Ingest, structure, and enrich unstructured data at any scale with AI-powered ETL.
Start a ProjectOverview
Data is the fuel for AI, but most of it is trapped in unstructured formats — PDFs, emails, images, spreadsheets, web pages. Our AI-powered pipelines extract meaning from chaos, transforming raw data into clean, structured datasets.
Whether you need real-time streaming or batch processing, we build scalable infrastructure that handles millions of records with built-in quality monitoring, anomaly detection, and automated alerting.
Key Capabilities
Why choose us
AI extracts and structures data from PDFs, emails, images, and any unstructured source.
Automated deduplication, classification, and enrichment ensure pristine datasets.
Process millions of records with infrastructure that scales from batch to real-time streaming.
Continuous monitoring catches anomalies, data drift, and quality issues before they compound.
How it works
Catalog all data sources: APIs, databases, file systems, email, PDFs, web feeds. Map schemas, formats, volumes, and update frequencies. Identify quality issues (missing fields, inconsistent formats, duplicates) and document the target output schema the downstream systems expect.
Design the full ETL/ELT architecture: ingestion strategy (batch vs. streaming), AI processing nodes (extraction, classification, enrichment), transformation logic, deduplication approach, error handling, and output destinations. Scalability requirements documented upfront.
Implement the pipeline with AI-powered extraction and transformation nodes. Built-in data quality framework: schema validation, completeness checks, anomaly detection on statistical distributions, and automated quarantine for records that fail quality thresholds. Unit and integration tests with real data samples.
Production deployment with monitoring dashboards: records processed per run, error rates, processing latency, data freshness lag, and cost per record. Automated alerting for anomaly spikes, pipeline failures, and data quality degradation. Runbook documentation for on-call response.
Measurable impact
Structured, queryable datasets from previously unprocessable unstructured sources
99%+ extraction accuracy on well-formed documents; 90%+ on messy/handwritten sources
End-to-end pipeline latency under 5 minutes for most document processing workflows
Technology stack
FAQ