Ocular AI

AI Agents for Automated Data Annotation

Leverage intelligent AI agents to automatically label massive multimodal datasets with precision, speed, and cost-effectiveness that scales with your data growth.

Autolabeling Agent

Label Configurations

ChairCouch

29%

PersonHuman

87%

Model

Apply to Frames:

11000

Label data at scale

Flexible scaling from frames
to entire datasets

Label individual frames for precision work or process entire jobs at once for maximum efficiency. AI agents adapt to your workflow needs, whether you're handling single image annotations or batch-processing thousands of video frames, providing consistent quality and speed at any scale.

Agent on canvas

Intelligent AI agents that work
directly on your annotation canvas

Use pre-trained or custom models trained on your specific data to achieve higher accuracy and consistency, while processing massive multimodal datasets with AI agents that understand your domain and annotation requirements.

Flexible accuracy

Configurable accuracy and
confidence controls

Fine-tune your annotation workflow with flexible accuracy thresholds and confidence score settings. Adjust parameters to balance speed and precision based on your specific needs, ensuring AI agents only auto-label data that meets your quality standards while flagging uncertain annotations for human review.

Models in the loop

Label data with off-the-shelf
or custom models in the loop

Cut down cost & time

Cut down annotation time & speed
without increasing headcount

BENEFITS

Agentic data annotation built for
scale, speed, and accuracy

Scalable Annotation Speed

AI agents process large volumes of data at unprecedented speed while human experts focus on quality control and complex edge cases.

Consistent Quality Assurance

Human oversight ensures accuracy and catches nuanced errors while agents maintain consistent labelling standards across massive datasets.

Cost-Effective Labelling

Dramatically reduce annotation costs by leveraging agents for routine tasks while strategically deploying human expertise where it matters most.

Rapid Iteration & Learning

Human feedback continuously improves agent performance, creating a virtuous cycle of enhanced accuracy and reduced oversight requirements.

Complex Task Handling

Agents handle high-volume routine labelling while humans tackle complex scenarios, ambiguous cases, and domain-specific annotations.

Reduced Human Fatigue

Eliminate repetitive labelling tasks for human annotators, allowing them to focus on high-value oversight and quality verification.

Systematic Error Detection

Human review systematically catches and corrects agent errors before they propagate, maintaining dataset integrity at scale.

Ocular SDK

Engineered for ambitious
AI projects

Ocular AI SDKOcular AI SDK

ocular-sdk.py

1from ocular import Ocular
2
3# Initialize the SDK with your API key
4ocular = Ocular(api_key="api_key")
5
6# Access a workspace
7workspace = ocular.workspace("workspaceID")
8
9# Get a project from the workspace
10project = workspace.project("projectID")
11
12# Get a version from the project
13version = project.version("versionID")
14
15# Get an export from the version
16export = version.export("exportID")
17
18# Download the export dataset
19dataset_path = export.download()
20

Integrations

Integrate with your tech stack

Ocular integrates with your existing tech stack and multiple integrations that slot seamlessly into your workflows and pipelines.

Even when you connect to external data sources, all data stays on your existing infrastructure and data sources.

Security

Enterprise-grade security

Enterprise-grade and battle-tested security measures and protocols to protect your data.

All our systems are built with security in mind and are constantly monitored and audited.

Autolabeling Agent

Overview

Data

Data Annotation

Build AI Models