AdvancedLeads AI — Multi-Agent Lead Scoring

B2B lead qualification is usually a single model's judgment call, and single-model judgment is unreliable: it's confident, opaque, and hard to audit. AdvancedLeadsGeneration-AI replaces that with a structured debate between agents, then a scored reconciliation against historical deal data. The system runs three roles. An advocate agent argues why a lead is a strong fit, citing signals from the lead's profile and company data. A risk agent argues the opposite — surfacing counter-evidence, red flags, and gaps in the data. A reconciliation layer then scores both arguments against historical won/lost deal patterns and produces a final lead score with the reasoning attached, so a sales team can see not just the score but why it was given. The key design decision was separating advocacy from risk assessment. A single prompt asking a model to "evaluate this lead" tends to collapse into one perspective. Forcing two agents to argue opposing positions, then having a third layer judge between them, produces more balanced output and surfaces concerns a single-pass evaluation would miss. We built it on IBM Granite models via IBM Watsonx.ai, with a FastAPI backend exposing the workflow as public-facing REST APIs and a Next.js frontend. The project won the IBM Granite Hackathon on lablab.ai (Team PolyEns, February 2025). The architecture and API contracts are documented publicly in the repository so the debate-and-reconcile pattern can be reused for other scoring workflows — vendor evaluation, candidate screening, or any decision where a single model's opinion isn't enough.