BasicAI
BasicAI is a secure, AI-assisted data annotation platform and managed labeling service that transforms raw, multi-modal data—images, 3D LiDAR/point clouds, video, audio, and text—into high-quality tra
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What is BasicAI?
BasicAI is a data annotation platform designed to prepare raw multi-modal data for machine learning. It handles images, 3D point clouds, video, audio, and text, converting them into labelled training datasets. The platform combines automated annotation tools with human expert teams and quality assurance processes targeting 99%+ accuracy. The tool suits organisations building AI models across autonomous vehicles, smart cities, healthcare, retail, and other sectors. You can define custom labelling schemes (ontologies) to match your specific needs, then choose between self-service annotation, managed teams, or a combination. BasicAI emphasises data security with private deployment options and integrations for existing ML workflows. It scales from small pilot projects to large enterprise programmes, with pricing structured around data volume and service level rather than per-seat licensing.
Key Features
Multi-modal data support
annotate images, 3D LiDAR point clouds, video, audio, and text within a single platform
Custom ontologies
define labelling schemas tailored to your specific use case and domain requirements
AI-assisted annotation
automated pre-labelling and suggestions to speed up manual annotation work
Managed labelling service
option to use BasicAI's expert teams rather than handling annotation in-house
Multi-level quality assurance
built-in review and validation workflows targeting high accuracy standards
Private and customisable deployments
on-premise or cloud options for sensitive data and compliance requirements
Pros & Cons
Advantages
- Handles diverse data types in one platform, reducing the need to switch between tools
- Combines automation with human oversight, balancing speed and accuracy
- Flexible service model lets you start small (self-service) or hand off to managed teams as volume grows
- Strong emphasis on security and compliance, with private deployment options for regulated industries
Limitations
- Specific pricing and free tier limits are not publicly detailed, making budgeting uncertain without contacting sales
- Best suited to organisations with substantial labelling needs; smaller projects may not justify the setup overhead
- Steep learning curve for custom ontology design and workflow configuration
Use Cases
Preparing training data for autonomous vehicle perception systems using camera, LiDAR, and radar data
Labelling satellite and aerial imagery for geospatial analysis and smart city applications
Annotating medical imaging datasets for diagnostic AI models whilst maintaining HIPAA compliance
Creating ground truth datasets for retail and logistics computer vision tasks like object detection and tracking
Preparing audio and transcript data for speech recognition and NLP model training