Today, we’re opening access to Labelbees in private beta—an operating platform for teams building and operating Physical AI systems with real-world multimodal data.
Teams building Physical AI depend on video, images, audio, telemetry, and sensor data from the physical world. But turning that raw activity into data and outputs that models and teams can use requires stitching together separate systems for storage, search, inference, evaluation, processing, analytics, and review.
Building Physical AI requires more than models.
Real-world data is large, multimodal, and highly specific to the environment where a system must work. Valuable behaviors and edge cases are often buried across collections. Generic benchmarks cannot tell a team which model or configuration performs best on its own real-world inputs. Unchecked model outputs can introduce errors that compound across downstream datasets, analytics, and automated systems.
Teams need more than an annotation tool or a model endpoint. They need a continuous way to turn proprietary real-world data into trusted datasets, evaluate models against their own environments, verify outputs, identify failures, and feed those signals into the next iteration.
One connected platform, from real-world data to trusted outcomes.
Labelbees connects the stages that real-world AI teams otherwise assemble themselves. Bring existing multimodal data, connect customer-controlled storage, and move from curation and model evaluation through processing, verification, and delivery in the same environment.
Bring multimodal data from existing datasets, customer-controlled storage, or connected collection workflows.
Organize collections and find target slices using natural-language, similarity, and embedding-based search.
Run your models, supported third-party models, or Labelbees-hosted models on identical proprietary inputs and compare results.
Apply validated model and prompt configurations across complete collections with repeatable batch workflows.
Route selected outputs to domain experts, trained reviewers, or specialized verification systems.
Deliver trusted datasets, analytics, and structured results to downstream systems and workflows.
Because every stage remains connected, teams can preserve context between source data, model configurations, and results, selectively verify uncertain outputs, and feed what they learn into the next iteration. The workflow becomes a continuous improvement loop rather than a collection of disconnected tools.
Start with the outcome your team needs.
Labelbees supports different outcomes from the same real-world multimodal foundation, from building trusted datasets to evaluating models and generating operational intelligence.
Build trusted datasets.
Curate, structure, and verify real-world multimodal data for training, evaluation, and downstream workflows.
Find what works.
Compare models and configurations on proprietary real-world data, understand where they succeed or fail, and run validated configurations at scale.
Turn data into decisions.
Turn multimodal data into searchable events, structured signals, and analytics for downstream systems.
Verification is built into the workflow where additional confidence is required. Teams can define their own criteria and route selected outputs to domain experts, trained reviewers, or specialized verification systems before those results enter training or operational workflows.
See the Labelbees platform in action.
Follow a real-world multimodal workflow from connected data to evaluated models, verified outputs, and downstream results.
Operate on your data and infrastructure.
Use Labelbees-managed storage or connect supported cloud storage for in-place indexing. Run supported models, connect model providers with your own credentials, or bring custom endpoints. Labelbees is available as a managed platform, with private-cloud and on-premises deployment options for qualified programs.
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.
Why private beta?
The platform is available today, but the right starting point depends on each team’s data, infrastructure, quality requirements, and desired outcome. We’re opening access to a small number of teams building and operating Physical AI systems with concrete real-world workflows.
Accepted teams can connect their data and storage, curate collections, evaluate and run models, generate analytics and structured outputs, and add verification where needed.
Build and improve Physical AI on your real-world data.
Tell us what your team is building, what data you have or need, and the outcome you want to achieve.