A | 97.2% | High Confidence |
| Estimated Annual Savings | N/A | $300,000+ (via reduced manual labor) | Significant ROI |