I've sat in dozens of these vendor evaluation calls. The questions teams ask almost never predict whether they'll end up happy with their choice. They ask about feature lists. They ask about pricing tiers. They don't ask about the things that actually matter—until they've already signed and it's too late.
The teams that end up frustrated usually picked a point tool that seemed cheaper and then spent six months working around its limitations. The teams that end up happy asked specific, concrete questions that revealed whether the platform was actually complete or just marketed that way.
Quick filter: If a vendor can't answer these quickly and concretely, move on.
Here are the 12 questions that actually predict success. They're the questions we've seen separate "this platform saved us months" from "we're evaluating alternatives again."
The 12 Questions That Matter
1. Can the platform auto-generate test scenarios from my agent's prompt?
Why this matters: Manual test case creation doesn't scale. Writing 100+ scenarios by hand takes weeks and misses edge cases humans wouldn't think of. It feels manageable until you have to maintain it.
What to look for: The platform should analyze your agent's system prompt and automatically generate diverse test scenarios—happy paths, edge cases, adversarial inputs, and boundary conditions.
Red flag: If the vendor says "you write the test cases, we run them," you'll spend more time maintaining tests than building your agent.
Hamming auto-generates hundreds of test scenarios from your agent's prompt. Paste your system prompt, and get comprehensive test coverage in minutes—not weeks.
2. How many concurrent test calls can the platform run?
Why this matters: Running tests sequentially takes hours. A 100-scenario test suite at 2 minutes per call takes over 3 hours sequentially—but under 5 minutes in parallel.
What to look for: Enterprise-grade platforms support 1,000+ concurrent calls. Anything less than 100 concurrent calls will bottleneck your testing workflow.
Red flag: If the vendor can't give you a specific number, or says "it depends on your plan," concurrency is probably limited.
Hamming supports 1,000+ concurrent test calls. Run your entire test suite in minutes, not hours.
3. Does the platform support production call replay with preserved audio?
Why this matters: When a production call fails, you need to replay exactly what happened—the original audio, timing, pauses, and caller behavior. Synthetic approximations miss subtle issues.
What to look for: True production call replay preserves the original audio recording and caller behavior. You should be able to debug the exact call that failed, not a recreation.
Red flag: If the vendor talks about "simulating similar calls" or "recreating the scenario," they don't have true replay capability.
Hamming replays production calls with preserved audio, timing, and behavior. Debug the actual call that failed—not an approximation.
4. How many built-in evaluation metrics does the platform provide?
Why this matters: Basic platforms offer 5-10 metrics. Enterprise teams need comprehensive coverage across latency, accuracy, compliance, sentiment, and conversation quality.
What to look for: 40+ built-in metrics is the baseline for serious platforms. Look for coverage across:
- Infrastructure (latency, audio quality, interruptions)
- Conversation (compliance, hallucination, repetition)
- Business outcomes (task completion, escalation rate)
Red flag: If the vendor lists metrics vaguely ("we track latency and accuracy"), ask for the complete list.
Hamming provides 50+ built-in evaluation metrics covering infrastructure, conversation quality, and business outcomes.
5. Can I define custom evaluation metrics for my business?
Why this matters: Built-in metrics cover common cases, but every business has unique requirements. A healthcare company needs HIPAA compliance scoring. A restaurant needs upsell tracking.
What to look for: The platform should support custom LLM-based scorers where you define the evaluation criteria in natural language. You shouldn't need engineering help to add a new metric.
Red flag: If custom metrics require "working with our team" or "custom development," you'll wait weeks for every new metric.
Hamming supports custom evaluation metrics defined in natural language. Create business-specific scorers in minutes, not weeks.
6. Does the platform analyze speech beyond transcripts?
Why this matters: Transcript-only analysis misses critical signals. A caller can say "that's fine" while sounding frustrated. Pauses, tone, and speaking rate reveal what words don't.
What to look for: Speech-level sentiment analysis, emotion detection, pause analysis, and speaking rate tracking. The platform should analyze the audio, not just the text.
Red flag: If the vendor only mentions "transcript analysis" or "NLP on the conversation," they're missing half the signal.
Hamming performs speech-level sentiment and emotion analysis—detecting frustration, confusion, and satisfaction from audio patterns, not just words.
7. What accents and languages does the platform support for testing?
Why this matters: Your users have diverse accents. If you only test with standard American English, you'll miss failures that affect significant portions of your user base.
What to look for: Support for major accent variations (Indian, British, Australian, Southern US, etc.) and multiple languages if you serve international users.
Red flag: If the vendor says "we support multiple voices" but can't list specific accents, coverage is probably limited.
Hamming tests with diverse accents including Indian, British, Australian, Southern US, and many more—ensuring your agent works for all users.
8. Can the platform inject realistic background noise and audio conditions?
Why this matters: Real calls happen in noisy environments—offices, streets, cars, cafés. An agent that works in silence may fail with background noise.
What to look for: Configurable background noise injection (office, street, café, car) and audio degradation simulation (poor connections, echo, static).
Red flag: If testing only works in "clean audio conditions," production failures will surprise you.
Hamming injects realistic background noise including office chatter, street sounds, and poor connection simulation.
9. Does the platform integrate with CI/CD pipelines?
Why this matters: Manual test triggers don't scale. At enterprise maturity, tests should run automatically on every PR and block deploys that fail quality thresholds.
What to look for: Native CI/CD integration (GitHub Actions, GitLab CI, Jenkins, etc.) with configurable quality gates that can block deployments.
Red flag: If the vendor says "you can trigger tests via API," but there's no native CI/CD integration, you'll build and maintain the integration yourself.
Hamming integrates with CI/CD pipelines and can block deploys that don't meet your quality thresholds.
10. What compliance certifications does the platform have?
Why this matters: Enterprise deployments require SOC 2 Type II at minimum. Healthcare and financial services need HIPAA and specific data handling guarantees.
What to look for: SOC 2 Type II certification (not just "in progress"), HIPAA BAA availability, and data residency options for regulated industries.
Red flag: If the vendor says "we're working on SOC 2" or can't provide a BAA, they're not enterprise-ready.
Hamming is SOC 2 Type II certified with HIPAA BAA available. Security is pre-configured, not bolted on.
11. What does the platform's observability integration look like?
Why this matters: Voice agent issues often span multiple systems. You need traces, spans, and logs correlated with test results and production calls.
What to look for: Native observability with OpenTelemetry support. The platform should complement your existing tools (Datadog, etc.) while keeping voice-agent-specific data unified.
Red flag: If observability requires "exporting data to your tools," you'll stitch together multiple systems instead of having unified visibility.
Hamming provides native OpenTelemetry observability that complements Datadog and your existing stack—keeping all voice agent data unified in one place.
12. What are the support SLAs and engagement model?
Why this matters: When production breaks at 2 AM, you need fast response. This is the one nobody thinks about until it’s too late. Enterprise teams also need dedicated support for onboarding and optimization.
What to look for: Specific response time SLAs (not just "priority support"), named customer success manager, and a direct communication channel (not just tickets).
Red flag: If support is "email-based" or "community forums," you're on your own when things break.
Hamming provides enterprise support with <4 hour response SLA, named customer success manager, shared Slack channel, and weekly product releases.
Voice Agent Testing Platform Comparison Checklist
Use this checklist when evaluating vendors:
| Capability | Question to Ask | Hamming |
|---|---|---|
| Auto-generation | Can it generate scenarios from my prompt? | Yes—hundreds of scenarios automatically |
| Scale | How many concurrent calls? | 1,000+ concurrent calls |
| Production replay | Preserved audio or synthetic? | Preserved audio, timing, behavior |
| Built-in metrics | How many metrics included? | 50+ built-in metrics |
| Custom metrics | Can I define my own scorers? | Yes—natural language definitions |
| Speech analysis | Beyond transcripts? | Speech-level sentiment/emotion |
| Accent coverage | Which accents supported? | Indian, British, Australian, Southern US, more |
| Background noise | Can it test noisy conditions? | Office, street, café, car |
| CI/CD | Native integration? | Yes—blocks failing deploys |
| Compliance | SOC 2? HIPAA? | SOC 2 Type II, HIPAA BAA available |
| Observability | Native or export-only? | Native OTel, complements Datadog |
| Support | Response time SLA? | <4 hours, forward deployed engineer, Slack channel |
Common Mistakes When Choosing a Voice Testing Platform
Mistake 1: Choosing a point solution
Some tools excel at stress testing but don't monitor production. Others monitor calls but don't help you test pre-launch. You'll end up stitching together 3-4 tools—or living with gaps.
Solution: Choose a platform that covers the complete lifecycle: pre-launch testing, production monitoring, and continuous improvement.
Mistake 2: Underestimating the value of auto-generation
Teams often think "we know our use cases, we can write the tests." Then they spend weeks writing scenarios, miss edge cases, and maintain an ever-growing test suite.
Solution: Prioritize platforms that auto-generate scenarios from your prompt. Manual test creation doesn't scale.
Mistake 3: Ignoring production replay capabilities
Synthetic test scenarios will never perfectly match real caller behavior. When a production call fails, you need to replay exactly what happened.
Solution: Require production call replay with preserved audio—not "similar scenario recreation."
Mistake 4: Settling for transcript-only analysis
Transcripts capture words but miss emotion, tone, and frustration. A caller can say "okay" while being deeply frustrated.
Solution: Require speech-level analysis that detects sentiment and emotion from audio, not just text.
Mistake 5: Choosing based on price alone
Cheaper tools often lack enterprise features—compliance certifications, support SLAs, custom metrics. You'll pay more in engineering time working around limitations.
Solution: Evaluate total cost of ownership including integration effort, maintenance, and the cost of bugs reaching production.
FAQ: Choosing a Voice Agent Testing Platform
What's the difference between voice agent testing platforms and load testing tools?
Load testing tools stress-test your infrastructure but don't evaluate conversation quality. Voice agent testing platforms run realistic conversations, evaluate responses against business criteria, and identify issues in your agent's behavior—not just whether it stays online under load.
How do I evaluate voice agent testing tools if I'm just starting out?
Start with these priorities:
- Auto-generation capability (saves weeks of manual work)
- Easy integration with your voice platform (Retell, VAPI, LiveKit, etc.)
- Built-in metrics that cover your main use cases
- Room to grow into production monitoring and custom metrics
What's the typical implementation timeline for enterprise voice testing platforms?
Point solutions can take 2-4 weeks to integrate properly. Complete platforms like Hamming enable enterprise teams to start testing in 15 minutes—no implementation project required. Connect your agent, auto-generate scenarios, and run tests immediately.
Should I build voice agent testing in-house or buy a platform?
Build vs. buy depends on your team size and priorities. Building basic testing takes 3-6 months of engineering time and ongoing maintenance. Most teams find that buying a platform and focusing engineering on their core product delivers better ROI.
How do I get buy-in for a voice agent testing platform?
Focus on risk reduction and time savings:
- Bugs caught before production (reduced customer impact)
- Engineering time saved vs. manual testing
- Faster iteration cycles (test on every PR)
- Compliance requirements met (SOC 2, HIPAA)
Next Steps
The best way to evaluate a voice agent testing platform is to try it with your actual agent.
Hamming offers a free trial where you can:
- Connect your agent in minutes (Retell, VAPI, LiveKit, ElevenLabs, Pipecat, Bland)
- Auto-generate test scenarios from your prompt
- Run tests with diverse accents and background noise
- See results with 50+ built-in metrics
