USE-CASES

AI lead qualification using your sales criteria

A good qualification rule explains why a lead belongs in a queue. Evaluate the information a prospect actually provided against a short, written definition of fit.

Separate fit from buying intent

A large company is not automatically ready to buy. Use explicit signals such as the stated use case, expected volume, current workflow, and implementation timeline. Do not infer budget or personal traits from a name or email address.

A practical first set of outcomes is ready for conversation, needs more information, nurture, and outside scope. Give each outcome a clear boundary in the decision instructions.

Review before you contact anyone

The tool returns a proposed category and confidence when supplied by the model. It does not send outreach. Low-confidence cases should prompt a follow-up question or manual review, not a confident rejection.

Evaluate the rule with historical leads that include both wins and losses. Check whether the model follows your definition, rather than whether its score simply correlates with company size.

Measure the business outcome

Track qualified conversations and opportunities alongside classification accuracy. A faster queue is valuable only if relevant prospects reach the right person. Revisit the rule when your product, target market, or sales capacity changes.

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