Panels
Custom audio datasets for training and evaluating voice AI, scoped to your model's spec.
Pick Panels when your failure mode is data fit, not data volume. The published process is the differentiator: Research turns your deployment conditions into acceptance criteria, Collect runs recording, QA, transcription and review in-house with samples shared before volume scales, and Iterate expands coverage into accents, noise conditions and long-tail scenarios. Exclusivity rights and multilingual speaker-separated audio matter if a shared corpus would put your data in a competitor's training mix. Pass if you need audio today from a self-serve catalog — bespoke samples alone are quoted at roughly two weeks, and off-the-shelf corpora remain the cheaper route for generic, non-exclusive
Verified 5d ago · liveness 54/100 · cite: rightaichoice.com/tools/panels
- Voice AI labs whose model underperforms on specific accents, noise conditions or domains
- Startups building voice assistants that need training audio matched to a narrow use case
- Teams evaluating turn-taking behavior in task-driven human-agent dialogue
- Buyers who need exclusive rights to audio rather than a shared corpus
- Solo developers or weekend prototypes running on open-source corpora with no data budget
- Teams whose timeline can't absorb a scoping cycle and roughly two weeks for bespoke samples
- Projects where generic, non-exclusive audio is good enough
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Skip Panels if you need a public dataset to download today from a self-serve catalog, or if your model's problem is data volume rather than a specific accent, noise condition or turn-taking gap.
Dataset work is quoted per project, so cost scales with the number of accents, environments and edge cases you add in the Iterate phase rather than staying fixed.
This is a project-scoped data engagement, so budget expectations sit well above downloading an open-source audio corpus and below running your own field-recording operation with a temp team. It fits funded voice labs and seed-to-Series-B startups with a defined data gap; teams with no data budget, or those who only need generic non-exclusive audio, are better served by public corpora.
In short
Panels — Custom audio datasets for training and evaluating voice AI, scoped to your model's spec. Best for Voice AI labs whose model underperforms on specific accents, noise conditions or domains, Startups building voice assistants that need training audio matched to a narrow use case, Teams evaluating turn-taking behavior in task-driven human-agent dialogue. Contact Sales pricing.
What people actually say about Panels — is it worth it?
We scanned public community sources for Panels on Jul 3, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Panels? 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
- High-Quality Speaker-Separated Audio dataset (proprietary, multilingual, speaker-separated)
- Single Speaker Scripted audio across diverse recording environments
- Turn Taking Evaluation dataset for human-agent turn-taking models
- Design Your Own fully bespoke dataset scoping
- Multilingual audio coverage across diverse topic domains
- Research phase defining use cases, speakers, environments and edge cases
- Acceptance criteria written into the dataset spec before collection
- In-house end-to-end collection, production, QA and transcription
- Checkpoints and samples shared before scaling to full volume
- Iterative coverage expansion into new accents, noise conditions and domains
- Long-tail and edge-case audio collection for real-world scenarios
- Exclusive rights to audio data available alongside purchase
- Free samples for all off-the-shelf datasets
- Bespoke project samples typically delivered within two weeks
- Dataset evolution tied to model results and real-world feedback
About Panels
Panels creates high-quality audio datasets for training and evaluating voice AI models. The company works with frontier voice labs and early-stage startups, and its model is spec-first rather than catalog-first: the team studies how your model will be deployed, defines target use cases, speakers, environments and edge cases, then translates those into explicit dataset requirements and acceptance criteria before recording starts. Four dataset types are published. High-Quality Speaker-Separated Audio is a proprietary large-scale multilingual set with speaker-separated audio across diverse topic domains. Single Speaker Scripted covers single-speaker audio across a range of recording environments. Turn Taking Evaluation is a multilingual set for evaluating human-agent turn-taking models in task-driven, real-world scenarios. Design Your Own covers anything outside those three — you describe the need and Panels scopes it. Delivery runs as a three-phase loop: Research (define use cases, speakers, environments, edge cases, acceptance criteria), Collect (end-to-end collection, QA, transcription and review handled in-house, with checkpoints and samples shared so you can validate direction before scaling), and Iterate (expand into new accents, noise conditions, domains and long-tail scenarios as your model ships). Samples are offered for all off-the-shelf datasets, and bespoke project samples are typically delivered within two weeks. Exclusivity rights are available alongside the audio, which matters if you don't want the same recordings in a competitor's training mix. This is a fit for teams whose bottleneck is data fit — a specific accent, noise profile or turn-taking behavior the model keeps getting wrong — rather than teams who need a download today.
Behind the Verdict
Panels sells a service, not a shelf. The homepage lists four dataset types — High-Quality Speaker-Separated Audio (proprietary, large-scale, multilingual, speaker-separated across diverse topic domains), Single Speaker Scripted (single-speaker audio across a range of recording environments), Turn Taking Evaluation (multilingual, built for evaluating human-agent turn-taking in task-driven real-world scenarios), and Design Your Own (anything outside those three) — but the real product is the process wrapped around them. Research converts "what is your model getting wrong" into named speakers, environments, edge cases and acceptance criteria. Collect does recording, QA, transcription and review in-house and shares checkpoints and samples along the way, which is the part that de-risks a six-figure data spend: you can redirect before volume scales instead of discovering the spec was wrong after delivery. Iterate then grows coverage against actual model results — new accents, noise conditions, domains, long-tail scenarios.Strengths, concretely: (1) spec-driven scoping rather than a fixed bundle; (2) in-house QA, transcription and review, so quality control isn't outsourced to a recording vendor; (3) early checkpoint and sample sharing; (4) exclusivity rights purchasable alongside the audio; (5) samples for every off-the-shelf dataset and bespoke samples in about two weeks. Those last two are the fastest way to evaluate the vendor — request a sample of the closest off-the-shelf set and compare it against whatever open-source corpus you're already training on. Lead times and scalability for large multi-language programs aren't documented either, so a large lab should pin those down in scoping.Where it fits: voice labs whose model underperforms on a specific accent, noise condition or domain, teams needing evaluation data for turn-taking behavior rather than more training hours, multilingual speech programs that need speaker-separated recordings, and buyers for whom exclusivity is a requirement. Where it doesn't: solo developers and weekend prototypes running on free corpora, projects where generic non-exclusive audio is good enough, and anyone whose timeline can't absorb the sample-and-scope cycle.
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Real-world workflow fit
Concrete scenarios for the personas Panels actually fits — and what changes day-one when you adopt it.
You describe how the model is deployed and which accents, environments and edge cases are failing. Panels turns that into target speaker and environment definitions plus acceptance criteria, then starts collection with in-house QA, transcription and review.
Outcome: Checkpoints and samples arrive before volume scales, so you can redirect the spec early, and the finished speaker-separated audio matches your deployment conditions instead of a generic accent mix.
You license the Turn Taking Evaluation set — multilingual audio covering task-driven, real-world scenarios — to test how your agent handles interruption, overlap and handoff with a human speaker.
Outcome: You get evaluation data targeted at turn-taking behavior specifically, rather than re-purposing general speech corpora and hoping the failure modes show up.
Your data need doesn't map to any published set, so you use Design Your Own: you describe speakers, environments, languages and edge cases, and Panels scopes it and delivers samples for validation.
Outcome: Bespoke samples land in roughly two weeks, letting you confirm the direction before committing to full-scale collection, with exclusivity available if the audio must stay out of competitors' training data.
Use Cases
- Train a multilingual voice model with speaker-separated audio across topic domains
- Evaluate human-agent turn-taking behavior using task-driven, real-world scenarios
- Fill an accent, noise-condition or domain gap your model keeps failing on
- Buy exclusive audio so the same recordings don't appear in a competitor's training set
- Commission single-speaker scripted recordings across specific recording environments
- Scope a bespoke dataset when no published set matches your deployment conditions
- Refresh and expand an existing training set as the model and product evolve
Limitations
- Panels delivers audio to a spec rather than selling a downloadable bundle, so engagement runs through the team and timelines depend on scoping.
- The public site doesn't publish dataset sizes, language counts, hours of audio, licensing terms or turnaround for full datasets, and it doesn't state lead times or scalability for large multi-language programs — a big lab should pin those down during Research.
- Off-the-shelf datasets can be sampled immediately, but bespoke samples are quoted at roughly two weeks, and full delivery follows the Research-Collect-Iterate loop rather than an instant download.
- Dataset generation is a capital line item, not a subscription, so budget accordingly.
as of 2026-10-04
Verification history
We have re-verified Panels 9 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-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — 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 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Panels's pricing actually pencils out — and where peers do it cheaper.
This is a project-scoped data engagement, so budget expectations sit well above downloading an open-source audio corpus and below running your own field-recording operation with a temp team. It fits funded voice labs and seed-to-Series-B startups with a defined data gap; teams with no data budget, or those who only need generic non-exclusive audio, are better served by public corpora.
Setup time & first value
How long it actually takes to get something useful out of Panels — broken out by persona, not the marketing-page minute.
Off-the-shelf datasets can be sampled right away. Bespoke projects run through the process: Research scoping (use cases, speakers, environments, edge cases, acceptance criteria) happens first, then bespoke samples are typically delivered within about two weeks, with full collection, QA and transcription following before delivery.
Switching to or from Panels
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From open-source audio corpora: use Panels' off-the-shelf samples to benchmark against your current corpus, then scope a set covering the accents or noise conditions the free data misses.
- →From a generic recording vendor: replace the fixed bundle with Research-phase acceptance criteria and in-house QA, transcription and review.
- →From a shared commercial dataset: add exclusivity rights so the same audio isn't in a competitor's training mix.
- ↗To open-source audio corpora: keep the spec Panels produced — speakers, environments and acceptance criteria — and apply it to free data where fit is less critical.
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Panels”, and we withheld 6: 6 could not be judged, because “Panels” 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 Panels.
Official links
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Krisp
Krisp pairs real-time AI noise cancellation with a bot-free AI note taker, accent conversion, and voice translation for calls.
Featured Head-to-Head Comparisons
Panels vs Screenplayiq
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Panels vs Geologicai
GeologicAI and Panels serve entirely different markets: mining vs. voice AI. Choose GeologicAI if you need rapid, AI-driven core scanning for critical minerals. Choose Panels if you require custom, high-quality audio datasets for voice model training. They are not substitutes.
Panels vs Versatile
Versatile and Panels serve entirely different markets: Versatile is a niche crane intelligence platform for steel erectors, while Panels provides custom audio datasets for voice AI. Choose Versatile if you manage crane operations and need real-time pick tracking without workflow changes; choose Panels if you develop voice models and need tailored, high-quality training data. They are not direct competitors.
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Fish Audio
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