Panels
Custom audio datasets for voice AI training and evaluation
Panels delivers what its name promises: production-ready, curated audio data that most teams can't source internally. The consultative model and iterative coverage beats off-the-shelf datasets, but the contact-only pricing and lack of self-service gate access. If you have budget and a specific voice AI use case, it's worth the conversation; otherwise, look at open-source or self-recorded alternatives.
Verified 4d ago · liveness 58/100 · cite: rightaichoice.com/tools/panels
- Voice AI researchers needing specialized training data
- Startups building voice assistants with unique use cases
- Enterprise teams evaluating turn-taking models
- Developers seeking multilingual audio datasets for esoteric domains
- Users seeking free or self-service audio data
- Teams requiring real-time voice data collection
- Those needing pre-built off-the-shelf datasets without customization
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Skip Panels if you need immediate, low-cost audio data with self-service access, or if your project budget can't accommodate a consultative engagement with custom data collection.
No public pricing; bespoke projects likely require a significant budget and a long-term commitment.
Panels' contact-only pricing reflects its consultative, high-touch service, which typically suits funded startups and enterprise labs with dedicated budgets. Compared to self-service marketplaces like Scale AI or open-source datasets, Panels is at the premium end, but you get tailored data and ongoing iteration.
In short
Panels — Custom audio datasets for voice AI training and evaluation. Best for Voice AI researchers needing specialized training data, Startups building voice assistants with unique use cases, Enterprise teams evaluating turn-taking models. Contact Sales pricing.
What people actually say about Panels — 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.
60 mentions across 3 sources (Hacker News, App Store, Lemmy) · researched Jul 3, 2026.
- +Offers high-quality speaker-separated audio datasets.
- +Provides custom dataset design tailored to client needs.
- +End-to-end in-house audio collection and production.
- +Supports multilingual turn-taking evaluation datasets.
- +Rigorous QA and transcription review process.
- −No community feedback or independent reviews available.
- −Unclear pricing model—contact required for any estimate.
- −Name clashes with popular comic reader app.
- −Unknown reliability at scale without user testimonials.
- −Limited integration details—no APIs or platform support listed.
- • No public pricing—custom quotes may vary widely
- • Potential minimum order size or project fee
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: August 2026
How we score →Key Features
- Speaker-separated audio datasets
- Multilingual turn-taking evaluation datasets
- Single-speaker scripted audio across diverse environments
- Custom dataset design tailored to client needs
- End-to-end in-house audio collection and production
- Rigorous QA, transcription, and review process
- Checkpoint and sample sharing for early validation
- Coverage and performance iteration over time
- Large-scale proprietary multilingual dataset
- Exclusive dataset rights available
- Iterative expansion into edge cases (accents, noise, domains)
About Panels
Panels is an audio data platform for voice AI teams building and shipping better voice models. It creates high-quality, curated datasets for training and evaluation, working closely with frontier voice labs and early-stage startups. The platform offers proprietary multilingual speaker-separated audio, single-speaker scripted recordings across diverse environments, and turn-taking evaluation datasets. Panels follows a consultative process: research to understand your use case, in-house collection with rigorous QA, and iterative expansion to cover edge cases. It supports custom dataset design, sample sharing for early validation, and ongoing coverage improvement based on model results. Unlike generic data marketplaces, Panels provides exclusive, tailored datasets with a hands-on partnership approach, making it ideal for teams that need production-ready data that evolves with their product.
Behind the Verdict
Panels is a boutique audio data provider that focuses on quality over quantity. The homepage emphasizes a hands-on, consultative approach: they research your use case, collect data in-house, and iterate based on model results. This is a stark contrast to large-scale data marketplaces where you get what you pay for without much tailoring. If you are building a voice assistant for a niche domain—like medical dictation with specific accents or a multilingual customer service bot—Panels can craft datasets that directly address your edge cases. Their process includes sharing checkpoints and samples within two weeks for bespoke projects, which lets you validate early. They also offer exclusivity rights, which is a big deal if you want to keep your training data unique. However, there are notable gaps. There's no public pricing, no self-service tier, and no way to browse or download off-the-shelf datasets instantly. This means Panels is not for hobbyists or early-stage startups with tiny budgets. The lack of transparent pricing also makes it hard to compare costs with alternatives. If you need data quickly and cheaply, look at open-source datasets like Common Voice or LibriSpeech, or record your own. But if you need high-quality, tailored audio for a production voice AI, Panels' consultative model could be worth the price. Where Panels really shines is in its iterative coverage improvement. They don't just hand you a dataset and disappear; they help you expand into new accents, noise conditions, and long-tail scenarios as your model evolves. This is a distinct advantage over static datasets. The trade-off is that it's a long-term partnership, not a quick fix. So, if you're planning a multi-phase voice AI rollout, Panels can grow with you. But if you need a one-off dataset for a proof-of-concept, you might find the process too heavy.
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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.
Building a multilingual customer support voice assistant and needing training data for handle various accents.
Outcome: Engage Panels for a custom dataset; receive samples within two weeks, validate, then scale collection with rigorous QA and iterative edge-case expansion.
Evaluating turn-taking models for a new conversational agent product.
Outcome: Use Panels' turn-taking evaluation dataset to benchmark model performance across task-driven scenarios, with options for exclusivity to safeguard competitive advantage.
Wanting off-the-shelf speaker-separated audio for a research project but lacking budget for custom collection.
Outcome: Request samples of Panels' proprietary multilingual dataset; if cost is prohibitive, explore open-source alternatives like Common Voice for initial prototyping.
Use Cases
- Train a multilingual voice assistant with speaker-separated audio datasets
- Evaluate human-agent turn-taking models with task-driven scenarios
- Curate custom audio recordings for specific speaker environments
- Improve model robustness by iterative data collection and QA
- Build production-ready voice AI with in-house collected and transcribed data
- Collect edge-case audio (accents, noise conditions, domains) for model gaps
Limitations
- No public pricing or self-service tier; requires contact and likely significant budget.
- Dataset availability and lead times are not specified, so scalability for large projects may be uncertain.
- No off-the-shelf datasets available for immediate download; bespoke projects involve a research phase and samples within two weeks, so not suitable for instant needs.
as of 2026-08-18
Verification history
We have re-verified Panels 6 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-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-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
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.
Panels' contact-only pricing reflects its consultative, high-touch service, which typically suits funded startups and enterprise labs with dedicated budgets. Compared to self-service marketplaces like Scale AI or open-source datasets, Panels is at the premium end, but you get tailored data and ongoing iteration.
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.
First value: samples within two weeks for bespoke projects. Full dataset delivery depends on scope, but iterative checkpoints allow early validation.
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 Common Voice: If you need higher-quality, tailored data, Panels can supplement with custom recordings and edge-case expansion.
- ↗To Open Source: If budget becomes a constraint, you can pivot to open-source datasets like Common Voice, though you'll lose the customization and iteration.
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Panels
Common stack mates teams adopt alongside Panels, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Panels vs Screenplayiq
ScreenplayIQ and Panels serve entirely different domains—one for script marketability analysis, the other for custom voice AI datasets. Choose ScreenplayIQ if you're a screenwriter or producer needing data-driven feedback on feature film scripts. Choose Panels if you're building voice AI models and require high-quality, tailored audio training data. There is no overlap in use cases.
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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