RoboDesk / Platform
Quality Control
Score every interaction against your own criteria — not a two percent sample reviewed a week later.
From sampling to coverage
Traditional QA reviews a small sample long after the fact, which means most problems are never seen and the ones that are surface too late to fix. RoboDesk evaluates interactions on close, automatically, so coverage is the default and reviewers spend their time on judgment rather than on finding cases.
How it works
- Your own scorecards. Quality managers define categories, attributes, weights and templates to match how the operation actually measures quality.
- AI evaluation on close. Draft scorecards are generated automatically and quality issues highlighted for review.
- Humans keep the final say. Reviewers confirm, adjust or override; AI does not close the loop alone.
- Agents can dispute. A formal dispute process on any completed scorecard keeps scoring fair and visible.
- Annotation feeds learning. Marking best-practice responses trains the co-pilot, so strong answers propagate to the whole team.
Real-time guidance
Quality signals can also be generated during a live interaction — a sentiment drop, a keyword red flag — and surfaced to the agent as a tip or checklist while the conversation is still open, so a supervisor can step in before the relationship is damaged rather than reading about it afterwards.
See it run on your own traffic.
Thirty minutes with a CX engineer. No slideware.