A config-driven system for gathering, verifying, and synthesizing information from multiple sources. The pipeline uses multi-agent orchestration to handle research at scale while maintaining rigorous verification standards.

Problem

AI-assisted research faces a fundamental trust problem: models can hallucinate citations, misrepresent sources, and present unverified claims with high confidence. Manual verification doesn’t scale — checking 50+ claims per document can take 4+ hours.

Approach

The system uses four specialized agent types working in concert: intake agents for source processing, verification agents for claim checking, synthesis agents for pattern recognition, and quality agents for output validation. Each agent operates within config-driven workflows defined in YAML, making the pipeline reproducible and auditable.

What’s Implemented

• PDF extraction with PyMuPDF for citations, metadata, and structured content
• Source database for tracking provenance across all research documents
• Tiered verification system — immediate, before-use, background, and optional tiers based on claim criticality
• Dynamic insight tracking that evolves as new documents are processed
• SPARK scoring for research quality assessment
• Quality gates that block publication of unverified claims

What This Demonstrates

This project reflects capabilities in multi-agent system design, research verification pipelines, config-driven architecture, and building systems where AI assists but humans remain in control of key decisions.