Autonomous Research Intelligence Platform Core Value Proposition: An AI-powered research accelerator that autonomously ingests technical content (papers, code, docs), builds queryable knowledge graphs with deep technical understanding, and generates actionable research insights through multi-agent orchestration.

1. Research-First Data Ingestion Automated pipeline for arXiv papers, GitHub repos, dataset, models Deep extraction: not just keywords, but algorithms, experimental results, mathematical relationships, code patterns Continuous monitoring and graph updates as new research emerges

2. Multi-Agent Research Workflows Literature Agent: Discovers and synthesizes papers, identifies trends and gaps Code Analysis Agent: Maps implementations, finds patterns, connects theory to practice Synthesis Agent: Generates cross-domain insights, hypothesis suggestions Query Agent: Answers complex research questions with evidence trails

3. Technical Depth Graph nodes represent: algorithms, theorems, experimental results, code implementations, benchmarks Relationships: "implements", "improves upon", "contradicts", "validates", "uses as baseline" Mathematical and algorithmic concept mapping