Automate and accelerate your AI data pipelines with Data 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.
AI Agents
Powering the world's leading AI teams
Fast-track AI development with better data
Use Encord Data Agents to securely and seamlessly integrate with Claude-3, GPT-4o and more models directly into your data workflow to automate any data action at speed. Customize your workflow to combine AI and HITL to 10x your data throughput while retaining label accuracy at scale.
Integrate the right AI model to automate data workflows
Flexibly integrate your own model or SOTA foundation models. to enable accurate data preparation, fast. Automate any data action such as pre-labeling, routing by reasoning, evaluation and more.
Scale data pipelines without scaling headcount
Orchestrate bulk data labeling with AI to future-proof your data workflows to effectively handle large data volumes. Integrate HITL QA to maintain data quality and label accuracy.
Workflows
Save 1000s of hours, automate any data pipeline task
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.
Multimodal Prelabeling
Object Segmentation & Tracking
Video Captioning
Audio Transcription & Speaker Diarization
Sentiment Analysis
OCR Text Extraction
Routing By Reasoning
LLM As A Judge
Get started instantly using the Encord Data Agents Library
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
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.
T5
Text-to-Text Transfer Transformer, translates, summarizes, and generates text.
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.
T5
Text-to-Text Transfer Transformer, translates, summarizes, and generates text.
Build agentic data workflows for any use case
AI-powered multimodal data labeling for building Voice AI
Integrate AI models for automatic transcription, speaker diarization and sentiment analysis. Develop context-rich datasets for training and fine-tuning Voice AI Agents for Customer Support interactions.
Use YOLO & OCR for accelerating Physical AI data labeling
Eliminate manual labeling by accessing any model such as YOLO for accelerated object segmentation, OCR text extraction directly within the annotation interface.
Chain multiple models together to build rich GenAI datasets
Create agentic data workflows to automate sequenced data tasks and develop context rich multimodal datasets at scale including video, text and audio.
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.
Integrations
Integrate seamlessly with your toolstack
Connect your secure cloud storage, MLOps tools, and much more with dedicated integrations that slot seamlessly into your workflows.
Security
Built with security in mind
Encord is SOC2, HIPAA, and GDPR compliant with robust security and encryption standards.
API/SDK
Developer-friendly for easy access
Leverage our API/SDK to programatically access projects, datasets & labels within the platform via API.