Client: A SaaS provider for the tradeshow and exhibition management industry.
Automating Exhibitor Prospecting with a Deep Research AI Agent
The Challenge:
Finding new, high-quality exhibitors is a constant challenge for tradeshow managers. This process traditionally involves hours of manual research, guesswork, and sifting through irrelevant data, leading to a slow and often inefficient sales pipeline.
The Solution:
We built a sophisticated, multi-agent AI system designed for deep prospect research. The agent first analyzes the profiles of existing, successful exhibitors in the client’s database to build a dynamic Ideal Exhibitor Profile. It then autonomously scours the public internet, identifying and qualifying new companies that match this profile, and delivers a curated list of high-potential leads directly to the tradeshow manager.
Key Benefits:
Automated Lead Generation: Eliminates countless hours of manual research.
High-Quality Prospects: Leads are highly relevant, based on a data-driven ideal
profile.
Increased Sales Pipeline: Fills the pipeline with qualified companies ready for outreach.
Strategic Focus: Allows managers to focus on selling rather than manual searching.
Technology Highlights
We architected an advanced autonomous system using
- Python as the base programming language
- LangGraph for complex agentic orchestration
- Hasura for GraphQL layer for seamless data access
- Advanced Reasoning Models e.g., Gemini 2.5 Pro
- Multi-Agent System for Planning, Search, & Supervisor Agents
The Impact:
Our solution revolutionized how users interact with their financial data. It eliminated the bottleneck of traditional reporting, empowering non-technical users to perform complex data analysis through a simple conversation. This made the client’s SaaS product significantly more powerful and user-friendly, providing a clear competitive advantage.
