Ocular AI

Ocular AI is a web and API platform for engineering teams and organizations that need to ingest, curate, search, and annotate large-scale multimodal data (video, images, audio) and turn it into training-ready datasets for custom model development.

Key capabilities include:

• Multimodal Lakehouse and catalog to centralize data from cloud and local sources (AWS, GCP, Azure, Databricks, Snowflake, local storage), organize files and folders, and track data lineage with role-based access control.
• Natural language multimodal search across videos, images, and audio, including confidence scores, filters, and a Search API for embedding retrieval into applications.
• Data annotation at scale with agentic labeling plus humans-in-the-loop, along with project management workflows and dataset versioning for reproducible training sets.
• Model training and evaluation using managed GPU infrastructure, configurable training runs (for example epochs, batch size, metrics), model comparison, and an evaluation playground.

Pricing: Contact for pricing.

Notable for combining data ingestion, multimodal search, labeling, versioned datasets, and model training and evaluation in one collaborative system, plus developer integrations via REST API, Python SDK (pip install ocular-ai), and Jupyter Notebooks. It also offers expert feedback workflows for RLHF and model alignment via Ocular Bolt.