Your agent read an account balance before it knew who was calling.
Every banking call rides on identity verified before account access, no account number read back on an open line, and the disclosures your examiners expect. Roark scores all of it — on the audio, on every call.
Backed by YCCaller: What did my account close at this month?
Agent: Sure — you're at $4,212.08.
Disclosure before identity — Account balance read before the caller was verified.Identity check
Scoring production voice AI for teams at
§01 · When the call goes wrong
Here's how a banking call goes wrong.
Each one is an account takeover, an exam finding, or a UDAAP complaint — and the worst of them are inaudible to a tool that only reads the transcript.
Balance before identity
Caller: What did my account close at this month?
Agent: Sure — you're at $4,212.08.
The agent disclosed an account balance before verifying who was on the line. Roark fails any turn that touches account data before identity is confirmed — the exact gap an account-takeover caller probes for.
Identity checkThe full account number read aloud
Caller: Which card is the payment going on?
Agent: The one ending — actually the full number is 4147 8392…
A full PAN spoken on a recorded line is a PCI exposure and a permanent liability in your call archive. Roark flags account-number and PII read-back as a hard fail.
PII exposureThe skipped mini-Miranda
Caller: Why are you calling me?
Agent: It's about your past-due balance — how much can you pay today?
The required debt-collection disclosure was never read before the agent pursued payment. Roark checks your Reg F / mini-Miranda script as pass/fail on every collections call.
DisclosuresThe mishandled Reg E dispute
Caller: There's a charge I didn't make.
Agent: I can remove it, but you should really watch your card more carefully.
The agent both prejudged the claim and skipped the error-resolution rights the caller is owed. Roark checks the dispute script and flags blame-shifting language a transcript-only tool reads as 'resolved.'
Dispute handlingThe stressed caller, missed signal
Caller: Someone called me and I— I gave them the code they texted.
Agent: Okay. Is there anything else I can help you with today?
The caller's voice was shaking — a textbook scam-in-progress — and the agent moved on. The audio model hears the stress and confusion the transcript renders as a calm sentence, so the fraud signal never gets missed.
Vocal stress§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 PAN masking on by default
…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.
Compliance
policy
- Identity check
- PII exposure
- Disclosures
- Dispute handling
- Script adherence
Audio-native
custom models
- Vocal stress
- Pronunciation
- Accent clarity
- Emotion
- Pace & pauses
- Interruptions
Conversational
LLM + rules
- Task success
- Accuracy
- Hallucination
- De-escalation
- Repetition
- Tone
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
Insurance
Quote the coverage right, read every required disclosure, and never sound cold to a claimant in crisis.
ExploreCustomer Support
Resolve it for real, escalate cleanly, and hear the frustration the transcript hides.
ExploreHealthcare
Say the drug name right, verify before PHI, and never sound like a robot to a scared patient.
Explore
HIPAABAA availableAccount data & PII handling
PAN/PII masking, configurable retention, and zero-data-retention options — plus scripted checks for your Reg E, Reg F and disclosure requirements on every call.
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