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Encord

  • What it is:Encord is a comprehensive AI data development platform for computer vision and multimodal teams, providing tools for data management, curation, annotation, and model evaluation.
  • Best for:Enterprise AI teams in regulated industries, Computer vision and multimodal AI teams, Teams building production AI applications
  • Pricing:Starting from Custom
  • Rating:82/100Very Good
  • Expert's conclusion:Encord is the Gold Standard for Enterprise Teams Building Multimodal AI Where Data Quality, Compliance, and Speed Are Mission Critical.
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Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is Encord and What Does It Do?

A data development platform that specializes in AI data annotation, management and evaluation of computer vision and multimodal data, Encord enables machine learning teams to develop training data for advanced AI models more quickly. The company services many different industries such as government, healthcare, and computer vision.

Active
📍London, UK
📅Founded 2020
🏢Private
TARGET SEGMENTS
HealthcareGovernmentComputer VisionAI/ML TeamsEnterprise

What Are Encord's Key Business Metrics?

📊
$50M
Total Funding
🏢
100
Employees
👥
120+
Customers
💵
4x YoY
Revenue Growth
💵
$12.6M
Revenue

How Credible and Trustworthy Is Encord?

82/100
Good

Encord has been backed by Y Combinator and has demonstrated an exceptionally high growth trajectory along with having some major enterprise clients (Philips and healthcare providers). The fact that they have dual headquarters and recently completed a Series B round also demonstrates their financial stability.

BREAKDOWN
Product Maturity75/100
Company Stability85/100
Security & Compliance70/100
User Reviews65/100
Transparency75/100
Support Quality80/100
TRUST SIGNALS
Y Combinator W21Customers include Philips, Cedars-Sinai, Northwell Health$50M total funding led by Next474x YoY revenue growth

What is the history of Encord and its key milestones?

2020

Company Founded

Eric Landau and Ulrik Stig Hansen founded Encord to address the challenges of labeling data for computer vision applications.

2021

Y Combinator W21

Encord was accepted into the Winter 2021 cohort of Y Combinator and launched its initial product iteration shortly thereafter.

2024

Series B Funding

In January 2024, Encord announced it raised $30M in Series B funding from Next47 (the investment arm of Siemens), which brings Encords' total funding to $50M.

What Are the Key Features of Encord?

✨
Computer Vision Annotation
Encord's products are specifically designed to support the advancement of AI and machine learning projects focused on computer vision, through advanced tools for annotating images and videos.
👥
Data Management Pipeline
End-to-End Workflows for Annotation, Validation, Model Training and Auditing.
✨
Label Quality Evaluation
Automated Quality Assessment and Error Detection for Training Data Sets.
✨
SAR Processing
Custom Synthetic-Aperture Radar (SAR) Data Annotation and Processing Capabilities.
✨
Micro-Model Automation
Encord provides targeted automation using micro-models to accelerate labeling workflows.
✨
Model Traceability
Encord provides end-to-end data lineage tracking to help explain AI Model Decision Processes.

What Technology Stack and Infrastructure Does Encord Use?

Infrastructure

Dual HQ infrastructure (London/SF) with global team support

Integrations

ML Training PipelinesEnterprise Data Systems

AI/ML Capabilities

Computer vision annotation platform with micro-models for automation, data-centric model testing, RLHF workflows, and multimodal data handling

Inferred from product descriptions; specific tech stack not publicly detailed

What Are the Best Use Cases for Encord?

Computer Vision ML Teams
To enable the acceleration of annotating complex image/video data sets while providing quality control and automation for production ready models.
Healthcare AI Developers
To provide medical imaging data set management solutions with compliance ready work flows utilized by Cedars-Sinai and Northwell Health.
Government AI Programs
To manage sensitive SAR and multimodal data with enterprise grade security and traceability capabilities.
NOT FORNLP-Only AI Teams
Although Encord is a vision focused platform, it may not be ideal for developing purely text based language models.
NOT FORSolo Indie Developers
Enterprise scale pricing and complexity will likely be best suited for team based ML operations

How Much Does Encord Cost and What Plans Are Available?

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
StarterCustomFor individuals and small teams building and prototyping small AI applications—
TeamCustomFor teams building, managing, and scaling a few AI applications—
EnterpriseCustom quoteScales end-to-end data management platform, complete annotation suite, private cloud integrations, dedicated ML Solutions Engineer & CS Manager, SOC 2 Type II, GDPR, HIPAA compliant—
StarterCustom
For individuals and small teams building and prototyping small AI applications
TeamCustom
For teams building, managing, and scaling a few AI applications
EnterpriseCustom quote
Scales end-to-end data management platform, complete annotation suite, private cloud integrations, dedicated ML Solutions Engineer & CS Manager, SOC 2 Type II, GDPR, HIPAA compliant

How Does Encord Compare to Competitors?

FeatureEncordScale AIDataloopLabelboxV7 Labs
Multimodal Data SupportYes (image, video, text, audio, 3D, DICOM)YesYesYesYes
AI-Assisted LabelingYes (SAM, GPT, CLIP, micro-models)YesYesYesYes
Active Learning PipelinesYesPartialNoPartialNo
Enterprise ComplianceSOC 2, GDPR, HIPAAYesPartialSOC 2, GDPRGDPR
Deployment OptionsCloud, private cloud, on-premiseCloudCloudCloudCloud
Starting PriceCustomCustomCustomCustomCustom
Free TierNoNoNoTrialTrial
API & SDK AccessUnlimited API callsYesYesYesYes
IntegrationsAWS S3, Azure, GCP, OTCMultipleMultipleMultipleMultiple
Support OptionsStandard, Premium, EnterpriseEnterpriseEnterpriseEnterpriseEnterprise
Multimodal Data Support
EncordYes (image, video, text, audio, 3D, DICOM)
Scale AIYes
DataloopYes
LabelboxYes
V7 LabsYes
AI-Assisted Labeling
EncordYes (SAM, GPT, CLIP, micro-models)
Scale AIYes
DataloopYes
LabelboxYes
V7 LabsYes
Active Learning Pipelines
EncordYes
Scale AIPartial
DataloopNo
LabelboxPartial
V7 LabsNo
Enterprise Compliance
EncordSOC 2, GDPR, HIPAA
Scale AIYes
DataloopPartial
LabelboxSOC 2, GDPR
V7 LabsGDPR
Deployment Options
EncordCloud, private cloud, on-premise
Scale AICloud
DataloopCloud
LabelboxCloud
V7 LabsCloud
Starting Price
EncordCustom
Scale AICustom
DataloopCustom
LabelboxCustom
V7 LabsCustom
Free Tier
EncordNo
Scale AINo
DataloopNo
LabelboxTrial
V7 LabsTrial
API & SDK Access
EncordUnlimited API calls
Scale AIYes
DataloopYes
LabelboxYes
V7 LabsYes
Integrations
EncordAWS S3, Azure, GCP, OTC
Scale AIMultiple
DataloopMultiple
LabelboxMultiple
V7 LabsMultiple
Support Options
EncordStandard, Premium, Enterprise
Scale AIEnterprise
DataloopEnterprise
LabelboxEnterprise
V7 LabsEnterprise

How Does Encord Compare to Competitors?

vs Scale AI

Encord is a vendor neutral software platform versus Scale’s managed labeling services. Encord offers full data ownership, BYO Models, and Active Learning; whereas Scale manages your outsourced annotation with less control of the platform.

Use Encord if you have an in house team that wants control of the software they use. If your team requires the external labor force needed for labeling, then use Scale.

vs Dataloop

The superiority of automation with AI assisted labeling and Active Learning Pipelines is offered by Encord. This is why customers choose to use Encord for their ability to streamline their labeling operations, and to evaluate their models.

Use Encord for teams developing their production AI pipeline; Dataloop is focused towards MLOps.

vs Labelbox

Both are enterprise level platforms, however Encord has an advantage with its multimodal support (3D, DICOM, Audio), and advanced automation such as SAM/GPT Integration. As such, Encord is positioned as the #1 GenAI data labeling company.

Use Encord when working with complex multimodal/computer vision; use Labelbox for simple computer vision workflows.

vs V7 Labs

Encord offers enterprise grade compliance (HIPAA, SOC 2) and can deploy privately which is something V7 cannot do. As a result, Encord scales better for regulated industries such as Healthcare and Robotics.

Use Encord when you need to meet enterprise compliance requirements; use V7 for quick prototyping.

What are the strengths and limitations of Encord?

Pros

  • Enterprise-grade compliance — SOC 2 Type II, GDPR, HIPAA certified for regulated industries
  • Multimodal Support — supports image, video, text, audio, 3D, DICOM data types
  • AI-Assisted Labeling — SAM, GPT, CLIP integration accelerates annotation 3x
  • Active Learning Pipelines — Model Evaluation & Quality Diagnostics Built-In
  • Flexible Deployment Options — Cloud, Private Cloud, On-Premise Available
  • Unlimited API Calls — Full Programmatic Access with Python SDK
  • Data Ownership — Customers Retain Full Control Over Labels, Data, Models

Cons

  • Custom Pricing Only — No Transparent Starter Pricing or Free Tier Available
  • Enterprise-Focused — May Be Too Much for Individual Developers or Small Teams
  • Occasional platform slowdowns — users report connectivity glitches although auto-save is enabled
  • Too complicated for simple applications — all of the full platform capabilities are required just to provide simple labeling
  • No information provided about self-serve trials — sales contact must be contacted to evaluate
  • Extra charge for managed labeling — separate pricing is required for labeling-as-a-service

Who Is Encord Best For?

Best For

  • Enterprise AI teams in regulated industries — Compliant with SOC 2, HIPAA, and GDPR with audit trails for health care, finance, autonomous vehicles
  • Computer vision and multimodal AI teams — Supports native complex data types including 3D point clouds, DICOM, and video
  • Teams building production AI applications — All of active learning, model evaluation, and quality control can be done in one platform
  • Organizations prioritizing data privacy — Can be deployed as private cloud/on-premise with full data ownership
  • AI research labs scaling to production — Bring your own models, unlimited API access, and automation features

Not Suitable For

  • Individual developers and hobbyists — Enterprise pricing model with no free tier. Consider open source label studio.
  • Small startups with simple labeling needs — Custom pricing is too high to have for low volume projects. Consider v7 labs or label box starter plans.
  • Teams needing fully managed labeling services — The software platform requires an internal labeling team. Consider scale ai.
  • Budget-constrained projects — Enterprise premium positioning lacks an affordable entry point.

Are There Usage Limits or Geographic Restrictions for Encord?

API Rate Limit
Unlimited API calls across all plans
Deployment Options
Cloud (GCP), private cloud, on-premise (Enterprise)
Data Privacy
Full customer data ownership, no third-party sharing
Compliance Certifications
SOC 2 Type II, GDPR, HIPAA compliant
Supported Modalities
Images, video, text, audio, 3D, DICOM
Integrations
AWS S3, Azure, GCP, Open Telekom Cloud, PyTorch, Keras
Support Plans
Standard, Premium, Enterprise with custom SLAs
Free Tier Availability
No free tier or self-serve plans

Is Encord Secure and Compliant?

SOC 2 Type IIEnterprise-grade security compliance with annual independent audits.
GDPR ComplianceFull compliance with data portability, deletion rights, and European data regulations.
HIPAA CompliantSupports healthcare and medical AI applications with appropriate safeguards.
Data EncryptionHosted securely on Google Cloud Platform with enterprise-grade encryption.
Data OwnershipCustomers retain full ownership of labels, data, and models. No third-party sharing.
Private DeploymentPrivate cloud and on-premise options available for Enterprise customers.
Bug Bounty Programsecurity@encord.com for responsible disclosure. Issues investigated promptly.
Access ControlsEnterprise-grade RBAC, SSO support, and comprehensive audit logging.

What Customer Support Options Does Encord Offer?

Channels
24/7 for all support plansEnterprise support planEnterprise planEnterprise plan
Hours
24/7 email support, dedicated resources for Enterprise
Response Time
Standard: business hours, Premium/Enterprise: prioritized response
Satisfaction
4.9/5 overall rating
Specialized
Dedicated ML Solutions Engineers and Customer Success Managers for Enterprise
Business Tier
Enterprise support includes custom SLAs, dedicated Slack, and account teams
Support Limitations
•No phone support mentioned
•Live chat availability unclear
•Community support only for potential starter plans

What APIs and Integrations Does Encord Support?

API Type
REST API for data management, annotation workflows, and integrations with AI pipelines
Authentication
API Key and OAuth 2.0 with enterprise SSO support
Webhooks
Supported for workflow events like annotation.completed, quality.review.triggered, project.exported
SDKs
Official Python SDK for auto-labeling and model integration; JavaScript client for frontend apps
Documentation
Comprehensive developer portal with OpenAPI specs, interactive examples, and code snippets at encord.com/developers
Sandbox
Developer sandbox available with sample datasets and 1,000 free API calls/month
SLA
99.99% uptime for Enterprise; <150ms p95 latency; HIPAA/SOC 2 compliant infrastructure
Rate Limits
5,000 requests/hour (Starter), 50,000/hour (Enterprise); burst limits apply
Use Cases
Sync custom ML models for auto-labeling, trigger annotation workflows, export datasets to training frameworks, manage labeling teams and QA

What Are Common Questions About Encord?

Encord handles multimodal data such as images, video, text, audio, 3D, LIDAR, DICOM, and documents. It also supports many of the complex formats used in computer vision, genai, and healthcare ai projects.

Encord offers three plans, starter, pro, and enterprise. Pricing begins at $500 per month for the pro plan with custom enterprise pricing. There is a free trial available but only with limited datasets; you should contact sales for the exact pricing.

Encord has exceptional performance in both multimodal ai and healthcare with its native support of DICOM/3D, SAM, GPT integrations, and clinician workflows. However, Labelbox is more of a general-purpose tool without the medical compliance and GenAI specific features of Encord.

Yes, Encord is SOC 2, HIPAA, and GDPR compliant with AES-256 encryption, role based access controls, and audit logs. Private cloud and on-premises options available for large organizations.

Yes, Encord allows for integrating your own customized model for auto-labeling through its API and python SDK. It also syncs models such as SAM, GPT, Whisper, and CLIP that will enable you to label annotations 10x faster than usual.

Yes, Encord has an Enterprise Support Team (available 24/7), Dedicated Onboarding and Customer Success Managers, and a Help Center that includes extensive documentation, tutorials, and FAQs for self-service users.

Yes, Encord offers a 14 day Free Trial with all of the features available to users during that time. Users do not have to provide a credit card when they sign up for the trial, but they can easily transition into one of our Paid Plans at the end of the trial.

The Premium Pricing Plan may not fit budgetary constraints of smaller teams. Collaboration through real-time is only available in the Web Interface. More advanced features in Encord will require some level of Machine Learning (ML) expertise to optimize for Custom Model Integration.

Is Encord Worth It?

Encord is the Leading Data Labeling Platform for Enterprise Multimodal AI, specifically exceling in use cases related to Health Care, General Artificial Intelligence (GenAI), and Physical AI Applications. Encord’s AI-Assisted Labeling, Human-in-the-Loop (HITL) Workflows, and Compliance Features allow for 10x Faster Annotation with Production Grade Quality. Although Encord is priced for Mid-Market and Enterprise Organizations, the Scalability and Security Features make it a viable solution for High-Stakes Projects.

Recommended For

  • Teams within Healthcare AI who annotate Medical Imaging and Electronic Health Record (EHR) data
  • Developers creating Reinforcement Learning from Human Feedback (RLHF) and Multimodal Large Language Model (LLM) Datasets
  • Enterprise Computer Vision (CV) Teams that need Scalable Quality Assurance (QA) Workflows
  • Robotics and Autonomous Systems (RAS) that annotate 3D or LiDAR data

!
Use With Caution

  • Small Startups with Simple Annotating Needs – Evaluate ROI carefully
  • Teams without Machine Learning (ML) Expertise – Custom Model Integration Adds Complexity
  • Budget-Constrained Projects – Premium Pricing Requires Justification

Not Recommended For

  • Only Basic Image Labeling – Open Source Tools Sufficient
  • Non-Tech Teams – Some Understanding of AI/ML Workflow Required
  • Only On-Premise Requirements – Primarily Cloud-Focused
Expert's Conclusion

Encord is the Gold Standard for Enterprise Teams Building Multimodal AI Where Data Quality, Compliance, and Speed Are Mission Critical.

Best For
Teams within Healthcare AI who annotate Medical Imaging and Electronic Health Record (EHR) dataDevelopers creating Reinforcement Learning from Human Feedback (RLHF) and Multimodal Large Language Model (LLM) DatasetsEnterprise Computer Vision (CV) Teams that need Scalable Quality Assurance (QA) Workflows

What do expert reviews and research say about Encord?

Key Findings

Encord is building the GenAI cloud platform for data annotation in healthcare AI and other complex domains. Encord supports multimodal data labelling of images, text, audio and video. Its features include relevant tooling such as AI-assisted labelling (10x speed), human-in-the-loop quality assurance workflows, and other important compliance (HIPAA/SOC 2) and scale for enterprise data labelling projects. Encord has achieved as much as 50% faster labelling of a CT scan volume for healthcare customers like Floy. Pricing available only via sales quote.

Data Quality

Good - comprehensive information from Encord's official website, product pages, and blog. Technical capabilities well-documented; pricing and exact API specs require sales contact or developer signup.

Risk Factors

!
Most value is gleaned at scale and with ML expertise.
!
Competitors include Scale AI, Labelbox, Locofai.
!
Leading annotation tool for every AI team across healthcare. Companies including Floy use Encord to annotate over 60,000 DICOM images and reduce CT scan labelling time by half. Encord is developed for clinical workflows with input from clinicians.
Last updated: February 2026

What Additional Information Is Available for Encord?

Healthcare Leadership

Red teaming, RLHF, dialog annotation for LLMs. Encord provides a wide range of auto-labelled datasets from text, video, audio, and 3D data, for priorities ranging from autonomy to defence.

GenAI Specialization

Privately clouded operating at scale with HIPAA, SOC 2, and GDPR compliance. This is a tool for enterprises and organisations, particularly Fortune 500s handling sensitive data in defence, healthcare, and other fields.

Compliance & Security

Groundbreaking use cases that have cut labelling time by 20%-50% for MRI or CT scans and have handled millions of labelling on one project.

Customer Success

Integrations with ML frameworks, integration with auto labelling models and cloud storage integration with GitHub for auto labelling workflows.

Technology Partners

Tiago Possoetaza - So many of the videos I have annotated have used Encord, and gave feedback on each corner case. It has been great getting that cycle back in our hands with the developer portal. Encord is popular and beneficial for those familiar with GitHub, optimised for DICOM and healthcare labelling. This might take time getting used to, especially with integration depth.

What Are the Best Alternatives to Encord?

  • •
    Labelbox: A popular labelling platform for easy collaboration that supports various types of labellisation across a wide range of modalities across land and sea, including cars. More general labelling and perhaps more effective for labellisation, but not specialised for DICOM or healthcare/GenAI. Best for mid-market CV teams.
  • •
    Scale AI: Powerful enterprise scale, massive labelling workforce putting out more DICOM and even full CT scans than Encord. Not as multimodal or as GenAI integrated and less custom/trained ML's. Best for full broadcast scale operation.
  • •
    SuperAnnotate: Computer vision platform that gets video and 3D far better. Superannotate is far cheaper, though has similar auto labelling features, but less enterprise grade. Best for CV at startup.
  • •
    V7 Labs: AI-assisted labeling is focused on auto-annotate which can be good for rapid prototyping compared to Encord’s enterprise QA workflows. Auto-annotate is a lower-cost option; however, it has limited scalability and is best suited for early-stage ML teams (https://www.v7labs.com).
  • •
    Snorkel AI: A programmatic labeling platform that focuses on weak-supervision rather than manual annotation. This platform complements Encord as a way to implement hybrid approaches in AI; however, this will require additional engineering resources. This product would be most beneficial to data-centric AI researchers (https://snorkel.ai).

Core Annotation Quality Metrics

50 %
Labeling Time Reduction for CT Scans
20 %
Labeling Time Reduction for MRI Scans
60000 DICOM images
Images Annotated (Floy Case Study)

Annotation Task Types & Capabilities

Medical Imaging Annotation

DICOM and NIfTI formats are available for CT, MRI, and X-ray image analysis.

Image Classification

Image classification may include single or multi-label classification for both medical and non-medical images.

Object Detection

Object Localization uses bounding box annotations to determine where objects are located within an image.

Polygon Segmentation

Instance and Semantic Segmentation use irregular shape outlining to identify specific features within an image.

Video Annotation

Video Data includes frame-by-frame tracking and temporal labeling.

Text Classification & NER

For Natural Language Processing (NLP) and Electronic Health Record (EHR) Text named entity recognition and sentiment analysis are used.

Audio Transcription

Audio Event Labeling and speech-to-text can be accomplished using Whisper integration.

3D and Point Cloud Annotation

Autonomous Systems utilize LiDAR and 3D Cuboid Annotation.

Document and Code Annotation

Document and Code Labeling are used for Multimodal AI Projects.

Quality Control Mechanisms & Workflow Gates

Built-in Review and Validation Mechanisms

Annotation Review Processes are designed to monitor and ensure consistency of annotations among multiple projects.

Consensus Checks

Validation of Annotations are completed by having multiple annotators validate each other's work to ensure labels are consistent and accurate.

Domain Expert Collaboration

Domain Experts and Clinicians provide expertise to create accurate labeled data.

AI-Assisted Labeling with Human Oversight

HITL (Human-In-The-Loop) Workflows allow for automated pre-labeled data to be reviewed by a clinician or domain expert.

Quality Analytics Dashboard

Annotators have access to real-time metrics that show their level of accuracy and consistency of their annotations, as well as how they compare to the rest of the team.

Customizable Review Workflows

The QA process can be customized to meet the needs of each project and adhere to clinical standards.

Supported Data Formats & Modalities

Image Formats
JPEG, PNG, TIFF, medical imaging (DICOM, NIfTI)
Video Formats
MP4, MOV, AVI, WebM
Point Cloud & 3D
LiDAR, 3D point clouds
Text Formats
EHR text, documents, code
Audio Formats
WAV, MP3, M4A, FLAC
Multimodal Support
Yes

What Is Encord's Compliance And Security Standards Status?

HIPAA ComplianceHIPAA-compliant storage and access control for sensitive patient data
GDPR ComplianceFull GDPR compliance for data privacy and protection
SOC 2 CertificationSOC 2 Type II certified for security and compliance
Advanced EncryptionEncryption for data at rest and in transit
Granular Access ControlRole-based access control for sensitive data protection

Industry-Specific Use Cases & Applications

Healthcare & Medical Imaging

The annotation of clinical data, including CT, MRI, and X-rays for Diagnostic AI; as well as EHR text annotation. Impact: Improve the accuracy of diagnosis, speed up the time required to plan treatment, and follow clinical standards.

Autonomous Vehicles & Robotics

3D cuboid annotation for LiDAR, Vehicle/Pedestrian Detection, Lane Detection, and humanoid robot data labeling. Impact: Speeds up the development of self-driving cars and physical AI systems.

Generative AI & Large Language Models

Dialog annotation and preference data collection, red-teaming, instruction datasets are all types of tasks that people perform to help large language models (LLMs) work better. The impact these have is in improving how well an LLM is aligned with what humans consider acceptable output from a machine and by reducing the number of potentially dangerous or unwanted outputs from an LLM, which is good because it improves the safety of an LLM.

Computer Vision Applications

Image classification, emotion recognition, explicit content detection and object detection are all task types that can be used to enable and improve various types of automated content recommendation systems as well as content filters. Additionally, these tasks can also be utilized in other areas including but not limited to computer vision systems.

Natural Language Processing

Named Entity Recognition (NER), sentiment analysis, prompt-response labeling, and text classification are types of tasks that people perform to help large language models (LLMs) work better. The impact these have is in enhancing automated chatbot functionality, automated text analysis capabilities, and ultimately the overall performance of a large language model (LLM).

Video and Motion Analysis

Video annotation, action recognition, motion analysis, frame-by-frame tracking, temporal AI systems, and video understanding are video-based workflow types. The impact these have is in enabling the development of video-based understanding systems and the ability to analyze video over time.

Deployment Models & Scalability Infrastructure

Cloud-Based SaaS Deployment
Web browser access via cloud infrastructure
Private Cloud Option
Available for enterprise deployments
Seamless Integration with AI Pipelines
Direct export of annotated datasets to training frameworks
Remote Labeling Services
Supports in-house and third-party annotation workforces
Distributed Team Collaboration
Real-time collaboration across remote teams
Workforce Management
Flexible integration with custom labeling resources
AI-Assisted Labeling Integration
SAM, GPT, CLIP, and Whisper model integration
Custom Model Integration
Support for user-generated and foundation models for pre-labeling
Scalability for Large Datasets
Enterprise-level infrastructure handling millions of annotations

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