Ocular AI

Ocular AI Platform is a data-focused platform designed to transform unstructured, multi-modal data into golden datasets for generative AI, frontier models, and computer vision. It provides end-to-end data labeling, dataset management, and human-in-the-loop capabilities to accelerate AI development while ensuring security and scalability. The platform is built to integrate with existing tech stacks and scale across startups and enterprises.

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Core Components

• Ocular Foundry: The data engine to transform petabytes of multi-modal, unstructured data into high-quality datasets. Includes dataset versioning, workflow orchestration, and integration points to power AI applications.
• Ocular Bolt: A human-in-the-loop labeling service that accelerates data labeling with expert review, quality control, and feedback loops.
• Infinite Canvas: Visual workflow and annotation canvas that supports collaborative annotation and complex labeling pipelines.
• Dataset Versioning: Track changes across dataset iterations to support model training, reproducibility, and rollback.
• Label Management & Insights: Define label attributes, monitor label distributions, and assess data quality to ensure labeling consistency.
• Collaborative Annotations: Invite internal teams, external annotators, and reviewers to participate in labeling tasks.

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How It Works

1. Ingest multi-modal data (images, videos, text, etc.) into Ocular Foundry.
2. Annotate using Ocular Bolt with collaborative workflows and versioning.
3. Use Infinite Canvas to design and monitor labeling pipelines.
4. Version datasets and extract insights to drive model improvements.
5. Integrate with your existing tech stack while keeping data in your infrastructure.

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Security & Compliance

• Enterprise-grade security with continuous monitoring and audits.
• Data stays within your existing infrastructure and data sources when integrating with external data.
• Scales from startup to enterprise with robust governance.

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Target Users & Use Cases

• AI teams needing scalable, production-grade data labeling for multi-modal data.
• Organizations requiring reproducible dataset versions and rigorous quality control.
• Teams seeking seamless integration with their current tech stack and data pipelines.

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Core Features

• End-to-end data labeling and annotation workflows for multi-modal data
• Foundry: transform unstructured data into golden datasets with versioning
• Ocular Bolt: human-in-the-loop labeling with expert review
• Infinite Canvas: collaborative, visual workflow orchestration
• Dataset Versioning for reproducibility and model iterations
• Label Management & Insights for consistency and quality monitoring
• Collaborative annotations with internal and external participants
• Enterprise-grade security and data-integration capabilities
• Seamless integration with existing tech stacks and pipelines