A custom Retrieval-Augmented Generation system engineered for a leading FinTech client to automate complex regulatory analysis and portfolio intelligence.
Timeline
4 Months (MVP to Scale)
Services
AI Engineering, UI/UX Design
Our client faced a critical bottleneck: financial analysts spent 60% of their time manually parsing 1,000+ page regulatory filings and earnings transcripts. The objective was to create a sentient knowledge layer that could answer high-stakes queries with 99% citation accuracy.
Sentient-Arc architected a bespoke RAG pipeline that transforms unstructured PDF data into a queryable semantic mesh, providing instant intelligence through a conversational interface.
Next.js 14, Tailwind CSS
OpenAI GPT-4o, LangChain
Pinecone Serverless
Vercel, AWS S3
Solving for hallucinations and high-latency in high-frequency trading environments.
Standard RAG pipelines fail with nested tables and cross-page financial footnotes. Missing a single decimal point in a 200-page SEC filing is a critical failure.
We implemented a multi-stage OCR and recursive character splitting strategy with Parent-Document Retrieval, ensuring context is never lost during vector chunking.
Ensuring the AI only answers based on uploaded documents with zero external leakage.
Self-correcting reflection loops verify citations before every analyst-facing response.
Reducing time-to-first-token for real-time analyst workflows.
Parallelized embedding generation and streaming response delivery via edge functions.
Crafting a high-density, professional interface for the modern financial analyst.
Reduction in manual research time
"Sentient-Arc did not just build a tool; they transformed our entire research methodology."
- Chief Technology Officer, Client X
