Hybrid RAG Architecture for Enterprise CSV Analysis at Scale

One of the greatest unsolved challenges in Enterprise AI is the separation of quantitative data (numbers in a spreadsheet) and qualitative context (rules in a PDF document).

Standard AI workflows force analysts into a disjointed process. You must ask a standard LLM to summarize a vendor contract, manually write down the discount rules it finds, and then go write a separate Excel formula to apply those rules to your sales CSV.

The Hybrid Intelligence Advantage

Advantora Insights pioneered a Hybrid Agentic Architecture to solve this exact problem. Our platform allows you to upload a 50-page Master Service Agreement (PDF) alongside a 50,000-row transaction ledger (CSV) in the same unified session.

You can ask a single, complex question: "Calculate our total Q3 expenditure, and apply the bulk discount penalty clauses outlined in section 4 of the attached vendor PDF."

How It Works Under the Hood

1. Document Context (RAG): The platform uses an Enterprise Vision OCR pipeline to read the PDF, utilizing isolated Supabase databases to retrieve the exact semantic rule regarding the penalty clause.
2. Data Execution (WASM): Simultaneously, the browser-based DuckDB engine scans the local CSV, calculating the raw expenditure.
3. Synthesis: The AI merges the strict mathematical output from DuckDB with the legal context from the PDF, delivering a single, unified executive answer.

This architecture brings true automation to compliance, legal, and financial auditing.