Challenge
A financial operations team was spending 2–3 hours per search session manually navigating complex document repositories — scanned PDFs, spreadsheets, multi-hundred-page lease agreements, valuations, and inventory files. Microsoft 365 Copilot couldn’t index scanned PDFs, leaving a critical portion of the document library effectively invisible to search. The bottleneck was slowing analysis, decision-making, and team throughput.
What We Did
FusionHit designed and built a production-grade Retrieval-Augmented Generation (RAG) system tailored to the complexity of financial documents. The architecture combined Azure AI Document Intelligence for advanced OCR processing of scanned files, a multi-agent Copilot Studio layer for conversational queries, and multiple specialized LLMs — including GPT-4.1 and Claude Sonnet — to handle the range of document types and query complexity.
The ingestion pipeline runs automatically via Kubernetes CronJobs overnight, ensuring documents are indexed continuously without manual intervention. The result: any document in the repository — regardless of format or scan quality — can now be queried in plain language.
Results
- 130,000+ files processed across 7 client sites
- Documents previously unsearchable due to scan format now fully accessible
- Search time reduced from 2–3 hours to minutes for complex financial queries
- Full OCR pipeline built and deployed for approximately $50 in processing costs
- Conversational RAG agent live in production for the financial team
Why It Matters
For financial teams, the cost of slow document retrieval isn’t just time — it’s decision latency. When the data exists but can’t be found, analysis slows, errors creep in, and teams default to workarounds. This engagement shows what AI-powered document intelligence looks like at operational scale: not a prototype, but a production system processing six-figure file volumes at negligible cost.
