AI Agents

Automate your data pipelines with agents

Efficiently integrate humans, SOTA models, and your own models into data workflows to reduce the time taken to achieve high-quality data annotation at scale.

Powering the world's leading AI teams

Workflows

Set up streamlined data workflows to enable faster and more accurate data labeling

Build multi-step workflows that unite AI models, labeling teams, and reviewers to produce high-quality labeled data. Maintain visibility of annotation progress across multiple teams.

No-code workflow builder to set up data workflows

Add desired stages to your labeling workflow. Select from annotate, review, consensus, webhooks, and various routers to configure your workflows.

Custom pathways with advanced routers

Create specific pathways for any labeling instances. Leverage annotator-specific routing, label-specific routing, or weighted routing to suit your needs.

Use Encord SDK to set up workflows

Programatically create workflows that fit your data operations and ML pipelines.

Use Encord Agents to automatically pre-label data

Integrate with Claude-3, GPT-4o and more foundational models directly within Encord's Label Editor to pre-label data in bulk and speed up annotators' workflows.

Use Agents within the Label Editor to accelerate labeling and prepare accurately labeled data even faster.

Encord AI Annotation Agents test with Claude 3 Opus results.

Trigger any Agent on your data to automatically pre-classify data to speed up annotators' efforts.

Customize data workflows with Encord Agents

Integrate SOTA models or your own models directly into your data workflows to automate any data action such as reviews, pre-labeling, data classification, filtering and more.

Models

Use Encord Agents to integrate any SOTA Foundational Model into your data workflow

GPT-4o

Open AI’s new flagship model which can reason and generate content across audio, vision, and text in real time. For data pipelines, it can analyze unstructured data and generate contextual labels, enrich metadata, or automate multimodal annotation tasks.

DINOv

A self-supervised computer vision model that uses the Vision Transformer (ViT) architecture to perform image and pixel-level visual tasks such as image classification, video understanding, and depth estimation.

Gemini Pro and Flash 1.5

Models developed by Google that can process text, images, audio and video. Use the model to reason across different modalities to generate text, answer questions and analyze various data.

Claude3-Opus

AI model from Anthropic that is designed to handle complex tasks, such as research, analysis, and task automation. It can process and analyze images, including charts, graphs, technical diagrams, and optical character recognition (OCR).

Open AI Whisper

Automatic speech recognition (ASR) model developed by OpenAI. It can accurately transcribe speech into text across multiple languages and dialects, even in noisy environments. Whisper is trained on a massive dataset of diverse audio clips.

LLaMa 3.2

More accurate and coherent than LLaMa 3.1, Meta's updated model 3.2 introduces vision-based models, allowing it to process and generate text based on images. This is a major advancement, enabling new applications like image captioning and image-based question answering. LLaMa 3.2 is also multilingual and performs well in text based tasks.

LLaVa

A multimodal foundation model that excels at tasks involving both text and images. It has been trained on a large dataset of paired text and image data, allowing it to understand the relationship between language and visual content.

BERT

Bidirectional Encoder Representations from Transformers, understands context, widely used in NLP tasks.

Text-to-Text Transfer Transformer, translates, summarizes, and generates text.

GPT-4o

DINOv

Gemini Pro and Flash 1.5

Models developed by Google that can process text, images, audio and video. Use the model to reason across different modalities to generate text, answer questions and analyze various data.

Claude3-Opus

Open AI Whisper

LLaMa 3.2

LLaVa

BERT

Bidirectional Encoder Representations from Transformers, understands context, widely used in NLP tasks.

Text-to-Text Transfer Transformer, translates, summarizes, and generates text.

Agents

Create Agents to accelerate your data annotation workflows

Automate any action to be taken on your data, from data filtering and sorting to more efficient and robust quality control.

Automatically filter data before labeling

Accelerate your labeling workflows by using an Agent to reduce the size of your dataset and speed up annotation workflows.

Smart routing for optimizing annotation resources

Create an Agent to sort data and route to the appropriate labeling teams to ensure that labeling budgets are maximized.

Minimize the burden on human reviewers

Achieve better and more efficient quality control by integrating an Agent to review labels and route only certain cases for human review.

Leverage Encord Agents to customize your data workflows

To learn how to automate any part of your data workflow, discuss your use case with our ML team and find out how to achieve the most efficient data workflow.

Why Encord?

Join the thousands of teams deploying production-ready AI applications with best-in-class data curation, labeling, and model evaluation tooling with Encord.