Call QA that used to take an hour per call, now happens on every single call.
ClearCall Analytics is a cross-platform desktop app built for a telemarketing team that needed rubric-based quality assurance without paying someone to sit and listen to hundreds of recorded calls. It transcribes, scores, coaches, and flags — automatically.
Overview
The problem
The team was recording every outbound sales call but could only manually review a small fraction of them. Quality issues — script drift, weak objection handling, missed booking details — went unnoticed until they showed up in the numbers weeks later.
Manual review didn't scale, wasn't consistent from reviewer to reviewer, and gave agents feedback too late to matter.
The approach
Every call gets transcribed and run through a weighted rubric by an LLM, producing a 0–100 report card with category-level scores, a script-compliance checklist, audio metrics, and coaching notes with evidence quotes pulled straight from the transcript.
- Full coverage instead of spot-checks — every call scored, not a sample
- One consistent rubric, applied the same way every time
- Coaching notes agents can actually act on, not just a number
- Recurring objections and script gaps surfaced automatically
Product tour
Six screens from the app, in the order a QA manager would actually use them.
Program health at a glance: total calls analyzed, average score, booking rate, and active red flags — plus a live pattern-discovery queue surfacing new coaching signals as they're detected.
Every processed call, searchable and filterable by agent, call type, date range, and QA status — with one-click CSV export for reporting up the chain.
Each call gets a score out of 100 across five weighted categories — tone & energy, objection handling, booking quality, brand pronunciation, script compliance — plus a step-by-step compliance checklist, audio metrics like talk ratio and filler-word rate, and coaching notes with evidence quotes lifted straight from the transcript.
Roll-up scorecards per agent — booking rate, decline rate, red-flag count, and the same five-category breakdown — so a manager can spot who needs coaching without listening to a single call.
Drill into one agent's score trend, category radar, outcome breakdown, and red-flagged calls, alongside auto-generated strengths and most-common-improvements notes aggregated across their entire call history.
The system watches for recurring objections and coaching patterns across the batch — "homeowners keep asking whether it's worth it if they move within 5 years" — and queues them for one-click approval into the script or objection library.
Built with
A native-feeling desktop app that ships to a small team without hosting overhead or a subscription bill for a third-party QA platform.
Cross-platform desktop
Electron + React front end, packaged for Mac and Windows so the team installs it like any other desktop tool — no browser tab, no server to keep alive on their end.
LLM-driven scoring
FastAPI backend orchestrates transcription and rubric scoring against Claude and OpenAI models, with re-scoring and a rules-only fallback mode built in.
Built to extend
Objection library, script editor, and pattern discovery are designed so the rubric and script evolve from real call data instead of being fixed at launch.
Need something like this?
If your team is doing manual review or QA of any kind — calls, tickets, transcripts, documents — this pattern of AI scoring plus surfaced patterns usually applies.