What is Ocular AI?

Ocular AI provides a unified multimodal lakehouse that consolidates data from cloud, local storage, and external sources, enabling teams to search, curate, and version datasets in a single catalog. The platform supports large‑scale labeling through AI agents and human‑in‑the‑loop workflows, allowing precise annotation of videos, images, and audio with minimal manual effort.

Ocular Bolt extends the data layer to include reinforcement‑learning‑with‑human‑feedback (RLHF) and expert alignment for model training and evaluation, facilitating domain‑specific quality control. Integrated GPU clusters and scalable training pipelines let users build, test, and compare custom AI models directly on their enterprise data.

A RESTful search API enables developers to embed multimodal retrieval capabilities into applications with natural language queries. The system includes role‑based access control, data lineage, and compliance features to maintain security and privacy across the data lifecycle.

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Ocular AI's key features

Centralized multimodal lakehouse
Ingest and curate multimodal data
AI agent + human labeling
GPU cluster training pipelines
Interactive model evaluation playground
REST API multimodal search
Versioned dataset project management

Ocular AI use cases

Consolidate product design assets, user feedback videos, and sensor data into Ocular AI’s multimodal catalog, enabling cross‑functional teams to search, version, and label data with human‑in‑the‑loop for accelerated feature development.
Deploy Ocular AI’s RLHF training pipeline to fine‑tune a conversational AI model on a curated, GPU‑accelerated dataset, while maintaining role‑based compliance controls over sensitive content.
Expose the enterprise multimodal search API to internal data scientists, allowing real‑time retrieval of labeled video, audio, and text snippets for downstream analytics, with built‑in data lineage for auditability.