Lead qualifier
Score every lead against your ideal customer profile: hot, warm or cold.
A good lead is several questions, not one: industry fit, company size, a problem you solve, authority to buy, intent and budget signals. Each is answered separately and combined with weights you control, so you can see exactly why a lead is hot.
Try it live
Real calls to Jev. Pick an example or paste your own.
Pick an example or paste your own input, then run it.
Under the hood
Every question and rule, in the open. Fork it to change any of them.
What it asks
Reads icp and lead, then asks 6 questions in one call.
- Q1Industry fitScore
How well does the
leadmatch the industry and company type described in theicp?- 0Not a fit
- 1Adjacent
- 2Good fit
- 3Perfect fit
- Q2Company sizePick one
How does the
lead's company size or stage compare with the range in theicp?In rangeToo smallToo largeUnknown - Q3Has the problemYes / no
Does the
leadmention or clearly have a problem theicpsays you solve? - Q4Buying rolePick one
What is the contact's role in buying?
Decision makerInfluencerUserUnknown - Q5Buying intentScore
How strong is the buying intent in the
lead?- 0None
- 1Researching
- 2Evaluating options
- 3Ready to buy
- Q6Budget or timelineYes / no
Does the
leadmention budget, pricing, a timeline or a current vendor contract?
How it decides
Rules run in code, top to bottom. The first one that matches gives the verdict.
- 1If Industry fit < 0.8 of 3Cold
- 2If Lead score ≥ 66Hot
- 3If Lead score ≥ 42Warm
- ·DefaultCold
A 0-100 score weighted from Industry fit ×3, Company size ×2, Has the problem ×2, Buying role ×1.5, Buying intent ×2.5, Budget or timeline ×1.
Call it from your code
Every template runs as-is with an API key, no setup: POST /api/v1/judges/lead-qualifier/run.
curl -X POST https://tryevally.com/api/v1/judges/lead-qualifier/run \ -H "Authorization: Bearer $EVALLY_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "input": { "icp": "B2B SaaS companies with 20-500 employees that ship AI features to customers. Buyers: CTOs, heads of AI/ML, engineering managers.", "lead": "Maya Chen, CTO at Brightdesk (120 employees, customer-support SaaS). Inbound: '\''We launched an AI reply assistant and it'\''s hallucinating refund policies. Need something in place before our renewal with our biggest customer in 3 weeks. What does pricing look like?'\''" }}'Seen in the wild
Community builds on Jev that use this pattern. Not affiliated with Evally.
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