AI visibility report for Nomic AI
Vertical: AI Data Curation and Dataset Versioning
AI search visibility benchmark across 3 platforms in AI Data Curation and Dataset Versioning.
Presence Rate
Top-3 citations across 75 prompt × platform pairs
Sentiment
Peer Ranking
Key Metrics
Platform Breakdown
Perplexity
Gemini Search
ChatGPTOverview
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
No concise AI response excerpt is available for this brand yet.
Most cited sources2
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 Coverage
Prompt-Level Results
| Prompt | Gemini Search | ChatGPT | Perplexity |
|---|---|---|---|
Curating multimodal training datasets0/5 cited (0%) | |||
Which platform handles parallel inference across millions of files for dataset enrichment without hitting OOM on a single machine? | |||
I have millions of unlabeled videos in S3 — which tool can help me filter and enrich them with model-generated metadata before training? | |||
Looking for a Python SDK that lets me apply LLMs and vision models to clean and enrich a training dataset without moving data out of cloud storage. | |||
How do teams curate diverse, high-quality fine-tuning datasets for vision-language models from raw object storage? | |||
What's the best way to curate a large image and video dataset for training a multimodal model? | |||
Dataset versioning and lineage for ML0/5 cited (0%) | |||
What's the cleanest way to version control datasets alongside code for an ML project? | |||
Looking for a Git-like workflow for branching, committing, and merging changes to large training datasets stored in S3. | |||
How do I track dataset lineage from raw files through preprocessing to the final training set so experiments are reproducible? | |||
Need atomic commits across data and code so I can roll back a model regression to its exact training snapshot — what works at scale? | |||
Which tool gives me reproducible dataset snapshots without copying terabytes of data? | |||
Detecting and fixing label errors0/5 cited (0%) | |||
What's the fastest workflow to find and re-label outliers in a 1M-image dataset? | |||
Looking for a tool that surfaces ambiguous and noisy labels in a multimodal dataset before I retrain. | |||
Which platforms use confident learning or model-based heuristics to flag bad labels for review? | |||
How can I automatically detect mislabeled examples in a computer vision training set? | |||
How do production ML teams audit annotation quality across labeling vendors before they ship to training? | |||
Embedding-based dataset exploration and deduplication1/5 cited (20%) | |||
Which platform lets me search a dataset by example — give an image or text, get nearest neighbors with metadata? | |||
How do I find near-duplicate examples across a multimodal training corpus before fine-tuning? | |||
How are teams using embedding maps to surface coverage gaps and bias in training data? | |||
What's the best way to explore a huge text dataset visually using embeddings? | |||
Looking for a tool that clusters and deduplicates an image dataset based on semantic similarity. | |||
Reproducible data pipelines over object storage0/5 cited (0%) | |||
Looking for a Python-native data pipeline framework that handles parallelism, checkpointing, and lineage without ETL infrastructure. | |||
What's the cleanest way to author a dataset pipeline locally and scale it to hundreds of cloud workers without rewriting? | |||
Which tool supports incremental dataset builds — only reprocess the new files when underlying storage changes? | |||
How do I build a reproducible data preprocessing pipeline that reads from S3, applies Python transforms, and writes a versioned dataset? | |||
How do I keep training datasets in sync with raw object storage while preserving versioned metadata, lineage, and access control? | |||
Strengths1
What's the best way to explore a huge text dataset visually using embeddings?
Avg # 3.0 · 1 platform
Gaps2
Which tool gives me reproducible dataset snapshots without copying terabytes of data?
Competitors on 1 platform
What's the best way to curate a large image and video dataset for training a multimodal model?
Competitors on 1 platform
Vertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Voxel51 | 4.0% | 23.1% | 0.0% | 2.7% | 1.3% | #6.0 | +0.50 |
| 2 | Encord | 4.0% | 38.5% | 0.0% | 4.0% | 0.0% | #6.4 | +0.00 |
| 3 | lakeFS | 2.7% | 23.1% | 0.0% | 2.7% | 1.3% | #4.7 | +0.00 |
| 4 | Nomic AI | 1.3% | 15.4% | 1.3% | 0.0% | 0.0% | #6.0 | +0.70 |
| 5 | Activeloop | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 6 | DataChain | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 7 | Roboflow | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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