How to Build an AI Agent to Research 400 Leads Per Day
Build an AI agent to automate manual research. Turn a 6 hour prospect review into a 20 minute process and increase your capacity by 4X.
What you'll learn
Clear outcomes, practical examples, and a workshop structure designed to help teams understand how the system actually works.
Gather and enrich raw contact and company data automatically. Feed the AI agent exact context to process data accurately.
Build an engine to review headcount and growth metrics. The AI agent evaluates contacts to verify decision makers.
Run evaluations continuously to process hundreds of leads daily. A 3-person team can execute like a 40-person one.
Why this topic matters
Why this workshop matters in the real world, and what operational problem it helps teams solve.
Manual research forces professionals into data entry and caps your pipeline. Building an AI agent removes the grind of inspecting prospects and companies one by one. You will turn a 6 hour task of reviewing 100 prospects into a 20 minute automated process. This drives a 4X capacity increase and frees your team to just talk to customers.
Event details
Everything you need to know before registering, laid out clearly so the listing is easy to scan.
Speaker
Learn from an operator who builds AI systems inside real businesses, not just slide decks.
Jason Tan
Chief AI Officer at Ascendnce | Speaker
I am the Chief AI Officer at Ascendnce. We build AI agents that scale revenue without adding headcount, automating real operations like lead research and email triage.
Previously, I bootstrapped Engage AI to 100,000 users and built pricing systems for major insurers. After 20 years in data science, I prefer execution over theory. I map business workflows to prove financial cases and build actual technology.
In this course, I teach engineers and corporate employees this approach. You will learn to identify expensive bottlenecks, work backwards, and rapidly prototype a functional AI product. You leave with the frameworks and a working AI prototype to become an internal product leader.
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