Your FNOL agent quoted a deductible that does not exist.
Claims, FNOL, quotes and renewals run on exact coverage facts, state-required disclosures, and a human voice for someone who just totaled their car. Roark scores every call on the audio — and fails the ones that get the policy wrong.
Backed by YCCaller: My house flooded, everything is gone.
Agent: Okay. What is your policy number?
Flat empathy — Correct words, cold delivery to a claimant in crisis — heard by the audio model, invisible to the transcript.Empathy
Scoring production voice AI for teams at
§01 · When the call goes wrong
Here's how a policy call goes wrong.
Each one is a bad-faith exposure, a misquoted premium, or a missed fraud cue — and most are inaudible to a tool that only reads the transcript.
The deductible that does not exist
Caller: What's my deductible if I file?
Agent: Looks like $250 on this policy.
Stated with total confidence, never checked against the policy — the real number is $1,000. Roark flags coverage assertions that were never verified against the record.
Coverage accuracyThe skipped recorded-line disclosure
Caller: I need to report an accident.
Agent: Sure — let's get the details.
The required recorded-line and claims-handling notice was never read on a recorded call. Roark scores disclosure language as pass/fail on every call.
DisclosuresCold voice on a claimant in crisis
Caller: My house flooded, everything is gone.
Agent: Okay. What is your policy number?
The words were procedurally correct and the delivery was flat. The audio model scores warmth and the vocal stress in the claimant the transcript can never show.
EmpathyThe missed fraud cue
Caller: The flood was Tuesday — wait, no, before I bought the policy.
Agent: No problem, I will start the claim.
The caller contradicted their own loss date and the agent moved on. Roark flags inconsistency and fraud-signal cues the agent should have escalated, not processed.
Fraud signalsThe wrong coverage on renewal
Caller: Does this still cover water backup?
Agent: Yes, that is included on your plan.
It was dropped at last renewal. A wrong coverage answer becomes a denied claim and a bad-faith complaint months later. Roark fails unverified coverage confirmations.
Coverage accuracy§02 · From caught to fixed
Roark catches every one of these — and proves the fix.
Each failure above is filed with its evidence, becomes a repeatable simulation until a candidate passes, and is verified on your next thousand live calls.
Your fix, replayed against the exact failures above.
Every change explicit and diffed — you apply it.
You ship — Roark confirms the metric moved on live calls.
you ship it — Roark verifies every call, with state disclosure scripts enforced
…and the loop runs again on the next call.
§03 · Simulate before launch
Break it in staging,
not in production.
Run your agent against hundreds of simulated callers — realistic personas, accents, background noise and edge cases — and get every conversation scored before a customer ever dials in.
Scenarios & personas
Hundreds of simulated callers — the angry one, the rambler, the interrupter — built from your real call types.
45 languages & accents
Native accents, code-switching and background noise — in every market your agent answers.
Load & health tests
Peak-volume concurrency and always-on health checks, so the agent that passed in staging survives launch day.
Run it in CI
Every prompt or model change runs the suite before it merges — quality gates for conversations, not just code.
1 failure filed as an issue — fix it before launch, not after
§04 · Evals & observability
64+ metrics. Your models,
not just an LLM.
Every production call scored as it lands — issues filed, alerts fired, dashboards and OTEL traces on tap, for voice calls and chat threads alike. And where most tools grade a transcript with an LLM, Roark runs purpose-built audio models on the call itself, measuring what your customer actually heard.
Everyone else
LLM reads the transcript
“The agent said the right words.” Misses how it sounded — the mispronounced drug name, the flat apology, the rushed close.
Audio models hear the call
Pronunciation, accent, emotion and vocal stress measured from the waveform — the signal an LLM grading text can never see.
Accuracy & compliance
policy
- Coverage accuracy
- Disclosures
- Quote accuracy
- Fraud signals
- Script adherence
Audio-native
custom models
- Empathy
- Vocal stress
- Pronunciation
- Accent clarity
- Pace & pauses
- Interruptions
Conversational
LLM + rules
- Task success
- Hallucination
- Repetition
- Tone
- De-escalation
Performance
latency
- Time-to-first-word
- Turn latency
- ASR WER
- Barge-in handling
§05 · Get started
First call scored in under a minute.
One click on any platform below and production calls stream in on their own — or send any recording with three lines of code.
Read the quickstartimport Roark from '@roarkhq/sdk'const roark = new Roark({ apiKey })await roark.calls.evaluate({recordingUrl, agent: 'support_v2',}) // scored in seconds
Works with
Also built for
Finance
Verify before you disclose, never expose an account number, and read the disclosure your regulator wrote.
ExploreHealthcare
Say the drug name right, verify before PHI, and never sound like a robot to a scared patient.
ExploreCustomer Support
Resolve it for real, escalate cleanly, and hear the frustration the transcript hides.
Explore
HIPAABAA availableDisclosure & TCPA scripts
Roark checks state-required claims disclosures, recorded-line notices and TCPA consent language as pass/fail on every call — configurable retention and redaction for claimant PII.
Bring a recording.
We’ll score it live.
See your own agent measured on the audio it actually produced — in the demo, in real time. Stop guessing whether your voice AI works.
founders@roark.ai · we reply fast