Challenge
Valuable enterprise data often remains trapped in silos: state Medicaid portals, e-commerce catalogs, government PDFs, construction compliance filings, and financial disclosures.
Teams face fragmentation across portals, APIs, PDFs, and dynamic web applications; slow manual collection; incompatible formats; and strict security requirements for how data is collected and stored.
Solution
We built a platform-agnostic ingestion framework that adapts to diverse environments and produces clean, normalized, AI-ready datasets.
Platform capabilities
Extraction engines
Python stacks including Scrapy, Playwright, and Selenium, plus Puppeteer for headless browser automation.
Dynamic site handling
Proxy rotation, resilient scheduling, and JavaScript-rendered page support where permitted and ethical.
AI-assisted parsing
Hosted models plus deterministic parsers interpret unstructured text, normalize fields, and apply contextual tags.
Data modeling and orchestration
Pandas, PySpark, and dbt pipelines normalize schemas; Snowflake, PostgreSQL, S3, Azure Data Lake, and Airflow manage storage and jobs.
RAG readiness
Outputs feed pgvector, Pinecone, or Weaviate for assistant and agent workloads.
Security posture
Controls aligned to SOC 2, HIPAA, and GDPR expectations for encryption, access, and logging.
Process flow
- 01Discover and map structured and unstructured sources.
- 02Run automated scrapers that adapt to site changes within policy.
- 03Parse with AI where layouts are irregular or semi-structured.
- 04Normalize into unified schemas and comparable metrics.
- 05Integrate to BI tools, APIs, or GenAI pipelines.
Impact
- Used across Medicaid and Medicare intelligence, retail pricing, construction compliance, and financial reporting use cases.
- Reduced manual collection windows from weeks to hours with repeatable daily refreshes.
- Structured feeds power assistants that answer comparative and temporal questions at scale.
The framework treats ingestion, parsing, and normalization as one fabric so organizations can move from fragmented web and legacy data to a governed, model-ready asset.