Missing corrections
A pass/fail score doesn't record the correct reply or sequence of actions.
Voice AI testing
Test how your voice agent handles real calls.
Check whether your agent hears the caller correctly, handles interruptions, uses tools, and knows when to hand off. We review failed calls, approve better replies with your team, and test the changes in your voice setup.
Keep the correction alongside the failed call.
A failed test shows where to look. A reviewed example of what the agent should have said or done helps you build and test a fix.
Transcripts without judgment
Raw logs show what happened, not what should have happened.
Untested prompt edits
A new instruction can fix one kind of call and cause trouble on another.
Unused review work
Approved corrections can become examples for similar calls, but only if the agent can find the right ones.
Choose a fix for one type of call
We review the call and tool history with your team, write approved corrections, and test whether the problem needs a prompt change, retrieved examples, or language-model training.
Trace reconstruction
Turns, tools, handoffs, and final utterances from your stack exports.
Reviewed replies
Save the approved reply, the reviewer's explanation, and the policy version it follows.
Call results
Compare completed tasks, human handoffs, and time spent correcting replies. Staying on the line isn't success if the caller's problem remains.
Voice quality needs voice-specific tests
A text-only benchmark cannot expose every call failure. Voice evaluation should include the mechanics of a conversation as well as the final answer.
Turn-taking and interruption
Test barge-in, silence handling, confirmations, and whether the agent recovers after an interrupted tool call.
Speech and response time
Check uncertain transcriptions, pronunciation of names and numbers, and delays that make calls harder to complete.
Handoffs and compliance
Agree when to transfer a call, what notices are required, and what the agent should tell the person taking over.
Store approved call corrections in a golden dataset for AI agentsUse LLM evaluation services to set release gates and compare call outcomes
Calls, scenarios, and test results
Check how the agent handles the call as well as what it says.
Testing modes
Scripted scenarios, simulated calls, replay of production calls, and live production review with approval gates.
Call measurements
Interruptions, silence, uncertain transcription, confirmation of names and numbers, response time, and handoffs.
Test scenarios
Accents, noise, confirmation of details, keypad input, interruptions, and required notices.
Input requirements
Call audio or high-fidelity transcripts, tool traces, policy versions, and outcome labels where available.
Consent and personal data
Confirm permission to use recordings, remove unnecessary identifiers, and restrict access to approved reviewers.
Sample report outline
Failed call clusters, voice rubric scores, approved utterances, and a measured pilot recommendation.
Free template
Use the voice AI test checklist
A print-ready test matrix for ASR uncertainty, entity confirmation, barge-in, silence, latency, tools, disclosures, and handoffs.
This template uses made-up examples, not client data. Adapt it to your policies, privacy requirements, and approval process.
Who this is for
- Voice support and healthcare admin agents with repeating call patterns
- Teams that need people to review and correct calls from their voice platform
- Compliance-sensitive workflows where wrong utterances are costly
- QA leads who want approved example replies alongside scores
Sprint shape for voice
Review
Sample real calls and agree how to score the agent's replies and actions.
Build
Prepare approved replies and test a language-model adapter if appropriate. This doesn't include voice cloning or text-to-speech training.
Evaluate
Compare before-and-after results and recommend how to roll out the change.
Test before using replies on live calls
Review each example and restrict where it can be used. Test on separate calls before making it available to more customers.
Limitations
- Transcript-only scores omit barge-in, silence, and latency behavior.
- Simulated calls miss live accents, noise, and partial tool results.
- Approved utterances can become unsafe if policy or product state changes.
- Keeping a call with the agent isn't success if the caller's issue remains unresolved.
How we test changes
We review real conversations with your team and agree what the agent should do differently. Approved corrections can become training data, test cases, or examples the agent looks up. We compare each change with your best existing setup on separate test cases, then check for new failures in the software you plan to deploy.
References:
Common questions
What does the work include?
We review the call and tool history with your team, write approved corrections, and test whether the problem needs a prompt change, retrieved examples, or language-model training.
Do you fine-tune our model?
If it's worth testing after we review the problem. We try your best prompt and consider better examples or tool fixes first. We test an adapter on cases it wasn't trained on and check for new failures. The contract sets delivery and usage rights; the base model's license still applies.
What do we need to start?
Logs or conversation exports for one task, examples of good results, and a reviewer who can approve corrections. We agree data handling before you send anything. Remove unnecessary personal details.
How is success measured?
We test the existing and changed agent on the same task. Where the data is available, we compare completed tasks, human edits, handoffs, response time, and cost.
Related
Book a voice model review.
Discuss one type of call your agent struggles with. We'll agree data handling before you share recordings or transcripts.