Call to score. Under a minute.
Four steps, nothing manual.
Step 1
Upload your call
Drop a recording in any format. Need to process hundreds? Bulk upload handles the queue for you.
MP3, WAV, M4A, FLAC, OGG
Step 2
Transcription with speaker detection
Every word timestamped, every speaker labeled. The system figures out who said what: interpreter, client, provider.
Step 3
Three agents score independently
Three separate models score every call against your rubric. They don't see each other's work. Consensus catches what a single pass would miss.
Step 4
Get your score and report
You get a 100-point QA score, a full deduction breakdown, and specific feedback. Takes seconds.
Behind the score
Not a black box. Here's how the scoring actually works.
What does a 79 vs 92 actually mean?
Every deduction maps to something specific in the call. A 92 means a couple of polish items. A 79 means the interpreter dropped a term, summarized instead of interpreting, or mixed up roles. You see every issue and exactly what it cost.
- Hesitation in greeting-3
- Inconsistent terminology-5
- Dropped a key medical term-10
- Summarized instead of interpreting-8
- Mixed up speaker roles-3
Why three agents, not one
One model can hallucinate or fixate on the wrong thing. So Agent One runs three independent evaluations on every call. Each scores blind, then the system reconciles. Outliers get flagged. The consensus is more reliable than any single pass, human or AI.
Your QA team can only listen to so many calls
Most teams review about 5% of calls. After the first ten, quality drops. Agent One evaluates every call with zero fatigue. Every interpreter gets scored, every shift gets covered. Patterns show up before they turn into complaints.
~10
calls before fatigue sets in
500+
calls, same consistency
Done listening to calls manually?
Score every call with your rubric. You can be up and running in minutes. No integrations required.