AI visibility report
AI visibility report for Nomic AI in AI Data Curation and Dataset Versioning.
Outside the top three on 18 of the 25 prompts buyers actually ask.
lakeFS is cited on 15 of those losses.
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Top-3 citations across 125 prompt × platform pairs
Peer Ranking
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Platform Breakdown
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PerplexityHow to read this. Nomic AI appears in 0% of tracked prompt responses. Presence is absolute coverage; share of voice is relative citation share; sentiment measures tone only when the brand appears.
Where Nomic AI is losing
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Where Nomic AI is winning
No clear strengths identified yet.
Where Nomic AI 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 promptWhich AI dataset management tools have native connectors to annotation and labeling services so curated slices can be sent for labeling without manual export?
Competitors on 3 platforms
Track this promptI'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?
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
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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.
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 Facts
- Founded
- 2022
- HQ
- New York, USA
- Founders
- Brandon Duderstadt, Andriy Mulyar
- Employees
- 11-25
- Funding
- $17M
- Valuation
- ~$100M
- Status
- Private
Target users
Key Capabilities9
- 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
Key Use Cases7
- 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
Nomic AI customer outcomes
+30% productivity increase for tasks where Nomic was implemented; 10–20 hours saved per team per week
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
Recent Trend
How AI describes Nomic AI
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Most cited sources
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Alternatives in AI Data Curation and Dataset Versioning6
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.
Reviews
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
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.
Pricing
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.
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.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | Google AI Mode | Bing Copilot | ChatGPT | Perplexity |
|---|---|---|---|---|---|
Capability0/5 cited (0%) | |||||
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 | A competitor 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 | A competitor was cited |
Developer Experience0/5 cited (0%) | |||||
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 | A competitor 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 | A competitor was cited |
Integrations & Ecosystem0/5 cited (0%) | |||||
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 | A competitor was cited | A competitor was 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 Run0/5 cited (0%) | |||||
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? | 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 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 |
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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% | — | — |
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