Fodda x GLG: Two Models for Bringing Human Expertise Into Research
Verified public product capabilities as of September 20, 2026.
Core Takeaway & Selection Rule
GLG and Fodda both bring specialist human knowledge into research, but through different operating models: GLG centers on finding and engaging experts at scale; Fodda centers on making selected expert perspectives and supporting context repeatedly callable inside AI workflows.
Platform Design & Architecture Fit
GLG (Gerson Lehrman Group)
GLG connects decision-makers to an expert network of approximately 1.2 million professionals for 1-on-1 consultations, follow-up questioning, and post-call transcripts, with an MCP connector exposing client projects and Library transcripts.
Fodda
Fodda structures selected expert perspectives (Human Agents, Classic Agents, and Synthetic Experts) into reusable knowledge graphs and bounded context sources that external AI assistants can consult repeatedly.
Operational Selection Matrix
- Choose GLG When: The research problem begins with "find the right person and arrange primary expert access," especially when broad expert sourcing, managed matching, compliance, and bespoke 1-on-1 calls are central.
- Choose Fodda When: The research problem begins with "give my AI system a bounded specialist context source it can consult repeatedly," combining selected expert perspectives with Libraries, evidence relationships, and Signals.
- Use Together When: Primary expert conversations should become part of an ongoing AI research workflow, with expert-derived material complemented by other specialist context and institutional evidence.