Find the moments that matter.
Target skills and edge cases remain buried across large, inconsistent multimodal collections.
Now in private beta
Bring together multimodal data, models, evaluation, and verification in one connected platform to build and continuously improve Physical AI systems.
Physical AI workflowActive
01Real-world data connectedMultimodal
02Relevant data curatedIndexed
03Models evaluatedCompared
04Models run at scaleProcessed
05Outputs verifiedTrusted
Trusted results ready for downstream use.
Built for Physical AIMultimodal data. Models. Evaluation. Verification.
The Physical AI bottleneck
Real-world data does not arrive ready to use.
Physical AI teams have more real-world multimodal data than ever. The hard part is finding what matters, determining which models work on proprietary inputs, and knowing which outputs can be trusted.
Know what works on your data.
Public benchmarks cannot tell you which model and configuration performs best in your environment.
Know what you can trust.
Unverified model outputs can introduce errors that compound across downstream datasets, analytics, and Physical AI systems.
One continuous workflow
From real-world data to trusted outcomes.
Connect your data and infrastructure, curate collections, evaluate models, run validated configurations at scale, verify outputs, and deliver trusted results in one repeatable workflow.
Connect
Bring existing multimodal data, connect customer storage, or source new collections.
Curate
Find, organize, and select relevant data slices.
Evaluate
Compare models on identical proprietary inputs.
Run at scale
Apply validated configurations across complete collections.
Verify
Route selected outputs to domain experts or specialized verification systems.
Deliver
Send trusted datasets, analytics, and structured results downstream.
The platform
Bring your data, models, and infrastructure.
Connect customer-controlled storage or use Labelbees-managed storage. Curate multimodal collections, evaluate models on proprietary inputs, run validated configurations at scale, and verify or analyze outputs.
Collections
Search multimodal collections by meaning.
Manage data, inspect embedding-based groupings, run natural-language and similarity searches, and select relevant slices for the next workflow.
⌕ person assembling a small connector
Match 98%
Match 94%
Match 91%
Match 88%
Match 84%
Match 79%
Your infrastructure, your choice
Fit Labelbees into your existing stack.
Adopt Labelbees without rebuilding your existing stack. Choose where your data lives, which models and services participate, and how the platform is deployed.
Your data, where you need it
Use Labelbees-managed storage or connect customer-controlled cloud storage.
Bring your models
Use Labelbees-managed models, supported third-party providers, or your own custom models and endpoints.
Customer-controlled access
Connect storage, model providers, and external services using credentials controlled by your organization.
Enterprise deployment options
Use the managed platform or deploy in a private cloud or on premises for qualified programs.
Data use
Your data is not used to train our models.
Labelbees does not use customer data or outputs to train our models. Data is sent to customer-selected model providers only when the customer configures and uses them.
What you can build
Built around your outcome.
Start with the result your team needs. Labelbees connects the data, models, workflows, and verification required to deliver it.
Build trusted datasets.
Curate, structure, and verify real-world multimodal data for training, evaluation, and downstream workflows.
Curated · Structured · Verified · Ready
Find what works. Run it at scale.
Compare models on proprietary inputs, identify failures, and run validated configurations at scale.
Compared · Validated · Scaled · Monitored
Turn real-world data into decisions.
Turn multimodal data into searchable events, structured signals, and analytics for downstream systems.
Indexed · Analyzed · Structured · Delivered
FAQ
Common questions.
No. Labelbees works with existing multimodal data as well as new data as it is collected.
No. Labelbees is designed to work with your existing stack. Connect your storage, models, providers, and services, or use Labelbees-managed components where needed.
Use Labelbees-managed models, supported third-party providers, or your own custom models and endpoints. Prompts, parameters, and configurations can be saved and reused across workflows.
Route selected outputs through domain experts and specialized verification systems. Verification follows your guidelines, with results accepted, corrected, or escalated as required.
Yes. Labelbees supports tenant-isolated accounts, customer-controlled storage and credentials, user access management, and managed, private-cloud, or on-premises deployment options for qualified programs.
Labelbees is built for teams developing and operating Physical AI systems — across robotics, embodied AI, and other real-world applications.
Private beta
Build trusted Physical AI from real-world data.
Tell us what your team is building, the data you’re working with, and the outcome you want to achieve. Our team will follow up directly.