Most lead qualification is rules-based — score contacts by title seniority, company size, industry match. If the field exists and matches a threshold, it’s “qualified.” This works for simple cases but falls apart when the criteria require judgment.“Does this company sell to enterprise customers?” There’s no field for that. “Is this person likely the budget owner?” No checkbox in your CRM. “Has this company raised a Series B or later?” Maybe, but only if the funding data is complete and current.Qualify Leads evaluates every record using an AI model that reads the actual data and makes a judgment call — the same way a human SDR would, but across hundreds or thousands of records in minutes instead of days.
You define your criteria in plain language. No formulas, no field mapping, no scoring rubrics.
“Is this company likely a fintech?”
“Does this person’s job description mention managing a team?”
“Is this company actively hiring for data roles?”
The system evaluates every record against every criterion and returns three things per record:
A verdict — Yes, No, or Unknown
Evidence — the specific reasoning behind the verdict, citing what it found
Confidence context — which data it used (description, keywords, web research) so you can audit the decision
Show What's happening under the hood
Each record is evaluated by a language model optimized for the task. The model receives the qualification criteria as numbered yes/no questions and the record’s data formatted as context — company description, keywords, employee data, funding info, or any other field in the schema. It returns a structured response with a verdict and evidence string per criterion. Records are processed in parallel batches, backed by a distributed workflow engine for reliability. Responses are cached so re-runs are near-instant.
Traditional qualification checks whether industry == "fintech". Qualify Leads reads the company description and decides whether it’s a fintech — even if the industry field says “Software” or is blank. It evaluates semantic information (descriptions, keywords, job postings, skills) that rule-based systems can’t touch.
When the answer isn’t in the structured data, the tool can perform live web research per record — searching Google, reading company websites, checking job boards. A criterion like “Does this company have a public pricing page?” triggers a targeted web search for that specific company. The AI model decides per record whether research is needed.
Show What's happening under the hood
When web tools are enabled, the qualification model upgrades to a more capable variant with tool-calling support. It gets access to web search and webpage scraping. The model decides autonomously whether to use these tools — it won’t waste time researching a record where the offline data already answers the question. This adds a few seconds per record that needs research, but only for those records.
You don’t wait for the entire table to finish. Verdicts appear row by row as each record completes. You can start reviewing qualified records while the rest of the table is still being evaluated. An early-stop parameter lets you halt after finding N qualified records — useful for quick prospecting passes.
No black-box scores. Every Yes, No, or Unknown comes with a written explanation citing what the model found. “Yes — company description mentions ‘serving enterprise clients including Fortune 500 companies’” is a verdict you can trust or override.
