What Synthos does

Synthos AI GmbH is a Berlin-based synthetic data platform founded in 2024 that generates photorealistic, physics-accurate 3D environments with pixel-perfect ground-truth labels for enterprise teams training physical AI systems — including robotics, autonomous vehicles, industrial inspection, and service robotics. The platform addresses three structural bottlenecks in physical AI development: the months-long cycle of real-world data collection, the $50–200/hour cost of manual annotation, and the impossibility of safely capturing rare or dangerous edge cases. According to the company, seven enterprise customers across the EU and US are already using the platform, generating €480K in ARR that is reportedly growing 18% month-over-month.

The Synthos product is built around three integrated modules exposed via dashboard and API: Scene Generation, which renders physics-accurate warehouses, factory floors, and streets from natural-language prompts; Auto-Labeling, which attaches segmentation masks, depth maps, bounding boxes, and joint angles to every frame to eliminate human annotation; and Edge Case Factory, which synthesizes rare events such as sensor failures, unusual object configurations, and adversarial lighting conditions. Rendering runs in parallel on GPU clusters and the company claims a "prompt-to-dataset in under 10 minutes" workflow, with outputs compatible with ROS2, PyTorch, NVIDIA Isaac Sim, and MuJoCo. Generated datasets are packaged into four vertical use cases — Warehouse Automation, Autonomous Vehicles, Industrial Inspection, and Service Robotics — with multi-modal LIDAR + camera outputs for AV perception work.

The business model is a gated, high-touch enterprise motion with multi-year contracts and likely usage-based components tied to volume of scenarios rendered; pricing is not publicly disclosed and access requires a "Request Access" application reviewed personally by the team. Go-to-market is being built through a dedicated Enterprise Sales Lead role focused on the EU region, complemented by a developer-relations and content-marketing motion aimed at the robotics and ML community. The company is backed by a €3.8M seed round closed in Q1 2025, led by EQT Ventures with High-Tech Gründerfonds, Fly Ventures, and Antler, and operates with an 11-person team in Berlin that is actively hiring across ML, robotics simulation, full-stack, sales, and developer relations.

Name

Synthos

Legal name

Synthos AI GmbH

Website

https://synthos.ink

Company type

Private

Founded year

2024

Operating status

Operating

Short description

Synthos is a Berlin-based synthetic data platform that generates photorealistic, physics-accurate environments with pixel-perfect labels for enterprise robotics, autonomous vehicle, and industrial AI teams across the EU and US.

Ownership category

akta.pro rank

Product category

Synthetic Data Platform for Robotics

NAICS

Software Publishers (51321)

SIC

Services-Prepackaged Software (7372)

akta.pro primary industry

Synthetic Data Generation Platforms (HDAAALAA)

akta.pro secondary industry

Model Development & Training Platforms (AutoML, Notebooks, Feature Stores) (HDAEANAB)

Keywords

• Synthetic data generation
• Robotics training data
• Computer vision datasets
• Autonomous vehicle simulation
• Physical AI platform

Headquarters

HQ city

Berlin

HQ country

Germany

HQ region

Europe

Offices1 record

Markets served

GTM type

B2B

Offering type

Software

Cost components

Personnel, Infrastructure, Technology or R&D, Marketing or Sales, Operations

Revenue model

1. Enterprise synthetic data subscriptions: Recurring enterprise contracts for synthetic dataset generation and access to the Synthos platform. Indicated by €480K ARR growing 18% MoM across 7 enterprise clients in EU & US, gated "Request Access" onboarding, and explicit Enterprise Sales Lead role owning the full cycle. Pricing not publicly disclosed.
2. Usage-based dataset generation: Likely consumption / usage-based component tied to volume of scenarios, environments, or labeled frames rendered. Consistent with "Generate thousands of scenarios in parallel," "datasets compatible with ROS2, PyTorch, and Isaac Sim," and export-oriented delivery model.
3. Capital from seed funding: €3.8M seed round closed Q1 2025, led by EQT Ventures with High-Tech Gründerfonds, Fly Ventures, and Antler. Provides runway for scaling enterprise sales and product.

Pricing tiers

Go-to-market motion1 record

Distribution channels3 records

Marketing channels6 records

Core offering

Synthos is a synthetic-data-as-a-service platform that generates photorealistic 3D environments with physics-accurate properties and pixel-perfect ground-truth labels (segmentation masks, depth maps, bounding boxes, joint angles) for training physical AI systems. Customers describe environments such as warehouses, factory floors, or streets in natural language, and Synthos renders thousands of labeled scenarios on GPU clusters in minutes, including rare edge cases that cannot be safely captured in the real world. Output datasets are exported in formats compatible with ROS2, PyTorch, Isaac Sim, and MuJoCo for direct consumption by robotics and ML training pipelines.

Product overview

Synthos offers a single unified synthetic-data platform composed of three core modules — Scene Generation, Auto-Labeling, and Edge Case Factory — exposed via both a dashboard and an API. Scene Generation turns natural-language environment descriptions into physics-accurate 3D renders, Auto-Labeling attaches pixel-perfect ground-truth annotations to every frame, and Edge Case Factory generates rare or dangerous on-demand events; together these modules are packaged into four vertical offerings (Warehouse Automation, Autonomous Vehicles, Industrial Inspection, and Service Robotics) and produce datasets compatible with downstream robotics and ML tooling such as ROS2, PyTorch, Isaac Sim, and MuJoCo.

Differentiator

Problem solved

Functional benefit

Products and services

• Synthos Platform Unified synthetic-data-as-a-service platform combining Scene Generation, Auto-Labeling, and Edge Case Factory modules behind a single dashboard and API for enterprise robotics, AV, industrial inspection, and service robotics teams.
• Scene Generation Natural-language-driven module that renders physics-accurate synthetic environments (warehouses, factory floors, streets) in minutes via GPU-parallelized rendering.
• Auto-Labeling Automated labeling module that ships every rendered frame with pixel-perfect segmentation masks, depth maps, bounding boxes, joint angles, and skeleton tracking, eliminating human annotation from the training data pipeline.
• Edge Case Factory Module that generates rare, dangerous, or regulation-critical events on demand, including sensor failures, unusual object configurations, adversarial lighting, and pedestrian/cyclist edge cases, to expose long-tail failure modes.
• Warehouse Automation Use Case Vertical offering that generates thousands of shelf layouts, product sizes, lighting conditions, and conveyor-failure or occlusion edge cases for training pick-and-place and grid-based warehouse robots.
• Autonomous Vehicles Use Case Vertical offering for AV perception model validation that produces rare weather (fog, heavy rain, snow), sensor-degradation, and pedestrian/cyclist edge cases with multi-modal LIDAR + camera labeled datasets.
• Industrial Inspection Use Case Vertical offering for defect detection model training that synthesizes surface defects, dimensional deviations, and assembly errors across unlimited product variants, materials, lighting angles, and camera positions, producing class-balanced datasets with defect severity labels.
• Service Robotics Use Case Vertical offering that generates multi-room environments (hospitals, hotels, offices, homes) with diverse human poses, movements, and interaction scenarios, with full skeleton tracking and semantic scene graph export.

Quantifiable outcome

• 40× faster than real-world data labeling
• +7 more outcomes

Named customers7 records

Segments5 records

Ideal customer profiles4 records

Technology focussed Yes

API detail

Has API

Yes

API docs

API detail

Core technology

AI maturity

App detail

Integration4 records

AI capability9 records

Feature6 records

Scale indicators13 records

Recent moves6 records

Expansion highlights6 records

Broad incumbents

• Scale AI: Dominant data labeling and evaluation platform for ML with growing synthetic data capabilities (Scale Synthetic). Overlaps with Synthos in automated labeling and dataset generation, though Scale's primary positioning remains human-in-the-loop annotation at massive scale.
• Applied Intuition: Well-funded simulation and infrastructure provider for autonomous vehicle and robotics development. Overlaps with Synthos in synthetic data and edge-case generation but operates as a broader platform with much larger engineering and sales footprint.
• NVIDIA (Omniverse Replicator / Isaac Sim): Provides Omniverse Replicator and Isaac Sim for synthetic data generation in robotics and AV — the same ecosystem Synthos integrates with. As a platform incumbent, NVIDIA represents both an integration partner and the largest competitive threat via potential native synthetic data features.

Direct peers

• Anyverse: Spanish synthetic data company focused on automotive perception training with multi-modal sensor simulation. Directly comparable to Synthos's AV use case, particularly in LiDAR + camera labeled dataset generation, though Anyverse is more vertically concentrated in automotive.
• Parallel Domain: San Francisco-based synthetic data platform for autonomous systems and robotics, generating labeled 3D environments for perception model training. Directly comparable to Synthos in scene generation, automated labeling, and edge-case synthesis, though Parallel Domain is more established in AV.
• Synthesis AI: San Francisco-based synthetic data platform generating photorealistic face, body, and object data for computer vision training. Directly comparable to Synthos in synthetic data-as-a-service model with auto-labeling, though focused more on human-centric CV than robotics or AV.
• Foretellix: Israeli company providing scenario generation and coverage-driven validation for autonomous vehicles. Directly comparable to Synthos's Edge Case Factory and AV perception use case, focusing on measurable coverage of long-tail scenarios rather than photorealism.
• Datagen: Synthetic visual data provider for computer vision, specializing in human-centric and environment simulation. Directly comparable to Synthos in synthetic data generation and auto-labeling, with a similar enterprise GTM approach focused on privacy-preserving and edge-case-rich datasets.

Others

• CARLA (open-source): Open-source driving simulator widely used for AV perception research and edge-case scenario generation. Comparable to Synthos's AV use case as a free alternative for synthetic AV data, representing both a community-standard baseline and a potential substitution threat for budget-constrained academic and research customers.

Emerging players

• Roboflow: Computer vision dataset management and training platform with growing synthetic data features. Comparable to Synthos at the dataset pipeline and developer-tooling layer, though Roboflow primarily serves general CV rather than physical AI-specific 3D scene generation.

Market position

Strengths5 records

Weaknesses5 records

Competitive moat4 records

Key risks7 records

Key highlights7 records

Customer concentration

Revenue estimate

Valuation estimate

Number of profiles

Profiles4 records

Funding overview

Funding rounds

Investors

Funding detail is available on the Subscription and Enterprise plan.Contact sales →

M&A

Investments

M&A and investment is available on the Subscription and Enterprise plan.Contact sales →

Frequently asked questions about Synthos