If you're shortlisting voice AI testing platforms, Roark and Coval will both come up — and most vendor comparisons are sales pages wearing a lab coat. This one is written by a vendor too: we build Roark. So here are the ground rules, and then the honest case for why Roark is the stronger platform for testing voice agents.
We keep it specific and verifiable. Everything below about Roark is something you can see in our docs or reproduce on your own agent. Where we compare, we compare on the things that decide whether a voice agent is actually ready to ship: how deeply the platform simulates real calls, how it evaluates them, and what feeds the tests over time.
The short answer
Both products do simulation testing and production monitoring. The old "testing tool vs monitoring tool" framing is stale — this is a two-job category now, and both platforms cover both jobs.
Roark is the stronger choice on the job that catches the most failures: simulation testing. For the past year it's been our near-singular focus, and it shows in the depth — simulated callers that place real phone calls over the PSTN (not a text loopback), coverage across 45 languages and accents, audio-native metric models that score the sound of the call and not just the transcript, and a replay loop that turns real production failures back into simulations you can regression-test forever. If a voice agent fails, it usually fails in the audio layer and on the edge cases a transcript never shows — and that's exactly where Roark is built to look. The rest of this piece is the evidence.
Simulation testing: real calls, not a loopback
A simulation is only as trustworthy as the path it runs over. Reading the transcript of a simulated conversation tells you the model produced sensible words; it tells you nothing about endpointing, barge-in, dead air while a tool runs, or how the agent sounds under a real accent. Roark simulates the whole path.
- Real telephony. Phone simulations place actual calls over the PSTN from leased numbers, so your agent experiences real audio, real latency, and real interruptions — not a synthetic in-process handoff. Agents on WebRTC stacks (LiveKit, Pipecat) are tested over WebRTC directly.
- Structured, reusable tests. Simulations are built from personas, scenarios, run plans, and schedules — so "caller wants to add a driver mid-term," "caller starts in Spanish then switches to English," and "caller talks over the agent" are named, versioned tests with explicit pass criteria, not one-off demos.
- 45 languages and accents. Simulated callers — and the scoring that grades them — cover 45 languages and accents, so you know the accuracy delta between your best and worst accent bucket before a customer finds it.
- Runs in CI. Inbound and outbound are both supported, triggered automatically over HTTP — so a prompt change that regresses your booking flow fails the pipeline instead of a customer.
This is the layer teams under-invest in and Roark over-invests in. If your evaluation of "did it pass" is a transcript diff, you will ship agents that read fine and sound broken.
Audio-native evaluation
Most voice-agent failures that make customers hang up never appear in a transcript. The agent talked over the caller. It mispronounced the customer's name. The pacing dragged, the caller's voice tightened with frustration, and the words on the page still looked fine. Roark runs 64+ built-in metrics plus unlimited custom metrics, and the differentiating ones are audio-native models that score the sound itself: pronunciation, accent clarity, emotion, vocal stress, pace and pauses, and interruptions.
It's the first question we'd put to any platform in a bake-off: show me a failure you caught that the transcript alone would have missed. That's the failure class that costs you customers, and it's the one audio-native scoring is built for.
Production replay: real failures become regression tests
Simulations catch the failures you thought to write. Real callers generate the ones you didn't. Roark's signature loop closes that gap: a real call goes wrong, Roark captures it, and you re-run that exact interaction against your updated agent until it passes — then keep it in the suite so the same failure can't ship twice. Production traffic continuously feeds your test suite instead of just being watched.
You don't have to take our word for the distinctiveness of that loop. Speechmatics' independent platform roundup singles Roark out precisely because it converts real-call failures into repeatable regression tests. It's the mechanism that makes every incident permanent coverage rather than a one-time firefight.
Reporting you can actually act on
Simulating and scoring only matters if the results are legible to the people who have to act on them. Roark turns every run and every live call into reporting your whole team can use: dashboards with configurable widgets, saved reports over your metric suite, and issues filed automatically the moment a call fails a metric or a check — so a regression is a ticket with the call attached, not a Slack message someone half-remembers. Calls also expose OpenTelemetry (OTEL) traces, so the same evidence lands in the observability stack your engineers already live in.
The point is that Roark isn't a black box that says pass or fail. Product, engineering, and compliance look at the same scored calls, the same trend lines, and the same filed issues — which is what makes the "is this agent ready to ship?" conversation an evidence review instead of an argument.
Integrations, documented in public
Roark's integration paths are public docs pages, each with its own mechanics and stated limits — you can inspect exactly how a connection works before you ever talk to us:
- Vapi and Retell: API-key flows that sync your agents, system prompts, phone numbers, transcripts with speaker labels, and tool invocations. Historical calls import at setup; new calls sync automatically.
- LiveKit: a webhook integration for LiveKit Cloud, or a Python SDK for self-hosted LiveKit Agents that streams stereo audio to Roark from inside your worker.
- Pipecat: a code-level observer you splice into your pipeline — call lifecycle, per-turn transcripts, tool invocations, and merged stereo recordings, self-hosted or on Pipecat Cloud.
- Bland, ElevenLabs, and custom stacks: Bland syncs on a recurring pull schedule, ElevenLabs has its own documented integration, and any other stack can send calls through Roark's API and Node or Python SDKs.
Compliance and security
Roark holds SOC 2 Type II, offers a HIPAA BAA, undergoes annual penetration testing, and has ISO 27001 certification in progress (not yet certified — we'd rather you hear that from us). Enterprise controls include SSO/SAML with Okta SCIM, role-based access, configurable data retention, IP whitelisting, and PII redaction. Details are on our security page. If you're deploying voice agents in healthcare or another regulated vertical, a signed BAA is usually the first procurement gate — so settle it early with any vendor you evaluate.
Side by side
A straight read on the dimensions that decide a voice-agent testing platform. Roark documents all of these publicly — you can verify every cell in our column against our docs. Where Coval doesn't publish the detail, we say so rather than guess; that transparency gap is itself worth weighing when you buy.
| Dimension | Roark | Coval |
|---|---|---|
| Simulation over real telephony (PSTN) | Yes — real calls from leased numbers, plus WebRTC for LiveKit/Pipecat | Not publicly documented |
| Audio-native metric models | Yes — pronunciation, emotion, vocal stress, pace/pauses, interruptions (64+ metrics) | Not publicly documented |
| Production replay → regression tests | Yes — failed live calls replayed against updated agents, kept as tests | Not publicly documented |
| Reporting & dashboards | Dashboards + widgets, saved reports, auto-filed issues, OTEL traces | Not publicly documented |
| Simulation languages & accents | 45 languages & accents, in simulation and scoring | Not publicly documented |
| Per-platform integration docs | Yes — public docs for Vapi, Retell, LiveKit, Pipecat, Bland + API/SDKs | Not publicly documented |
| Compliance | SOC 2 Type II, HIPAA BAA, annual pen tests, ISO 27001 in progress | SOC 2 referenced; HIPAA not stated publicly |
How Roark and Coval differ
Coval is a real company in this category, and we won't pretend otherwise. The honest distinction is one of depth and provenance. Coval leans on structured human review as a named part of its product; if a manual QA queue is a hard requirement for your team, ask both vendors to demo it.
Roark leans the other way — toward automated, audio-native evaluation and a production-fed test loop, because that's what scales past the 1–2% of calls a human team can review by hand. If your bottleneck is catching failures at production volume, in the audio layer, and turning them into permanent tests, that's the workflow Roark is built around. Much of Coval's technical detail isn't published, so on the dimensions above we can only report what each side documents — which is itself a reason to run the comparison on your own traffic rather than a marketing page.
When Roark is the right call
- You want simulation you can trust. Real calls over the PSTN and WebRTC, structured personas and scenarios, 45 languages, and runs that gate CI — the failures surface before launch, not after.
- Your failures live in the audio layer. Interruptions, pronunciation, pacing, emotional escalation — if transcript-level review keeps telling you calls were fine when customers clearly weren't, audio-native metrics are the fix.
- You're in production and want failures to become regression tests. Every live call scored, breaks filed as issues, and failed calls replayable against your next agent version.
- You're in a regulated vertical. SOC 2 Type II plus a HIPAA BAA and annual pen tests clears procurement in healthcare and financial services without a compliance workaround.
- You run Pipecat or self-hosted LiveKit. A documented code-level observer and a self-hosted SDK mean the integration path for open-source stacks is public and inspectable.
What Roark doesn't do
Credibility is the point of a comparison, so here are the limits we'd want to know as a buyer:
- Roark doesn't fix your agent for you. It finds failures, measures them, and makes them reproducible — the fix is still an engineering change you ship. Any platform claiming it automatically repairs agents deserves skepticism.
- Integrations have documented boundaries. The LiveKit integration doesn't sync agent prompts or worker source code (agent names only). The Pipecat observer only sees what it forwards from your pipeline. Bland calls must be created with recording enabled and a persona ID, or they can't be matched to an agent. These are stated in the docs rather than discovered in week two.
- ISO 27001 is in progress, not certified. SOC 2 Type II and the HIPAA BAA are in hand today.
Frequently asked questions
Is Roark a simulation tool or a monitoring tool?
Both, and simulation is where it's deepest. Roark runs pre-launch simulations over real phone calls and WebRTC, scores every production call against 64+ audio-native metrics, and closes the loop by replaying failed live calls as regression tests. Treating it as "just observability" is out of date — the past year of the product has been built around simulation testing.
Does Roark support Pipecat and self-hosted LiveKit agents?
Yes, and it's one of Roark's strongest areas. Pipecat is a code-level observer that captures transcripts, tool calls, and stereo recordings, self-hosted or on Pipecat Cloud. LiveKit has both a webhook integration for LiveKit Cloud and a Python SDK for self-hosted agents. Simulations reach both stacks over WebRTC — no phone number required.
Which platform is better for healthcare voice agents?
Roark publishes a HIPAA BAA alongside SOC 2 Type II. Coval's public pages don't state HIPAA support as of this writing, so you'd need to confirm it with them directly. For most healthcare buyers the BAA is a gating requirement, so resolve it first.
What's the fastest way to decide?
Run a bake-off on your own traffic. Take your ten worst production calls from last month, put them through each platform's evaluation, and compare what each one actually catches — then run the same simulation scenario against your staging agent on both. For Roark, reach out at support@roark.ai or book a demo and bring a recording — we'll score it live.