The reliability platform for Voice AI
Know what happened in every voice-agent call. Improve what happens next.
Review production conversations, find failures, monitor quality trends, and evaluate model, prompt, or rubric changes before broader rollout, inside infrastructure you control.
Real captured calls · No signup · Nothing to install
QA pass rate82%-4.2%
Calls evaluated1,24894% coverage
Needs attention37Review queue
Priority calls37
Call reviewScheduling fallback failed
Score 54CallerI need to move my cleaning to Tuesday morning.
AgentI cannot access scheduling. Please call us again later.
Task completion28
Intent understanding91
Information capture44
Likely next fix
The outage branch did not preserve the existing appointment or create a staff handoff.
Replay this failure after the workflow change →From production failure to verified improvement
- Detect
- Investigate
- Evaluate
- Improve
- Monitor
See the workflow
Watch the product walkthrough.
See how a production call moves from evidence to a reviewable score and next action.
Bring one recurring voice-agent failure. We will map the evidence and the next evaluation step together.
One production QA loop
See the problem, understand the evidence, and verify the fix.
VaaniEval connects conversation review, production quality monitoring, and evaluation without asking you to move sensitive call data into another vendor-hosted data plane.
01
Call observability
Available todayReview every conversation in context.
Bring production recordings, transcripts, outcomes, and available provider metadata into one review workspace. Move from a weak aggregate score to the exact call and evidence behind it.
Explore production QA- Synchronized recording and waveform when available
- Turn-by-turn transcript review
- Provider outcomes and call metadata
- Evaluation rationale linked to the conversation
02
Production monitoring
Available todaySee where quality is moving.
Track QA coverage, pass rate, quality scores, review queues, and agent-level trends. Daily summaries can reach email or Slack, while missing provider outcomes remain visible instead of being counted as success.
Use the production QA checklist- Priority review queue
- Score and pass-rate trends
- Agent-level quality breakdown
- Daily email and Slack summaries
Roadmap boundary: Continuous ingestion alerts and richer turn-level latency reporting are still being developed.
03
Evaluation and experiments
Available todayEvaluate the next change before it spreads.
Turn production failures into stable evaluation criteria. Test versioned rubrics on real conversations, compare candidate stacks through offline replay or shadow runs, and keep human review for disputed or high-impact cases.
Explore evaluation workflows- Versioned evaluation rubrics
- Evidence-backed scorecards
- Offline replay and shadow comparison
- Human review for uncertain results
04
Customer-controlled data plane
Deployment-specificKeep voice data under your control.
Run VaaniEval on-premises, in your VPC, or in your cloud account. You choose storage, retention, network policy, credentials, and the approved routes used when an external model provider must receive evaluation inputs.
Review security architecture- Customer-controlled deployment and storage
- Retention and deletion policies you operate
- Encrypted provider credentials
- Explicit provider routing and optional redaction
Evidence before averages
Trace a weak outcome back to the conversation.
Aggregate trends tell you where quality moved. The call recording, transcript, provider metadata, and evaluator rationale help explain what to fix next.
01
Find the call from the priority review queue.
02
Review the evidence next to the available recording and transcript.
03
Test the change against the same success criteria.
turn 14 · 00:48.2High risk
Candidate B
Pay ₹4,820 by the 12th
Candidate C
Pay ₹4,220 by the 12th
- Audio
"…four thousand eight hundred twenty…"
- STT
Transcribed 4,220
- LLM
Intent: create payment link
- Tool call
create_link(amount=4220)
- TTS
"Sending a link for 4,220 rupees."
A ₹600 error reaches the payment link.
Caught in evaluation, before rollout.
This trace illustrates a full voice-agent failure path. The depth of timing and provider metadata available in production depends on what each connected provider exposes.
Security through architecture
Run evaluation where your conversations already live.
Deploy on-premises, in your VPC, or in your cloud account. Calls and results stay in storage you control. Hosted model evaluation uses only the egress routes and inputs you approve.
Review security architectureSelf-hosting gives you control, but it also makes your team responsible for capacity, upgrades, backups, access policy, and incident response.
Production callsVaaniEval servicesYour storage
Approved model endpoints egress you configure
StorageYour database and object store
RetentionYour deletion policy
CredentialsEncrypted at rest
Provider egressExplicit and reviewable
Connect what you already run
Start with the platforms already handling your calls.
Connector depth varies because providers expose different recordings, transcripts, timestamps, and outcome fields. We confirm the available evidence before implementation.
- LiveKitAvailable today
- VapiAvailable today
- BolnaAvailable today
- ElevenLabsAvailable today
- S3-compatible object storageAvailable today
- Private model endpointsConfigured during onboarding
Review current integrations, or request another connector during onboarding.
Questions
What production Voice AI teams ask first.
Clear boundaries make a better security and product review.
What can I review for each production call?
VaaniEval brings available recordings, transcripts, call metadata, provider outcomes, evaluation scores, rationales, and recommended next steps into one workspace. Exact fields and turn timing depend on what each connected voice provider exposes.
Does my production call data leave my environment?
The data plane runs on-premises, in your VPC, or in your own cloud account. Calls, transcripts, and evaluation results stay in storage you control. If you select a hosted voice or evaluator provider, that provider receives the inputs you approve through an egress route you configure. Optional redaction is deployment-specific.
What does production monitoring include today?
The current workspace reports QA coverage, pass rate, average quality, calls needing attention, score trends, data coverage, and agent-level performance. Daily summaries can be delivered through email or Slack. Continuous ingestion alerts and richer turn-level latency reporting are still being developed.
How do evaluations lead to an actual fix?
Each evaluation uses a versioned rubric and returns scores with rationales tied to the conversation. Reviewers can identify the weakest behavior, change the prompt, tool, workflow, or policy, then test the same criteria on representative historical and new calls.
Can I compare a change without letting it answer customers?
Yes. Historical conversations can be replayed offline, and shadow evaluation metadata is supported for candidate comparisons. Available replay depth depends on the connected voice stack and the model endpoints configured in your deployment.
What infrastructure does a deployment need?
A container runtime, PostgreSQL, object storage, and evaluation workers. A small deployment can begin with a Kubernetes namespace or a pair of VMs. As volume grows, plan for database growth, queue depth, media storage, evaluator rate limits, backups, and worker concurrency.
Talk to us
Bring one recurring production failure. Map the path to a verified fix.
In 30 minutes, we review your current call evidence, evaluation criteria, and deployment constraints. No call upload is required.
Founder-ledProduct and architecture reviewNo call upload required
VaaniEval is early. We will tell you where the current product fits and where it does not.