Lead Quality Engine
Score how well every lead was handled, then send that signal back to the ad platform.
Lead handling quality could not be measured at scale. Reviewing calls and chats by hand was impossible, so whether the team followed the playbook was anecdotal, and managers could not coach on anything concrete. Meanwhile the ad platform optimized for raw lead volume, so it kept buying cheap leads that rarely closed.
Every night the engine gathers each lead's CRM trail, calls, and chat threads, transcribes the calls with speech to text, and scores the handling on a 100-point card: half from process metrics in the CRM, half from an AI read of the transcript with quoted evidence. A human can flag any score as wrong, and those corrections retrain the model against a gold set before any change ships. The same quality signal goes back to the ad platform, so budget can chase value instead of volume.
Walk the pipeline
Click through each stage to see what the system does and what the team gets back.
Stage 1 of 5
Pull the whole story of every lead
Each night it gathers every lead's CRM trail, call recordings, and chat threads. Nobody samples a handful by hand and hopes it is representative.
100-pt
scorecard on every lead
Nightly
across every call and chat
Human
verified calibration loop
Lead handling went from anecdote to evidence. Managers coach on real moments from real calls, the score keeps itself honest through a human calibration loop, and the same quality signal teaches the ad platform to buy leads that actually close. The budget stops chasing volume and starts chasing value.