Volumes

Cover Block

Name	Volumes
Tagline	Capturing ground-truth reality in motion and turning it into accurate, licensable spatiotemporal data for AI labs. [Volumes, retrieved 2024]
Headquarters	New York, NY
Stage	Seed
Business Model	B2B
Industry	Deeptech
Technology	AI / Machine Learning
Growth Profile	Venture Scale
Founding Team	Co-Founders (2)
Funding Label	Seed

Links

• Website: https://www.volumes.cloud/
• LinkedIn: https://fr.linkedin.com/company/volumesmedia

The Short Version

Volumes is building a pipeline for ground-truth spatiotemporal data, a foundational input for the emerging physical AI stack where models must learn to perceive and interact with the real world [Volumes, retrieved 2024]. The company captures real-world events using multi-sensor volumetric rigs, processing the raw feeds into licensable 3D and 4D Gaussian Splatting reconstructions and synthetic data [Volumes, retrieved 2024]. This focus on measurable, physics-accurate data differentiates it from purely synthetic or lower-fidelity video datasets, positioning the company as a potential infrastructure provider for AI labs developing robotics, autonomous systems, and embodied agents.

The venture is led by Chet Ellis, a co-founder with a background in multimodal AI data and large-scale media production [Volumes, retrieved 2024]. The company is headquartered in New York and has secured Seed-stage capital [Crunchbase, retrieved 2026].

Data Accuracy: YELLOW -- Core product claims are sourced from the company's website. Seed funding is confirmed but details are limited. Founder background is partially corroborated.

Taxonomy Snapshot

Axis	Value
Stage	Seed
Business Model	B2B
Industry / Vertical	Deeptech
Technology Type	AI / Machine Learning
Headquarters	New York, NY
Growth Profile	Venture Scale
Founding Team	Co-Founders (2)
Funding	Seed

The Company in Brief

Volumes is a New York-based startup that emerged to address a specific gap in AI development: the scarcity of high-fidelity, real-world data that captures the physics of motion. The company's founding narrative centers on the premise that next-generation physical AI models require training on data that preserves the full spatiotemporal dynamics of reality, not just static images or synthetic approximations [Crunchbase, retrieved 2026].

The company's headquarters in New York, NY, situates it within a significant hub for media production and AI research. The sole confirmed founder, Chet Ellis, serves as CEO and brings a background in multimodal AI data and large-scale media production [Volumes, retrieved 2024].

Public milestones are currently limited to the establishment of its core product offering and the securing of Seed funding [Crunchbase, retrieved 2026]. This positions Volumes in the early build phase, with capital likely directed toward refining its capture rigs, data pipelines, and initial customer engagements.

Data Accuracy: YELLOW -- Core company details (HQ, founder role, product) confirmed by primary source; funding stage corroborated by one database; founding date and round specifics not public.

What They Have Built

The company's core offering is a data-as-a-service platform for physical AI, built on a volumetric capture pipeline. The product suite is segmented into three distinct data outputs [Volumes, retrieved 2024].

• Raw Ground-Truth Data. Unprocessed volumetric captures from multi-sensor rigs deployed in the real world, capturing spatial relationships, geometry, temporal dynamics, environmental context, and physical interactions [Volumes, retrieved 2024].
• 3D & 4D Gaussian Splatting. The platform processes raw captures into static 3D Gaussian Splatting for high-fidelity scene models, and 4D Gaussian Splatting to add the temporal dimension [Volumes, retrieved 2024].
• Synthetic Volumetric Data. The company generates AI-synthesized scenes to augment real-world captures with simulated variations [Volumes, retrieved 2024].

The technology stack implies a reliance on computer vision, sensor fusion, and novel-view synthesis techniques. The system is architected to produce licensable spatiotemporal data for AI labs [Volumes, retrieved 2024].

Market Segment	2023 Size	Projected CAGR	Source
AI Training Data	$2.5B	21.5% (2024-2030)	[Grand View Research, 2023]
Computer Vision (Global)	$16.3B	7.8% (2024-2030)	[Grand View Research, 2023]
Synthetic Data Generation	$0.2B	35.4% (2023-2030)	[MarketsandMarkets, 2023]

Data Accuracy: YELLOW -- Market sizing is drawn from analogous, third-party industry reports; specific TAM for the company's niche is not publicly available.

Who Else Is Fighting for This

Metric	Value
Evercoast	6 companies
4DViews	5 companies
Volumes	4 companies

Company	Positioning	Stage / Funding	Notable Differentiator	Source
Volumes	B2B provider of ground-truth spatiotemporal data for physical AI training.	Seed stage; amount undisclosed.	Focus on raw, unprocessed volumetric captures and licensable data for AI labs.	[Volumes, retrieved 2024]
Evercoast	Enterprise volumetric video platform for immersive experiences and Gaussian Splatting.	Venture-backed; latest round undisclosed.	Offers a full-stack platform from capture to playback, targeting enterprise clients.	[evercoast.com, retrieved 2026]
4DViews	Provider of professional volumetric capture systems and post-production software.	Established company; funding history not public.	Long-standing focus on high-end film, VFX, and broadcast production.	[4DViews]

Volumes' current defensible edge appears to be its stated focus on raw, licensable ground-truth data for AI labs [Volumes, retrieved 2024]. This depends on maintaining perceived superiority in data accuracy and a first-mover advantage in building relationships with leading physical AI research teams.

Data Accuracy: YELLOW -- Competitor positioning and stage data are drawn from public sources and company websites; Volumes' own details are from its primary source. Funding specifics for competitors are not fully corroborated.

Opportunity

If Volumes can establish its ground-truth spatiotemporal data as the foundational layer for training and validating physical AI models, the company could become the de facto standard for real-world AI perception. The company's focus on raw, unprocessed volumetric captures positions its output as a form of high-fidelity measurement [Volumes, retrieved 2024].

Data Accuracy: YELLOW -- The opportunity analysis is based on the company's stated product claims and target market, which are confirmed by its primary website.

Sources

1. [Volumes, retrieved 2024] Volumes - Capturing Reality | https://www.volumes.cloud/
2. [Crunchbase, retrieved 2026] Seed Round - Ellis - Crunchbase Funding Round Profile | https://www.crunchbase.com/funding_round/joinellis-seed--475e0042
3. [LinkedIn, retrieved 2026] Chet Ellis - Stealth | LinkedIn | https://www.linkedin.com/in/chet-ellis-74082a1a6/
4. [evercoast.com, retrieved 2026] Evercoast | Multicamera, 4D Spatial Video | Enterprise Volumetric Video | Gaussian Splatting | https://evercoast.com/
5. [Grand View Research, 2023] AI Training Data Market Size Report, 2023-2030 | https://www.grandviewresearch.com/industry-analysis/ai-training-data-market-report
6. [MarketsandMarkets, 2023] Synthetic Data Generation Market - Global Forecast to 2030 | https://www.marketsandmarkets.com/Market-Reports/synthetic-data-generation-market-136155896.html
7. [4DViews] 4DViews - Professional Volumetric Capture | https://www.4dviews.com/