Automatic Lead Qualification
Automatically analyze incoming inquiries, qualify leads, and prioritize high-potential opportunities.
Monday morning, nobody knows which message to call back first
The pile of requests that built up since Friday is wildly uneven: some spell out a budget, a deadline, a project already under way; others just say “send me a quote.” Without a shared way of reading them, the salesperson works through them in the order they arrived, and a high-potential lead can wait behind a message that was pure curiosity. By the time it gets a callback, a competitor may already have replied.
A score that mostly reflects what it was taught to notice
A model trained on form fields misses the signal sitting in free text: a budget mentioned in passing, urgency carried by the phrasing rather than a keyword. When the model is tuned by the same person who then double-checks every score by hand, automation adds a review step on top of an already full workload.
The missing link: connecting a lead to what happened to it
A qualification score only earns its keep if it is built from real history: which requests turned into clients, which went nowhere, and why. That data rarely exists in a usable form, and marketing and sales do not always agree on what “qualified” means. Building the score before settling that disagreement amounts to automating a guess, however polished the interface around it looks.
The score sets the calling order; the conversation stays a sales job
We build systems that read each request, whatever the channel, extract the context, and propose a ranked calling order with the reasoning shown alongside the score. The salesperson keeps the call itself, and their corrections, whenever the ranking is wrong, feed back into the model.