Evidence-backed web research for AI agents. Real-time search with cited claims, confidence scores, and compare mode showing raw LLM hallucination vs evidence-backed answers.
Enables iterative deep research by integrating AI agents with search engines, web scraping, and large language models for efficient data gathering and comprehensive reporting.
A multi-agent research system that decomposes complex queries into targeted sub-questions, searches the web in parallel, scores source credibility, and synthesizes findings into structured markdown reports.
Enhances LLM applications with deep autonomous web research capabilities, delivering higher quality information than standard search tools by exploring and validating numerous trusted sources.
Open-source research engine that extracts structured knowledge from any topic via MCP, enabling AI assistants to get verified, scored claims and entity graphs from live sources.