# Nomic AI AI visibility in AI Data Curation and Dataset Versioning Canonical: https://devtune.ai/verticals/ai-data-curation-and-dataset-versioning/nomic-ai [Website](https://nomic.ai/) Updated: 2026-10-23T23:49:23.22535+00:00 Prompts: 25 Runs: 5 ## Platforms - perplexity - bing-copilot-search - google-ai - google-ai-mode - chatgpt-search Rank: 7 Total brands: 7 Measured responses: 125 Presence percent: 0 Share of voice percent: 0 Average position: Not available Docs presence percent: 0 Blog presence percent: 0 Brand mention percent: 0 ## Profile Overview: Nomic AI is a New York-based AI infrastructure company founded in 2022 by Brandon Duderstadt and Andriy Mulyar. Its flagship developer product, Nomic Atlas, is an AI-ready data platform enabling ML engineers and data scientists to explore, curate, visualise, and retrieve datasets of text, images, PDFs, and embeddings at multi-million-point scale through an interactive browser interface. Nomic also produces Nomic Embed, a fully open-source text embedding model with an 8192-token context window benchmarking above OpenAI Ada-002 on standard retrieval tasks, and GPT4All, a widely adopted open-source local LLM runtime. Since 2024 the company has pivoted toward a domain-specific AEC AI platform for architecture, engineering, and construction firms. Nomic raised $17M in a Series A led by Coatue in July 2023 at approximately a $100M valuation. Product summary: Nomic AI provides an AI data intelligence platform built around three core products: (1) Nomic Atlas, a browser-based and API-accessible platform for interactive embedding visualisation, dataset curation, semantic search, deduplication, and topic modelling over large unstructured datasets; (2) Nomic Embed, a suite of fully open-source long-context text and multimodal embedding models; and (3) GPT4All, an open-source local LLM inference runtime. Layered on this foundation, Nomic has launched a domain-specific AEC AI platform with automated drawing review, code compliance, submittal review, and project research workflows, plus a Developer API for building custom knowledge agents over AEC firm data. ### Key capabilities - Interactive browser-based data maps for exploring millions of embeddings, text, and multimodal data points - Nomic Embed: fully open-source (Apache-2) long-context (8192-token) text and vision embedding models outperforming OpenAI Ada-002 and text-embedding-3-small on MTEB and LoCo benchmarks - AI-powered dataset curation via semantic clustering, lasso selection, bulk tagging, and deduplication at scale - Vector search and nearest-neighbour retrieval over stored embeddings via the Atlas API - Automatic topic modelling across uploaded datasets with hierarchical topic trees - GPT4All: open-source local LLM inference runtime supporting multiple model families on consumer hardware - AEC-domain document parsing (Nomic Parse) for large PDFs, drawing sets, and engineering specifications - Automated code compliance checking against 380+ building codes and standards - Developer API for programmatic embedding, document parsing, extraction, and semantic search ### Target users - ML engineers and data scientists curating and exploring training datasets - AI researchers debugging and optimising embedding model outputs - Enterprise software teams building RAG and semantic search applications - Architecture, engineering, and construction firms seeking document intelligence automation - Developers building knowledge agents or AI-powered applications over unstructured data - Non-technical domain experts needing low-code access to large proprietary datasets ### Key use cases - Exploring and curating unstructured text, image, and PDF datasets for ML model training - Embedding visualisation and model debugging to detect cluster overlap, misclassification, and feature drift - Deduplication and quality filtering of large training or retrieval datasets - Semantic search and RAG pipeline construction over proprietary knowledge bases - AEC-firm document intelligence: automated drawing review, submittal review, and code compliance - Synthetic data generation and domain-expert feedback collection - Local, privacy-preserving LLM deployment for sensitive enterprise environments Integrations ecosystem: Atlas integrates via a Python SDK (pip install nomic) and REST API, supporting direct embedding ingestion from any ML framework and compatible with LangChain and LlamaIndex. Nomic Embed is available on AWS Marketplace via Amazon SageMaker for production RAG and retrieval workloads. The AEC-focused Nomic Platform natively integrates with SharePoint, Egnyte, Autodesk Construction Cloud (ACC), Bentley ProjectWise, Google Drive, Dropbox, Box, Microsoft Teams, Gmail, and Outlook. GPT4All enables local LLM inference on consumer-grade hardware across Windows, macOS, and Linux. Industry partnerships include MongoDB and Hugging Face for dataset distribution and model hosting. Pricing summary: The AEC-focused Nomic Platform (Business tier) is priced at $40 per user per month with a minimum 25-seat commitment ($1,000/month minimum), annual contract required; each seat includes $20 of pooled AI usage credits. Enterprise tier is custom-priced and includes VPC or on-premises deployment, SCIM, audit logs, and dedicated CSM. Atlas and Nomic Embed are available with a free individual tier and usage-based API billing; Nomic Embed is also available on AWS Marketplace with per-token SageMaker pricing. GPT4All is free and open-source with no usage fees. Review summary: No verifiable aggregate scores for Nomic Atlas or the Nomic Platform were found on G2, Gartner Peer Insights, or comparable review platforms at time of research. Qualitative feedback from the published Aurecon case study highlights strong productivity gains, improved data explainability, and positive reception among both technical and non-technical stakeholders. The ML open-source community has broadly adopted Nomic Embed, with practitioners citing strong MTEB benchmark performance and full training-data auditability as key differentiators versus OpenAI and Jina embedding models. Competitive positioning: Nomic AI positions Atlas as an open, interactive data intelligence layer for unstructured data, differentiating through browser-based visual exploration of datasets up to tens of millions of points combined with fully open-source embedding models. Unlike annotation-centric competitors such as Encord and Roboflow, Atlas prioritises embedding visualisation and semantic clustering for holistic data understanding rather than label management. Against storage-layer competitors like Activeloop and lakeFS, Nomic competes on explorability and AI-readiness rather than data versioning primitives. Its dual open-source posture—releasing model weights, training code, and training data for Nomic Embed—appeals to ML teams prioritising auditability. The company has simultaneously pivoted toward a closed, AEC-vertical SaaS platform built on the same underlying models, which may narrow its general AI data curation footprint over time. Limitations: Nomic AI's strategic focus is visibly shifting from general AI data curation (Atlas) toward a closed AEC-vertical SaaS product, creating uncertainty about long-term Atlas roadmap investment. Atlas lacks native dataset versioning primitives (branching, rollback, lineage) comparable to lakeFS or DataChain. The platform has limited annotation or human-labelling tooling relative to Encord or Roboflow. Minimum commitment for the AEC platform (25 seats / $1,000 per month, annual contract) may be prohibitive for smaller teams. The company's small headcount (~21 employees as of early 2026) may constrain product breadth, support capacity, and enterprise-grade SLA coverage. Enterprise Atlas pricing and SLA terms are not publicly disclosed. No verifiable aggregate scores from G2 or Gartner Peer Insights were found for either Atlas or the AEC platform. ### Source urls - https://nomic.ai/ - https://docs.nomic.ai/ - https://github.com/nomic-ai/nomic - https://nomic.ai/pricing - https://nomic.ai/case-studies/aurecon-case-study - https://www.cbinsights.com/company/nomic-ai - https://www.cbinsights.com/company/nomic-ai/financials - https://www.clay.com/dossier/nomic-ai-funding - https://finance.yahoo.com/news/ai-startup-nomic-raises-17-000409855.html - https://aws.amazon.com/marketplace/pp/prodview-xume634dhbnyu - https://www.nomic.ai/news/nomic-embed-text-v1 - https://docs.nomic.ai/api/datasets/data-maps/guides/data-curation - https://engineering.jhu.edu/magazine/changemakers/eager-disruptter/ - https://tracxn.com/d/companies/nomic/__jlaB-oRF-VeDE8Z_mOsXJjV9WXlvOiutFNXoQxHZJ3A - https://atlas.nomic.ai/roles/ai-teams Reviewed at: 2026-05-06T17:53:56.438+00:00 ### Customer outcomes | Customer | Summary | Metric | | --- | --- | --- | | Aurecon | Global engineering consultancy with 7,500 employees deployed Nomic Enterprise for data exploration, deduplication, curation, RAG system integration, and project knowledge retrieval, enabling both technical and non-technical stakeholders to collaborate on AI-powered workflows. Tea | +30% productivity increase for tasks where Nomic was implemented; 10–20 hours saved per team per week | ### Reviews breakdown ### Review themes #### Praised - Intuitive browser-based visual exploration of large and complex datasets - Full open-source auditability of Nomic Embed weights, training code, and training data - Strong MTEB and long-context benchmark performance versus OpenAI embedding models - Low-code curation interface accessible to non-technical domain experts - Seamless integration with existing enterprise storage systems (SharePoint, ACC, Egnyte) - Significant time savings on document-heavy knowledge workflows - Positive experience enabling junior engineers to work at senior-principal efficiency #### Criticized - Strategic pivot toward AEC vertical creates uncertainty for general AI data curation users - No native dataset versioning or branching primitives comparable to dedicated version-control tools - Limited annotation or human-labelling tooling relative to specialist competitors - High minimum seat commitment ($1,000/month) may be prohibitive for smaller teams - Small team size may limit enterprise support capacity and product breadth - Enterprise Atlas pricing not publicly disclosed ### Company facts Founded year: 2022 Hq: New York, USA #### Founders - Brandon Duderstadt - Andriy Mulyar Employees range: 11-25 Total funding: $17M Valuation: ~$100M Arr: Not available Customer count: Not available Status: Private Readiness: Not available ## Ranking | Display name | Pair count | Total pairs | Presence percent | Avg position | | --- | --- | --- | --- | --- | | lakeFS | 40 | 125 | 32 | 4.877551020408164 | | Voxel51 | 18 | 125 | 14.399999999999999 | 6.724137931034483 | | Encord | 8 | 125 | 6.4 | 7.05 | | Roboflow | 6 | 125 | 4.8 | 6.823529411764706 | | Activeloop | 2 | 125 | 1.6 | 7 | | DataChain | 0 | 125 | 0 | Not available | | Nomic AI | 0 | 125 | 0 | Not available | ## Platform breakdown | Platform | Prompt count | Presence rate | | --- | --- | --- | | perplexity | 0 | 0 | | bing-copilot-search | 0 | 0 | | google-ai | 0 | 0 | | google-ai-mode | 0 | 0 | | chatgpt-search | 0 | 0 | ## Strengths ## Gaps | Prompt text | Competitor presence count | | --- | --- | | Which AI dataset management tools have native connectors to annotation and labeling services so curated slices can be sent for labeling without manual export? | 4 | | Which AI data curation platforms offer the best visual dataset explorer so non-engineers on a labeling team can review samples without writing code? | 4 | | Which dataset management tools are production-proven for enterprise ML teams managing hundreds of dataset versions without storage cost spiraling? | 3 | | Looking for a dataset versioning tool that works with multiple cloud object storage providers to avoid lock-in — what are the best options? | 3 | | Which dataset management tools make it easiest for ML engineers to query, filter, and tag unstructured data using embedding-based similarity search? | 3 | ## Topic scores | Topic name | Prompt count | Cited prompt count | | --- | --- | --- | | Capability | 5 | 0 | | Developer Experience | 5 | 0 | | Integrations & Ecosystem | 5 | 0 | | Performance & Reliability | 5 | 0 | | Setup & First Run | 5 | 0 | ## Prompt results - Prompt text: Which AI dataset platforms have the best performance for querying embedding indexes across tens of millions of vectors in a curation workflow? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity ##### Bing-copilot-search ##### Google-ai ##### Google-ai-mode ##### Chatgpt-search | Display name | Position | | --- | --- | | Voxel51 | 2 | - Prompt text: What's the quickest way for an ML engineer to set up reproducible dataset snapshots without migrating away from existing cloud object storage? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity | Display name | Position | | --- | --- | | lakeFS | 1 | ##### Bing-copilot-search ##### Google-ai ##### Google-ai-mode ##### Chatgpt-search | Display name | Position | | --- | --- | | lakeFS | 3 | - Prompt text: What are the best platforms for embedding-based deduplication and near-duplicate detection across a multimodal training corpus at scale? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity ##### Bing-copilot-search ##### Google-ai ##### Google-ai-mode ##### Chatgpt-search - Prompt text: What data curation platforms work well alongside a feature store and a model registry for a fully lineage-tracked ML pipeline? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity | Display name | Position | | --- | --- | | lakeFS | 5 | ##### Bing-copilot-search ##### Google-ai ##### Google-ai-mode ##### Chatgpt-search | Display name | Position | | --- | --- | | lakeFS | 1 | - Prompt text: Which dataset versioning platforms have the best Python SDK for iterating over large image datasets without loading everything into memory? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity ##### Bing-copilot-search ##### Google-ai ##### Google-ai-mode | Display name | Position | | --- | --- | | Activeloop | 1 | ##### Chatgpt-search | Display name | Position | | --- | --- | | Voxel51 | 2 | | lakeFS | 3 | - Prompt text: Which Python-native dataset management libraries make it easiest to start versioning multimodal training data from an existing object storage bucket? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity | Display name | Position | | --- | --- | | lakeFS | 4 | ##### Bing-copilot-search ##### Google-ai ##### Google-ai-mode | Display name | Position | | --- | --- | | lakeFS | 4 | | Voxel51 | 7 | ##### Chatgpt-search | Display name | Position | | --- | --- | | lakeFS | 2 | - Prompt text: Which dataset management tools are production-proven for enterprise ML teams managing hundreds of dataset versions without storage cost spiraling? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity | Display name | Position | | --- | --- | | lakeFS | 1 | ##### Bing-copilot-search ##### Google-ai | Display name | Position | | --- | --- | | lakeFS | 1 | ##### Google-ai-mode ##### Chatgpt-search | Display name | Position | | --- | --- | | lakeFS | 1 | - Prompt text: Looking for a dataset versioning tool that works with multiple cloud object storage providers to avoid lock-in — what are the best options? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity | Display name | Position | | --- | --- | | lakeFS | 3 | ##### Bing-copilot-search ##### Google-ai | Display name | Position | | --- | --- | | lakeFS | 2 | ##### Google-ai-mode ##### Chatgpt-search | Display name | Position | | --- | --- | | lakeFS | 3 | - Prompt text: Which dataset management tools make it easiest for ML engineers to query, filter, and tag unstructured data using embedding-based similarity search? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity | Display name | Position | | --- | --- | | Voxel51 | 1 | | Encord | 2 | ##### Bing-copilot-search ##### Google-ai ##### Google-ai-mode | Display name | Position | | --- | --- | | Voxel51 | 3 | | Activeloop | 8 | ##### Chatgpt-search | Display name | Position | | --- | --- | | Voxel51 | 1 | - Prompt text: Which data curation platforms integrate with workflow orchestration tools so dataset preprocessing and versioning steps run as part of an automated ML pipeline? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity | Display name | Position | | --- | --- | | lakeFS | 1 | ##### Bing-copilot-search ##### Google-ai ##### Google-ai-mode | Display name | Position | | --- | --- | | lakeFS | 1 | | Voxel51 | 7 | ##### Chatgpt-search | Display name | Position | | --- | --- | | lakeFS | 1 | - Prompt text: What are the best dataset versioning tools for a small ML team to get started with object storage without a complex infrastructure setup? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity | Display name | Position | | --- | --- | | lakeFS | 3 | ##### Bing-copilot-search ##### Google-ai ##### Google-ai-mode ##### Chatgpt-search | Display name | Position | | --- | --- | | lakeFS | 3 | - Prompt text: 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? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity | Display name | Position | | --- | --- | | lakeFS | 4 | ##### Bing-copilot-search ##### Google-ai | Display name | Position | | --- | --- | | lakeFS | 2 | ##### Google-ai-mode ##### Chatgpt-search | Display name | Position | | --- | --- | | lakeFS | 1 | - Prompt text: Which AI dataset management tools have native connectors to annotation and labeling services so curated slices can be sent for labeling without manual export? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity | Display name | Position | | --- | --- | | Voxel51 | 2 | | Encord | 4 | ##### Bing-copilot-search ##### Google-ai | Display name | Position | | --- | --- | | Voxel51 | 1 | | Encord | 5 | ##### Google-ai-mode | Display name | Position | | --- | --- | | Voxel51 | 1 | | Roboflow | 6 | ##### Chatgpt-search | Display name | Position | | --- | --- | | Voxel51 | 1 | | Encord | 3 | - Prompt text: Looking for a dataset versioning tool with a great notebook-friendly workflow — what are the best options for teams that live in Jupyter? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity | Display name | Position | | --- | --- | | lakeFS | 3 | ##### Bing-copilot-search ##### Google-ai ##### Google-ai-mode | Display name | Position | | --- | --- | | lakeFS | 3 | ##### Chatgpt-search | Display name | Position | | --- | --- | | lakeFS | 3 | - Prompt text: Which dataset versioning tools have the smoothest onboarding for teams migrating off a manual folder-based data management system? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity | Display name | Position | | --- | --- | | lakeFS | 3 | ##### Bing-copilot-search ##### Google-ai ##### Google-ai-mode ##### Chatgpt-search | Display name | Position | | --- | --- | | lakeFS | 11 | - Prompt text: Which AI data curation platforms offer the best visual dataset explorer so non-engineers on a labeling team can review samples without writing code? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity | Display name | Position | | --- | --- | | Encord | 2 | | Voxel51 | 5 | ##### Bing-copilot-search | Display name | Position | | --- | --- | | Encord | 1 | ##### Google-ai ##### Google-ai-mode | Display name | Position | | --- | --- | | Roboflow | 2 | | Voxel51 | 3 | ##### Chatgpt-search | Display name | Position | | --- | --- | | Encord | 2 | | Roboflow | 3 | | Voxel51 | 4 | - Prompt text: I'm evaluating AI dataset management tools — which ones support automated data quality checks and slice-level statistics for model evaluation sets? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity ##### Bing-copilot-search ##### Google-ai ##### Google-ai-mode ##### Chatgpt-search - Prompt text: Which data curation tools handle mixed-modality datasets — images, text, and structured metadata — in a single versioned artifact? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity ##### Bing-copilot-search | Display name | Position | | --- | --- | | lakeFS | 6 | ##### Google-ai | Display name | Position | | --- | --- | | Voxel51 | 1 | ##### Google-ai-mode | Display name | Position | | --- | --- | | Voxel51 | 3 | ##### Chatgpt-search | Display name | Position | | --- | --- | | lakeFS | 3 | - Prompt text: 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? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity ##### Bing-copilot-search ##### Google-ai ##### Google-ai-mode ##### Chatgpt-search | Display name | Position | | --- | --- | | lakeFS | 1 | - Prompt text: Which dataset versioning tools integrate best with experiment tracking platforms so training runs automatically link to the exact dataset version used? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity | Display name | Position | | --- | --- | | lakeFS | 1 | ##### Bing-copilot-search ##### Google-ai ##### Google-ai-mode ##### Chatgpt-search | Display name | Position | | --- | --- | | lakeFS | 3 | - Prompt text: Which dataset versioning tools support branching and merging semantics similar to source control for managing parallel data experiments? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity | Display name | Position | | --- | --- | | lakeFS | 1 | ##### Bing-copilot-search ##### Google-ai ##### Google-ai-mode | Display name | Position | | --- | --- | | lakeFS | 4 | ##### Chatgpt-search | Display name | Position | | --- | --- | | lakeFS | 1 | - Prompt text: 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? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity | Display name | Position | | --- | --- | | Roboflow | 1 | ##### Bing-copilot-search ##### Google-ai ##### Google-ai-mode | Display name | Position | | --- | --- | | Roboflow | 2 | ##### Chatgpt-search | Display name | Position | | --- | --- | | Roboflow | 1 | | Voxel51 | 3 | | Encord | 4 | - Prompt text: Which dataset versioning tools handle petabyte-scale training datasets without bottlenecking the data loading pipeline during distributed training? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity | Display name | Position | | --- | --- | | lakeFS | 1 | ##### Bing-copilot-search ##### Google-ai ##### Google-ai-mode ##### Chatgpt-search | Display name | Position | | --- | --- | | lakeFS | 1 | - Prompt text: What are the best AI data curation platforms for streaming random-access reads from large image datasets stored in object storage with low latency? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity ##### Bing-copilot-search ##### Google-ai ##### Google-ai-mode ##### Chatgpt-search | Display name | Position | | --- | --- | | Voxel51 | 4 | - Prompt text: Looking for a data versioning layer over object storage that handles concurrent writes from multiple experiment runs without corruption — what are my options? #### Brand position by platform Perplexity: Not available Bing-copilot-search: Not available Google-ai: Not available Google-ai-mode: Not available Chatgpt-search: Not available #### Platform rows ##### Perplexity | Display name | Position | | --- | --- | | lakeFS | 1 | ##### Bing-copilot-search ##### Google-ai | Display name | Position | | --- | --- | | lakeFS | 2 | ##### Google-ai-mode ##### Chatgpt-search | Display name | Position | | --- | --- | | lakeFS | 1 | ## Top sources ## Response excerpts ## Competitor excerpts | Platform | Competitor name | Excerpt | | --- | --- | --- | | perplexity | Voxel51 | Voxel51 FiftyOne → V7 Darwin: FiftyOne can send a dataset *view*—a curated subset—to V7 for annotation, then load the resulting annotations back into FiftyOne. | | google-ai | Voxel51 | FiftyOne (by Voxel51) * What it is: An open-source and enterprise data-centric AI tool used extensively for dataset curation, visualization, and error analysis. | | google-ai-mode | Voxel51 | voxel51.com/integrations/cvat.html)Labelbox Integration — FiftyOne 1.21.0 documentationLabel Studio Integration — FiftyOne 1.22.1 documentation 1\. | | chatgpt-search | Voxel51 | If your goal is to select a curated subset of an AI dataset, send it directly to an annotation provider, and bring the completed labels back without manually exporting files, the strongest candidates are Voxel51 FiftyOne, Encord, and Labelbox. | | chatgpt-search | Encord | If your goal is to select a curated subset of an AI dataset, send it directly to an annotation provider, and bring the completed labels back without manually exporting files, the strongest candidates are Voxel51 FiftyOne, Encord, and Labelbox. | | perplexity | Encord | [1] 2. Encord — best for richer visual curation, especially across modalities. Its Explorer supports visual browsing, metadata filters, similarity and natural-language search, and embedding plots for spotting clusters, outliers, and coverage gaps. | | bing-copilot-search | Encord | The best visual dataset explorers for non-engineers are Encord Index, Labelbox Catalog, Scale AI’s Nucleus, and VisionRepo—each offering intuitive, no-code interfaces for browsing, filtering, and reviewing samples. | | google-ai-mode | Roboflow | The top AI data curation platforms offering robust, no-code visual dataset explorers for non-engineers and labeling teams are Labelbox, Roboflow, Cleanlab Studio, and Voxel51 FiftyOne (via its web/team interfaces). | | google-ai-mode | Voxel51 | The top AI data curation platforms offering robust, no-code visual dataset explorers for non-engineers and labeling teams are Labelbox, Roboflow, Cleanlab Studio, and Voxel51 FiftyOne (via its web/team interfaces). | | chatgpt-search | Encord | If your goal is to let a labeling team browse a visual dataset, inspect individual samples, spot bad annotations, filter examples, and flag issues without writing code, I'd start with Encord, Labelbox, and V7 Darwin. | | chatgpt-search | Roboflow | ...n and annotation of complex images and videos \| Visual annotation, sample review, AI-assisted labeling \| Very good \| \| 5 \| Roboflow \| Straightforward image dataset browsing and curation \| Dataset browser, class filters, analytics, annotation overlays \| E... | | perplexity | lakeFS | ..., but these are credible options: \| Tool \| Good fit \| How it limits storage growth \| Key trade-off \| \|---\|---\|---\|---\| \| lakeFS \| Large datasets in S3-compatible object storage or a data lake; teams needing Git-like branching and immutable commits \|... | ## Trend Visibility delta: 0 Avg position delta: Not available Citation count delta: 0