AI visibility report
Encord ranks #2 in AI Data Curation and Dataset Versioning AI search.
Outside the top three on 13 of the 25 prompts buyers actually ask.
lakeFS is cited on 13 of those losses.
Free trial. Setup comes pre-filled for Encord.
Track Encord across these prompts daily.
Start free trial#2 among 7 vendors · still absent from 92.8% of tracked prompt responses
Top-3 citations across 125 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
Perplexity
ChatGPT
Gemini Search
Google AI Mode
Bing CopilotVisible, but narrative can improve. Encord ranks #2 on presence but #4 on sentiment. The brand appears relatively often, but competitors may be getting more favorable language when they appear.
Where Encord is losing
Prompts where competitors are visible and Encord is not.
These prompt-level losses are the first prompts to track and repair.
Where Encord is winning2
Which AI dataset management tools have native connectors to annotation and labeling services so curated slices can be sent for labeling without manual export?
Avg # 2.0 · 2 platforms
Which AI data curation platforms offer the best visual dataset explorer so non-engineers on a labeling team can review samples without writing code?
Avg # 3.0 · 1 platform
Where Encord is losing5
Which dataset versioning tools have the smoothest onboarding for teams migrating off a manual folder-based data management system?
Competitors on 3 platforms
Track this promptLooking for a dataset versioning tool that works with multiple cloud object storage providers to avoid lock-in — what are the best options?
Competitors on 3 platforms
Track this promptWhat's the quickest way for an ML engineer to set up reproducible dataset snapshots without migrating away from existing cloud object storage?
Competitors on 3 platforms
Track this promptWhich Python-native dataset management libraries make it easiest to start versioning multimodal training data from an existing object storage bucket?
Competitors on 2 platforms
Track this promptWhich dataset versioning tools handle petabyte-scale training datasets without bottlenecking the data loading pipeline during distributed training?
Competitors on 2 platforms
Track this prompt
Track Encord daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
Overview
Encord is an AI-native data infrastructure platform founded in 2021 and headquartered in San Francisco, with offices in London. It provides a unified 'universal data layer' enabling AI teams to manage, curate, annotate, and align multimodal data — including video, images, audio, LiDAR, DICOM, and sensor fusion — at petabyte scale. The platform spans the full AI data lifecycle from raw data ingestion and embedding-based curation through human-in-the-loop annotation, RLHF-based post-training alignment, and model evaluation. Encord is particularly focused on physical AI applications such as autonomous vehicles, robotics, drones, and smart spaces. Trusted by 300+ AI teams including Woven by Toyota, Zipline, AXA, UiPath, and Flock Safety, the company has raised $110M in total funding and holds SOC 2, HIPAA, and GDPR compliance certifications.
Encord is a multimodal AI data platform that unifies data curation, annotation, post-training alignment, and model evaluation in a single end-to-end system. Built for physical AI workloads, it handles diverse data modalities including video, LiDAR, audio, DICOM, and sensor fusion at petabyte scale, with AI-assisted annotation, embedding-based dataset curation, agentic workflow automation, and RLHF capabilities — all while keeping customer data within their own cloud storage infrastructure.
Key Facts
- Founded
- 2021
- HQ
- San Francisco, CA / London, UK
- Founders
- Eric Landau, Ulrik Stig Hansen
- Employees
- 100-200
- Funding
- $110M
- Customers
- 300+
- Status
- Private
Target users
Key Capabilities10
- Embedding-based multimodal data curation and outlier/edge-case detection (Encord Index)
- Native annotation for video, image, audio, LiDAR/3D point cloud, DICOM, text, and geospatial data
- AI-assisted labeling with SAM2, object tracking, interpolation, and model-assisted pre-labeling
- RLHF, rubric-based evaluation, and pairwise comparison for post-training model alignment
- Agentic data workflow automation (Encord Data Agents) for human-in-the-loop pipelines
- Label quality control with consensus workflows, annotator performance dashboards, and active learning
- Dataset versioning, lineage tracking, and full audit trail across annotation history
- Native integrations with AWS S3, GCP, Azure Blob, and other private cloud storage providers
- Managed labeling services with expert annotators and domain specialists
- Model evaluation and validation against ground-truth data with custom metrics
Key Use Cases7
- Training perception models for autonomous vehicles and ADAS (LiDAR, camera, radar fusion)
- Building robotics and humanoid robot manipulation datasets (RGB-D, point cloud, sensor fusion)
- Medical imaging AI development (DICOM/NIfTI annotation, clinical workflow integration)
- Post-training alignment and RLHF for frontier and generative AI models
- Drone and aerial system data labeling (thermal, multispectral, LiDAR LAS)
- Smart spaces and retail analytics AI training (video, IoT sensor data)
- Large-scale multimodal dataset curation and edge-case discovery for production AI
Encord customer outcomes
60% increase in labeling speed; 40,000+ images curated efficiently
CONXAI, an AI platform for the architecture, engineering and construction (AEC) industry, replaced their in-house annotation tool with Encord, achieving significantly faster labeling and more efficient dataset curation at scale.
10x faster video annotation
SDSC partnered with Encord to accelerate surgical video annotation workflows, dramatically reducing the time required per annotation task for their research pipelines.
Recent Trend
How AI describes Encord3
Encord : Combines dataset management with active-learning techniques. It enables users to curate data that is highly uncertain to a model and send it directly into an annotation interface.
Which dataset versioning tools support branching and merging semantics similar to source control for managing parallel data experiments?
Encord : An active learning and data curation platform that excels at indexing and managing large-scale image and video data.
Which AI dataset management tools have native connectors to annotation and labeling services so curated slices can be sent for labeling without manual export?
...best visual dataset explorers for non-engineers to review samples without code are Labelbox , SuperAnnotate , Encord , and FiftyOne . These platforms prioritize intuitive user interfaces, active learning, and visual debugging for ma...
Which dataset versioning tools integrate best with experiment tracking platforms so training runs automatically link to the exact dataset version used?
Most cited sources8
19Best Data Labeling Platform (2026 Buyer’s Guide) | Encord
encord.com·Product Page
- 16
7 Best Data Curation Tools for Computer Vision of 2024
encord.com·Product Page
- 10
AI Annotation Platforms with the Best Data Curation (2026) - Encord
encord.com·Product Page
- 5
Data Versioning: Build Successful ML Models
encord.com·Blog Post
- 5
AI Data Platform: Manage, Curate & Annotate Multimodal ...
encord.com·Product Page
- 4
Multimodal Data Curation Tool for AI
encord.com·Product Page
Alternatives in AI Data Curation and Dataset Versioning6
Encord positions itself as an AI-native, end-to-end 'universal data layer' for physical AI — differentiating from point-solution annotation tools by unifying data management, embedding-based curation, multimodal annotation, RLHF/post-training alignment, and model evaluation in a single platform.
- Its strongest differentiator is native, video-first and multimodal support (video, LiDAR, audio, DICOM, sensor fusion) at petabyte scale, targeting physical AI verticals such as autonomous vehicles, robotics, and drones where multimodal data complexity is highest.
- Unlike lakeFS or Activeloop (which focus on data versioning/storage), Encord emphasizes active curation, label quality, and model-feedback loops.
- It competes with Roboflow on computer vision teams but targets larger enterprise and physical AI workloads.
- Its 4x revenue growth year-over-year and 5 petabytes under management signal momentum against Scale AI and Labelbox at the enterprise tier.
Reviews
Praised
- Video-native and video-first annotation capabilities
- User-friendly and intuitive interface
- Responsive and helpful customer support team
- Efficient large-scale annotation team management
- Seamless AWS S3 and cloud storage integrations
- Encord Index for full dataset visibility and gap analysis
- Advanced image segmentation tools (SAM2)
- Rapid product evolution and feature releases
Criticized
- Python SDK occasionally missing features available in the REST API
- Limited mobile interface capabilities
- Video clip-level analysis tools less developed than frame-by-frame tools
- Some niche features and functions missing or hard to discover
Encord holds a 4.8/5 rating across 65 verified G2 reviews, with 92% giving five stars. Reviewers consistently highlight the platform's ease of use, video-native annotation capabilities, responsive customer support, and efficient annotation team management. Users praise the seamless AWS S3 integration, the Index dataset visibility feature, and the breadth of modality support. Criticisms are limited but include occasional gaps in the Python SDK versus the full REST API, some missing features for mobile use, and a desire for more advanced video clip-level analytics tools.
Pricing
Encord offers three tiers: Starter (self-serve, for individuals and small teams prototyping AI applications, includes image/video annotation, custom workflows, and self-serve support), Team (for scaling teams, adds data agents, performance analytics, model evaluation, and onboarding support), and Enterprise (for large organizations, adds SSO, multiple workspaces, enterprise SLA, VPC and on-premises deployment options — requires contacting sales). Specific dollar pricing for Team and Enterprise tiers is not publicly disclosed. Advanced modalities (LiDAR, DICOM, geospatial, ECG) are available as add-ons. Managed data labeling and collection services are available separately.
Limitations
- Public G2 reviews note that the Python SDK occasionally lags behind the full REST API in feature coverage.
- Some users report limited mobile interface capabilities.
- Video clip-level analysis tooling is less developed than frame-by-frame annotation tools.
- Pricing is not publicly disclosed for Team and Enterprise tiers, requiring a sales engagement.
- Advanced modalities such as DICOM/NIfTI, geospatial, ECG, and LiDAR are add-ons and not included in base plans.
- The platform is relatively newer compared to incumbents like Scale AI or Labelbox, meaning some niche enterprise integrations may be less mature.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | Google AI Mode | Bing Copilot | ChatGPT | Perplexity |
|---|---|---|---|---|---|
Capability2/5 cited (40%) | |||||
Which dataset versioning platforms let ML teams tag, lineage-track, and roll back to any historical dataset state used for a production model training run? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand was cited |
Which dataset versioning tools support branching and merging semantics similar to source control for managing parallel data experiments? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
What are the best platforms for embedding-based deduplication and near-duplicate detection across a multimodal training corpus at scale? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
I'm evaluating AI dataset management tools — which ones support automated data quality checks and slice-level statistics for model evaluation sets? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which data curation tools handle mixed-modality datasets — images, text, and structured metadata — in a single versioned artifact? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Your brand was cited |
Developer Experience2/5 cited (40%) | |||||
Which dataset versioning platforms have the best Python SDK for iterating over large image datasets without loading everything into memory? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which dataset management tools make it easiest for ML engineers to query, filter, and tag unstructured data using embedding-based similarity search? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
What data curation tools do ML platform teams typically use to give model trainers a clean, reproducible slice of a dataset without raw storage access? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand was cited |
Looking for a dataset versioning tool with a great notebook-friendly workflow — what are the best options for teams that live in Jupyter? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which AI data curation platforms offer the best visual dataset explorer so non-engineers on a labeling team can review samples without writing code? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand was cited |
Integrations & Ecosystem1/5 cited (20%) | |||||
What data curation platforms work well alongside a feature store and a model registry for a fully lineage-tracked ML pipeline? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which AI dataset management tools have native connectors to annotation and labeling services so curated slices can be sent for labeling without manual export? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand was cited | Your brand and a competitor were cited |
Looking for a dataset versioning tool that works with multiple cloud object storage providers to avoid lock-in — what are the best options? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which data curation platforms integrate with workflow orchestration tools so dataset preprocessing and versioning steps run as part of an automated ML pipeline? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which dataset versioning tools integrate best with experiment tracking platforms so training runs automatically link to the exact dataset version used? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Performance & Reliability0/5 cited (0%) | |||||
Looking for a data versioning layer over object storage that handles concurrent writes from multiple experiment runs without corruption — what are my options? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which dataset versioning tools handle petabyte-scale training datasets without bottlenecking the data loading pipeline during distributed training? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which dataset management tools are production-proven for enterprise ML teams managing hundreds of dataset versions without storage cost spiraling? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
What are the best AI data curation platforms for streaming random-access reads from large image datasets stored in object storage with low latency? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which AI dataset platforms have the best performance for querying embedding indexes across tens of millions of vectors in a curation workflow? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Setup & First Run1/5 cited (20%) | |||||
Which dataset versioning tools have the smoothest onboarding for teams migrating off a manual folder-based data management system? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
I'm evaluating data curation platforms for a computer vision team of 5 — which ones have the fastest path from raw images to a labeled, versioned dataset? | Your brand was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | Your brand and a competitor were cited |
Which Python-native dataset management libraries make it easiest to start versioning multimodal training data from an existing object storage bucket? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
What are the best dataset versioning tools for a small ML team to get started with object storage without a complex infrastructure setup? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
What's the quickest way for an ML engineer to set up reproducible dataset snapshots without migrating away from existing cloud object storage? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | lakeFS | 29.6% | 59.1% | 7.2% | 13.6% | 52.8% | #2.9 | +0.41 |
| 2 | Encord | 7.2% | 21.5% | 0.0% | 7.2% | 16.8% | #4.3 | +0.38 |
| 3 | Voxel51 | 4.0% | 11.8% | 3.2% | 0.0% | 9.6% | #4.5 | +0.53 |
| 4 | Roboflow | 2.4% | 5.4% | 0.0% | 2.4% | 7.2% | #2.8 | +0.27 |
| 5 | Activeloop | 0.8% | 2.2% | 0.0% | 0.0% | 6.4% | #1.5 | +0.90 |
| 6 | DataChain | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 7 | Nomic AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
Turn this into your team dashboard
Sign up to unlock project-level analytics, daily tracking, actionable insights, custom prompt configurations, adoption tracking, AI traffic analytics and more.
Free trial. Setup comes pre-filled from this report.