6-agent self-correcting system that debates and fact-checks to produce PhD-level enterprise research.
Enterprise organizations spend $167 billion annually on R&D, yet 67% of strategic decisions are based on incomplete or superficial analysis. Traditional AI tools produce "intern-level" work — acceptable for basic tasks but insufficient for critical business intelligence. Our client, a Fortune 500 consulting firm, was losing $2.4M annually to research errors.
Traditional AI research relies on one model's output — a critical vulnerability where hallucinations and biases go unchecked. Occurrence: 15%. Annual cost: $2.4M.
Standard LLMs provide surface-level insights suitable for undergraduate work, not enterprise decisions that demand PhD-level depth. Occurrence: 78%. Annual cost: $5.1M.
No built-in fact-checking means researchers must manually verify every claim — a time-consuming and error-prone process. Occurrence: 92%. Annual cost: $3.2M.
Six specialized agents orchestrated through LangGraph and n8n. No single point of failure. Adversarial validation. Consensus-driven accuracy. Each agent has a specific role in a democratic review loop.
The Scout gathers data across 50+ sources. The Drafter synthesizes raw research into coherent analysis with proper structure, citations, and academic formatting.
The Critic identifies logical fallacies and weak arguments. The Fact Checker verifies every URL, statistic, and claim against original sources with real-time validation.
The Synthesizer resolves conflicts using consensus algorithms. The Manager provides human-in-the-loop oversight and ensures enterprise compliance before delivery.