An autonomous agentic platform designed for institutional financial analysts. It intelligently routes complex queries between quantitative Data Lakes and qualitative Knowledge Bases to decode SEC filings with precision.
System Online: Monitoring SEC FilingsPowered by LangGraph and AWS serverless computing, SecRadar abstracts away the complexity of XBRL financial reporting into a seamless chat interface.
A deterministic routing engine utilizing Claude Sonnet/Haiku. The agent autonomously interprets user intent, disambiguates complex financial terminology, and selects the optimal tool sequence to fulfill the request.
Enterprise-grade scalability deployed through BedrockAgentCore. The application runs securely in isolated AWS microVMs, ensuring zero cross-tenant contamination and instant cold starts.
A fully automated AWS Lambda ingestion layer. Monitors SEC EDGAR daily, applies rigorous bronze-to-silver data transformations, and seamlessly hydrates down-stream analytical engines without duplication.
Traditional RAG fails at math, and SQL fails at narratives. SecRadar combines both paradigms to offer comprehensive risk surveillance.
When asked "What was Tesla's R&D spend in 2024?", the agent crafts Presto SQL to query an S3 Data Lake of flattened US-GAAP JSON Lines. This circumvents LLM hallucination in financial mathematics, ensuring SEC-verified numerical accuracy across historical fiscal periods.
When asked "What are the primary supply chain risks?", the agent queries an Amazon Bedrock Vector Store. Raw 10-K HTML is procedurally cleaned into pure Markdown, stripping XBRL tags while preserving semantic table data, enabling pinpoint retrieval of managerial narratives.