Roark
Voice AI QA and evals: simulate callers before launch, then score every production call on 500+ audio-native metrics.
Roark is the reference implementation for voice-agent QA. The audio-native metric layer is what transcript-graders can't fake — pronunciation, vocal stress, barge-in and interruption scoring run on the waveform, not on a text rendering of it — and the simulate-replay-verify loop makes a fix provable rather than anecdotal. Pick it if you run production voice traffic on Vapi, Retell, LiveKit or Pipecat and need evidence a prompt or model change actually helped. Compare it against Coval or Cekura if you also want transcript-grade LLM evaluation, and against a spreadsheet of call recordings if your volume is tiny enough that pay-as-you-go minutes never pay off.
Verified 57m ago · liveness 73/100 · cite: rightaichoice.com/tools/roark
- Teams running production voice agents on Vapi, Retell, LiveKit, Pipecat or a custom stack
- QA and platform engineers who need repeatable simulation suites and CI quality gates
- Regulated voice deployments in healthcare, finance or insurance needing compliance evidence per call
- Product managers who want a scored post-deployment scorecard instead of anecdotal call reviews
- Teams still shopping for a voice agent builder — Roark tests agents, it does not build or host them
- Chat-only teams with no voice or telephony surface
- Anyone expecting value without handing over call recordings, agent IDs and a scoring rubric
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip Roark if you don't yet have a running voice agent or you have no monthly call volume worth scoring — Roark measures and improves agents you already run, it doesn't build them.
Simulation is billed at $0.15/min on pay-as-you-go plus the telephony and STT/TTS provider costs for every call Roark places, so your effective per-minute rate is higher than the headline number.
Roark is priced as usage in plain dollars and fits teams whose monthly voice spend is real but not enormous: pay-as-you-go makes sense under roughly $500/mo of usage, Team at $500/mo (included usage at $0.10/min simulation, $0.02/metric/min) around 3,300+ simulation minutes a month, and Enterprise from $4,000/mo committed when you need volume rates, SSO, data residency, a DPA, an SLA or invoicing. For comparison, transcript-grading tools like Coval or Cekura price differently and Roark is
In short
Roark — Voice AI QA and evals: simulate callers before launch, then score every production call on 500+ audio-native metrics. Best for Teams running production voice agents on Vapi, Retell, LiveKit, Pipecat or a custom stack, QA and platform engineers who need repeatable simulation suites and CI quality gates, Regulated voice deployments in healthcare, finance or insurance needing compliance evidence per call. Free to start; paid plans from $500/mo.
What's new in Roark
Checked todayAcross the latest 5 updates: 5 news mentions.
Pre-launch testing for outbound voice AI campaigns
A PM checklist for testing outbound voice campaigns before launch, covering disclosures, voicemail handling, DNC compliance, IVR navigation, warm transfers and write-backs.
Testing full-duplex voice agents
Covers what breaks in full-duplex voice agents, which behaviors to test, and how to simulate them over real phone calls.
Testing agent-to-agent handoffs in voice AI squads
A QA playbook for multi-agent voice architectures covering routing, state-passing and re-entry between specialist agents.
Testing backchannels in voice AI agents
A technical test plan for turn-taking failures in voice agents, focused on backchannels such as 'mhm' and 'uh-huh'.
Testing async tool calls in voice AI agents
Outlines the failure modes introduced when async function calling keeps a voice agent talking while tools run, and what to test.
What people actually say about Roark — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
30 mentions across 2 sources (Hacker News, Lemmy) · researched Jul 3, 2026.
Average across the 2 sources that answered — each source counts once, not each post.
- +Automates test generation from failed production calls.
- +Monitors 40+ metrics including latency and sentiment.
- +Multi-speaker analysis with up to 15 speakers.
- +Configurable personas with gender, accent, noise, emotion.
- +Graph-based conversation flow testing covers edge cases.
- −Replay tests can mismatch when AI logic changes.
- −Limited to four native integrations at launch.
- −As a new startup, long-term stability unproven.
- −Requires SDK work for unsupported platforms.
- −Pricing not public — unclear value for small teams.
- • Extra costs for high call volumes or advanced evaluators not transparent
Viability Score
How well maintained and how widely used is Roark? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Simulation testing against hundreds of realistic caller personas built from your real call types
- Red teaming with adversarial callers probing prompt injection, jailbreaks and social engineering
- Multilingual testing across 45 languages with native accents, code-switching and background noise
- Load testing with hundreds of concurrent callers over real PSTN and WebRTC
- Always-on health checks that catch an outage before a customer does
- Regression testing diffed against your last green baseline on every change
- CI/CD quality gates that run the conversation suite before a prompt or model change merges
- 500+ audio-native metrics: pronunciation, accent clarity, emotion, vocal stress, pace, interruptions
- Conversational metrics: resolution, empathy, task success, hallucination, repetition, tone
- Compliance scoring for disclosures, PII exposure and identity checks
- Latency metrics including time-to-first-word, turn latency, ASR WER and barge-in handling
- Custom metrics scored against your own rubric on every call
- Issue tracker with failure clustering and tracking across deploys
- Alerts and monitors with thresholds on any metric, sent to Slack or webhook
- Prompt optimizer drafting evidence-grounded edits from failing calls
About Roark
Roark is a QA and evals platform for teams that already run voice AI agents — it does not build or host them. If your agents sit on Vapi, Retell, LiveKit, Pipecat or a custom stack, Roark adds the quality layer on both sides of launch. Before launch you point your agent at hundreds of simulated personas built from your own real call types (the angry caller, the rambler, the interrupter) plus adversarial red-team callers probing prompt injection, jailbreaks and social engineering. Suites run across 45 languages with native accents, code-switching and background noise, over real PSTN and WebRTC, with peak-concurrency load tests, always-on health checks and regression diffs against your last green baseline — and they can run in CI so every prompt or model change passes a quality gate before it merges. Once live, every call is scored on 500+ metrics. Where most tools grade a transcript with an LLM, Roark runs purpose-built audio models on the waveform — pronunciation, accent clarity, emotion, vocal stress, pace and pauses, interruptions — alongside conversational metrics (resolution, empathy, task success, hallucination, repetition, tone), compliance checks for disclosures, PII exposure and identity verification, and latency measures like time-to-first-word, turn latency, ASR WER and barge-in handling. Failures are filed and clustered, alerts fire to Slack or webhook, and OTEL traces land in your observability stack. The improvement loop closes in one place: anchored prompt edits drafted from failing calls, replay against the exact callers that broke it, then live verification that the metric actually moved. Recent playbooks target the harder surfaces — full-duplex turn-taking, backchannels, async and slow tool calls, agent-to-agent handoffs, TTS pronunciation, hangup behavior, hallucination testing and treating knowledge-base edits as deploys. It sits beside tools like Coval or Cekura rather than replacing them. Pricing is usage in plain dollars with a $50 starting credit.
Behind the Verdict
The honest framing is that Roark is not an agent builder and doesn't pretend to be. You bring a working voice agent and a speech pipeline; Roark brings the measurement layer that most voice teams assemble badly or skip entirely. Strengths. The metric surface is the differentiator. 500+ metrics ship out of the box across four families — audio-native models (pronunciation, accent clarity, emotion, vocal stress, pace and pauses, interruptions), conversational LLM-plus-rules metrics (resolution, empathy, task success, hallucination, repetition, tone), compliance policy checks (disclosures, PII exposure, identity check, script adherence) and latency (time-to-first-word, turn latency, ASR WER, barge-in handling) — with unlimited custom metrics on your own rubric. Because audio scoring runs on the call itself, Roark catches things a transcript-only grader structurally cannot: a mispronounced drug name, dead air before a response, an interruption that cut off a required disclosure. The pre-launch side is equally concrete: personas built from your real call types, adversarial red-team callers, 45 languages with code-switching and background noise, load tests over real PSTN and WebRTC, and regression diffs against your last green baseline. CI gates mean a prompt change has to pass conversations, not just compilation. Everything is exposed — REST API, MCP server, CLI and SDKs — so QA lives where your engineers already work. Where it fits. Regulated voice deployments (healthcare, finance, insurance) get per-call compliance evidence for disclosures, PII and identity verification, which is hard to assemble from ad-hoc listening. Platform and QA engineers get repeatable suites instead of manually re-dialing an agent after every prompt tweak. Product managers get a scored scorecard rather than anecdotes. The recent playbook cadence — full-duplex turn-taking, backchannels, async and slow tool calls, agent-to-agent handoffs, TTS pronunciation, hangup behavior, hallucination testing, knowledge-base edits as deploys — maps to failure modes that only appear in real telephony. Weaknesses and where it doesn't fit. You must already have an agent, and you must hand over call recordings, agent IDs and a scoring rubric to get value. Teams still choosing a framework should come back later. Chat-only teams with no telephony surface get little from the audio-native models. Costs scale with minutes times metrics — a multi-metric rubric on a high-volume line is a real monthly number, not a rounding error. And the security controls that regulated buyers ask for in procurement (SSO/SAML with SCIM, RBAC, IP whitelisting, data residency, signed DPA, uptime SLA) sit at the Enterprise commitment level, which is a bigger step than a Team plan for a team that only needs the controls and not the volume. Verdict. If you run voice in production and can't currently prove that last week's change helped, Roark is the shortest path to an answer. If you're pre-agent or pre-volume, wait.
Researching Roark? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas Roark actually fits — and what changes day-one when you adopt it.
Reruns the booking_v2 simulation suite in CI against 200 simulated personas including the angry caller and a Gulf Arabic lobby-noise case, then diffs each metric against the last green baseline.
Outcome: One failure (interrupts mid-disclosure, score 61) is filed as an issue before launch rather than discovered by a customer, and the merge is blocked until it passes.
Wires Roark's alerting to Slack and sets thresholds on disclosure, PII exposure and identity-check metrics across every production call.
Outcome: A refund issued to an unconfirmed caller is flagged automatically, with the audio evidence attached to the issue instead of being reconstructed by hand from recordings.
Opens the scored call dashboard, filters to the support_v4 deploy, and reads pronunciation, empathy and resolution trends against the previous version.
Outcome: Pronunciation moved 71 to 78 since deploy with no regressions on other metrics, turning a change-review meeting into a one-screen answer rather than a sample of anecdotes.
Use Cases
- Test a new voice agent flow by simulating angry customer personas with background noise.
- Monitor live calls for compliance deviations and trigger Slack alerts on failures.
- Auto-generate regression tests from failed production calls in one click.
- Compare success rates across agent variants using graph-based conversation flows.
- Analyze multi-speaker conference calls (up to 15 speakers) for talk time distribution.
- Set up dashboards and scheduled reports to track latency and repetition metrics over time.
- Run a full test suite in CI before every prompt or model merge.
- Verify that AI disclosure statements are correctly delivered as state laws vary.
Models Under the Hood
as of 2026-09-27
Limitations
- Roark is a QA, testing and observability platform for voice and chat AI agents — not an agent builder — so you must already have a working agent and speech pipeline (Vapi, Retell, LiveKit, Pipecat or your own stack).
- Pricing is usage-based in plain dollars: $50 of free credit to start, then simulation at $0.15/min plus pass-through provider costs and metric evaluation at $0.04/metric/min on pay-as-you-go; Team at $500/mo is $500 of included usage at $0.10/min simulation and $0.02/metric/min; Enterprise is committed from $4,000/mo.
- Calls Roark places incur provider telephony and STT/TTS costs passed through at cost, which draw from your usage.
- Post-call transcription is $0.024/min on every plan, free when you send your own transcripts.
- Live production calls you already run are billed as metric evaluation only.
- Enterprise-only controls include SSO/SAML with SCIM, RBAC, IP whitelisting, data residency, extended retention, signed DPA and an uptime SLA.
as of 2026-10-08
Verification history
We have re-verified Roark 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Roark tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Pay as you go
$0
Ideal for
Solo builders, new agents and trials with under roughly $500/mo of usage — spiky or low volume, one or two projects, no commitment.
What this tier adds
Starting tier: $50 free credit with no card and no minimum, then usage at $0.15/min simulation and $0.04/metric/min, 1 project, 5 seats, 10 concurrent lines, 45-day trace retention.
Team
$500/mo
Ideal for
Scaling teams around 3,300+ simulation minutes a month whose usage would clear roughly $500/mo.
What this tier adds
Adds $500 of included usage at lower rates ($0.10/min simulation, $0.02/metric/min), 5 projects, 10 seats, 25 concurrent lines, 90-day retention, Slack channel and guided setup onboarding.
Enterprise
From $4,000/mo
Ideal for
Large-scale and regulated teams at roughly $4,000+/mo of usage, or any volume that needs security and contract controls.
What this tier adds
Adds committed volume rates from $0.05/min with volume metric discounts, unlimited projects/seats/concurrency, custom retention and data residency, SSO/SAML with SCIM, RBAC, IP whitelisting, signed DPA, uptime SLA, invoicing and a dedicated engineer.
Where the pricing makes sense
The company stage and team size where Roark's pricing actually pencils out — and where peers do it cheaper.
Roark is priced as usage in plain dollars and fits teams whose monthly voice spend is real but not enormous: pay-as-you-go makes sense under roughly $500/mo of usage, Team at $500/mo (included usage at $0.10/min simulation, $0.02/metric/min) around 3,300+ simulation minutes a month, and Enterprise from $4,000/mo committed when you need volume rates, SSO, data residency, a DPA, an SLA or invoicing. For comparison, transcript-grading tools like Coval or Cekura price differently and Roark is
Setup time & first value
How long it actually takes to get something useful out of Roark — broken out by persona, not the marketing-page minute.
Pay-as-you-go: minutes to a $50 credit and a first simulated call once you connect your agent ID — hours if you're building personas from real call types. Team buyers get guided setup onboarding. Enterprise gets white-glove onboarding and a dedicated engineer. Expect the first honest baseline to take longer than the first run: you need a rubric and labelled ground truth before the metrics mean
Switching to or from Roark
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual QA listening: import your real call types as personas, then replace ad-hoc listening with the same 500+ metric scorecard on every call.
- →From transcript-only LLM grading: add Roark's audio-native metrics alongside your existing transcript scores rather than replacing them outright.
- →From a homegrown simulation harness: port existing test scenarios as simulation suites and run them in CI with regression diffs against a green baseline.
- →From Coval or Cekura: run both in parallel on the same calls and compare per-metric agreement before you consolidate.
- ↗To Coval or Cekura: export issues and scored call metadata via the REST API so historical failure clusters survive a transcript-grading-first stack.
- ↗To a transcript-only evaluator: keep Roark's audio-native findings as a labelled dataset for the new tool's ground truth.
- ↗To manual review: use the issue tracker and saved reports to hand over a prioritised list of recurring failures instead of raw recordings.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Roark”, and we withheld 6: 6 could not be judged, because “Roark” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Roark.
Official links
Tools that pair well with Roark
Common stack mates teams adopt alongside Roark, with the specific reason each pairing earns its keep.
Arize Phoenix
Arize Phoenix is open-source LLM observability: trace every agent step, run evals, and self-host the whole thing.
Galileo AI Evals
AI observability and eval-engineering platform that turns offline evals into live production guardrails.
Galileo
AI observability and eval engineering platform that turns offline evals into live production guardrails for agents and RAG systems.
Featured Head-to-Head Comparisons
Roark vs Locus Robotics
These tools serve completely different domains: Locus Robotics automates physical warehouse tasks with AMRs, while Roark ensures the quality of voice AI agents. Choose Locus if you're scaling eCommerce fulfillment and need flexible, robot-assisted picking; choose Roark if you're building or deploying voice AI agents and need rigorous testing and observability. There's no overlap in function or buyer.
Roark vs Presto Voice
Choose Presto Voice if you run a multi-location QSR drive-thru and need a proven voice AI that automates orders and boosts revenue via upselling — it's built for restaurant operations, not engineering. Choose Roark if you're a voice AI development team shipping agents and need rigorous QA and monitoring across the full lifecycle, from simulated testing to live call observability. They solve entirely different problems — Presto is operational, Roark is technical.
Roark vs Truleo
Truleo and Roark are not competitors—they serve completely different buyers. Truleo is for law enforcement agencies needing to surface leads from jail calls, body cameras, and RMS, while Roark is for voice AI teams needing to test and monitor their conversational agents. Choose based on your domain: police work or voice agent development.
Alternatives to Roark
View all
Arize Phoenix
Arize Phoenix is open-source LLM observability: trace every agent step, run evals, and self-host the whole thing.
Galileo AI Evals
AI observability and eval-engineering platform that turns offline evals into live production guardrails.
Frequently Asked Questions
Best-of guides
Used Roark? Help shape our editorial sentiment research.