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AI workflow · Lighting exporter

Turning manual lead research into an AI workflow

AI handled initial filtering, lead scoring, and first-email drafts, leaving the founder's time for higher-value follow-up.

Illustration of an AI workflow for lighting-export lead research
Lighting exporter AI handled initial filtering, lead scoring, and first-email drafts, leaving the founder's time for higher-value follow-up.

Where was it stuck?

The founder spent too much time manually screening LinkedIn prospects.

What did we change?

Built an AI-agent lead workflow scoring prospects by role, company scale, and recent signals, then drafting personalized first emails.

What assets were built?

Customer screening criteria, lead-scoring rules, first-email templates, and human-handoff boundaries.

Public-safe result

Customer-acquisition cost dropped by 60%, and founder time shifted toward better-fit prospects.

Who can use this as reference?

Teams with high-value orders, complex customer judgment, and founders consumed by lead screening.

Boundary

This is a public-safe business breakdown. It does not disclose client names, contacts, contract scope, or private chats. Actual outcomes depend on industry, budget, materials, team execution, and time.