ResearchAI/TechLead Researcher / Builder
VenoxAI Agentic Lead Conversion Auditor
Branch-aware multi-agent research prototype for evaluating lead-conversion decisions.
The problem
Multi-agent systems are widely promoted for business decisioning, but there is little publicly reproducible evidence that they outperform simpler baselines on lead-conversion decisions.
The approach
- 01Build a branch-aware multi-agent prototype targeting lead-conversion decisions
- 02Author offline tests covering the decision branches and reasoning paths
- 03Run a mock benchmark across six synthetic cases
- 04Report the null result honestly and identify what a future real-world evaluation would need
Tools & stack
Python multi-agent orchestrationOffline unit and branch testsSynthetic benchmark harness
Results
- → 31 passing offline tests with 88.65% branch-aware coverage
- → 6 synthetic benchmark cases executed
- → Null result — no multi-agent advantage demonstrated on the mock benchmark
- → Jonathan Fan (Associate Faculty Director, Georgia Tech Center for AI in Business) requested a working prototype; delivered prototype is under review, with a possible collaboration currently being scoped (not a confirmed internship, research position, publication, or Georgia Tech-sponsored project)
Deliverables & links
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