Prove your AI agent works before it launches. Know exactly how it's performing afterwards.
Manual QA
A tester runs a few dozen conversations. Your agent will have thousands in week one.
Fails with customers
Without exhaustive QA before launch, teams are testing & learning on live customers.
Poor visibility
Today's analytics were not made for AI agents, so true performance assessment is limited.
An LLM plays the customer while another judges whether your agent did the job.
Turn actual conversations into test cases, or describe the scenario you want.
Run the same test case on both channels for complete visibility.
Pass and fail counts at both the test case and trial level, alongside response times: mean, minimum, maximum, and 95th percentile.
Every agent and function that ran, with a status against each. Run the same case fifty times or five thousand and see how much the result varies.
Each evaluation criterion scored individually, with the reasoning behind the verdict. You see which goal failed and what the judge concluded, not a single number.
The actual conversation that produced the result, kept with it. Read what the agent said, then compare across runs to see what improved and what regressed.
1,000s
Run thousands of trials
2
Core testing channels
Review every parameter against every scenario pre-launch, while the use case stakes are still zero.
Test new prompts
Find edge cases
Compare channels
Check PII masking
Validate guardrails
Test with stat sig
Every conversation analyzed for what customers actually came for, ranked by how often it comes up.
Sentiment read from the conversation itself, in real time, rather than from a survey after the actual moment.
The specific moments where conversations slow down or break, located precisely enough to fix.
VOICE INSIGHTS PAPER
What's Really Happening in the Drive-Thru?
Your universal AI agent analytics. Resolution and conversations handled, down to detailed logs. Observe all of your key KPIs in real-time for effective AI management,
Performance sliced by topic, so you see which subjects your agents resolve cleanly and which ones they need help with.
Every conversation stored with a transcript, AI summary, handle time, responder, and feedback. Filter it, roll it up, or export to CSV and JSON for external analysis.
Per-agent handle time, response time, and resolution rate, plus token consumption broken down by LLM provider and model.
Set what your agents can do before they do it. Rule-based functions, channel-level guardrails, built-in PII detection, and enterprise compliance keep actions secure and precise.
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Build multi-agent systems where agents coordinate to complete requests, running on speech and language models we develop ourselves specifically for your domain.
Our teams work alongside yours from evaluation through deployment. Technical deep-dives, ROI assessment, and design, plus managed services when you want us running it.
Experience SoundHound
Learn how OASYS AI agents handle your conversations in the real world.
Experience AI agents built for your use cases
See how we balance flexibility and reliability
Explore how we deliver outcomes that matter to you
Discuss what support looks like