Synetic

What Synetic does

Synetic AI, Inc. is a Redmond, Washington-based private company that generates physically-accurate synthetic training data for computer vision models using a procedural rendering engine. Rather than collecting and labeling real-world images, the company renders images with pixel-perfect ground truth annotations by construction, producing outputs that the company claims are statistically indistinguishable from real captures and that eliminate the sim-to-real domain gap. Its core products are synthetic datasets (custom training data covering any object, condition, or edge case), the LYNX computer vision SDK (a 2.4 MB model built entirely on Synetic data that matches a 32M-parameter transformer without any real training images), and a custom model development service in which Synetic builds complete deployed models for customer-specific SKUs, factory setups, or unique equipment. The company serves defense contractors, Fortune 500 manufacturers, security companies, robotics startups, and university research institutions across manufacturing quality control, agriculture, security, robotics, retail analytics, and logistics use cases.

The business model combines data monetization (synthetic datasets), professional services (custom model builds), and subscription/recurring revenue (the LYNX SDK). Pricing is not publicly disclosed; the go-to-market is sales-led and consultation-based, conducted via Calendly booking for direct engagement with technical staff rather than junior account representatives. Synetic is differentiated by a procedural rendering approach (explicitly positioned as not generative AI), peer-reviewed validation through a November 2025 University of South Carolina study showing 34.24% mAP improvement over real-world training across seven object detection architectures, and a content-driven marketing strategy that emphasizes technical proof over promotional claims. The technology stack runs on NVIDIA hardware with DeepStream integration on the roadmap, positioning the company as a complementary data layer to NVIDIA Omniverse.

Name

Synetic

Legal name

Synetic AI, Inc.

Website

https://synetic.ai

Company type

Private

Operating status

Operating

Headcount range

11–50 employees

Short description

Synetic AI generates physically-accurate synthetic training data for computer vision models using a procedural rendering engine, serving defense contractors, manufacturers, security firms, and robotics companies with pixel-perfect annotated datasets and the LYNX SDK.

Ownership category

akta.pro rank

Product category

Synthetic Data Generation for Computer Vision

NAICS

Scientific Research and Development Services (5417)

SIC

Services-Computer Processing & Data Preparation (7374)

akta.pro primary industry

Synthetic Data Generation Platforms (HDAAALAA)

akta.pro secondary industry

Synthetic Data & Data Augmentation for Foundation Models (HDAAACAK)

Keywords

• Synthetic data generation
• Computer vision training
• Procedural rendering
• Synthetic datasets
• Object detection models

Headquarters

HQ city

Redmond

HQ country

United States

HQ region

North America

Markets served

GTM type

B2B

Offering type

Software

Cost components

Technology or R&D, Personnel, Infrastructure, Operations

Revenue model

1. Synthetic Datasets: Generation and sale of physically-accurate synthetic training datasets with pixel-perfect annotations. Customers specify parameters and receive custom training data covering desired objects, conditions, and edge cases.
2. Custom Model Development: End-to-end model building service using synthetic data. Company creates training data for customer's specific SKUs, factory setup, or unique equipment, then either delivers data for customer to train or builds and delivers the complete model.
3. LYNX SDK: Off-the-shelf computer vision SDK product built entirely on Synetic data. 2.4 MB model matching 32M-parameter transformer performance.

Go-to-market motion1 record

Distribution channels2 records

Marketing channels4 records

Core offering

Synetic generates physically-accurate synthetic training data for computer vision models using a procedural rendering engine, producing pixel-perfect annotated images at scale without collecting or labeling real-world data. The company sells synthetic datasets, end-to-end custom model development, and the LYNX CV SDK—a 2.4 MB computer vision model built entirely on synthetic data.

Product overview

Synetic is a synthetic data generation platform for computer vision that offers two core products: Synthetic Datasets (procedurally rendered training data with pixel-perfect annotations) and Custom Models (full model development using synthetic data). The flagship LYNX SDK is a lightweight (2.4 MB) computer vision model built entirely on Synetic's synthetic data that rivals a 32M-parameter transformer. All offerings are built on procedural rendering technology that generates physically-accurate synthetic images with complete ground truth annotations at unlimited scale, validated by a peer-reviewed USC study showing 34% improvement over real-world training data.

Differentiator

Problem solved

Functional benefit

Brands

• LYNX: A 2.4 MB computer vision SDK built entirely on Synetic synthetic data. A lightweight model that matches a 32M-parameter transformer, without a single real training image.

Quantifiable outcome

• +34.24% mAP improvement over real-world training (YOLOv12: 0.240 to 0.322)
• +4 more outcomes

Named customers2 records

Segments6 records

Ideal customer profiles5 records

Technology focussed Yes

API detail

Has API

No

API docs

API detail

Core technology

AI maturity

App detail

AI capability5 records

Feature5 records

Partnerships

One partnership is on record.

• University of South CarolinacoreStrategic or Co-development Partner · 1 November 2025Research collaboration with Dr. Ramtin Zand and James Blake Seekings from USC. Jointly published peer-reviewed study in November 2025 demonstrating synthetic data superiority over real-world training data. USC researchers validated the methodology and confirmed superior feature diversity of synthetic data.

Scale indicators4 records

Recent moves1 record

Expansion highlights5 records

Direct peers

• Parallel Domain: Parallel Domain produces simulation-generated synthetic data for autonomous systems and advanced driver assistance, sharing Synetic's emphasis on physically accurate rendering for perception model training. Both companies position around closing the sim-to-real gap for high-stakes vision applications.
• CVEDIA: CVEDIA builds synthetic data solutions for computer vision and edge AI applications across transportation, retail, and industrial sectors. It shares Synetic's approach of using simulation to produce labeled training data for object detection and classification.
• Scale AI: Scale AI is a direct competitor in the AI training data market, offering both data labeling and synthetic data generation services for computer vision and other ML modalities. Both companies compete for enterprise customers building computer vision models, though Scale leans more heavily on human-in-the-loop labeling while Synetic emphasizes fully synthetic data.
• Zumo Labs: Zumo Labs produces photorealistic synthetic image and video datasets for computer vision training in domains such as mobility, retail, and robotics. It competes with Synetic in the synthetic-only data segment for enterprises that need privacy-compliant training data.
• Mindtech Global: Mindtech Global provides a synthetic data platform for vision AI training, focused on edge cases and rare events for retail, security, and smart city applications. It overlaps directly with Synetic's value proposition of generating unlimited, perfectly annotated training scenarios at scale.
• Datagen: Datagen generates synthetic visual data for computer vision training, with a similar focus on privacy-compliant datasets and photorealistic outputs. Both companies target enterprise CV use cases across retail, security, and mobility, making them direct competitors in the synthetic data platform category.
• Anyverse: Anyverse generates synthetic sensor data (camera, LiDAR, radar) for autonomous driving and ADAS perception training. It is comparable to Synetic in using physics-based simulation to produce pixel-perfect annotations for vision and multi-modal perception models.
• Sky Engine AI: Sky Engine AI offers a synthetic data platform for computer vision and deep learning, combining simulated environments with generated annotations. It is comparable to Synetic in targeting enterprise customers that need scalable, annotated training data without manual collection.

Broad incumbents

• Unity Technologies: Unity's real-time 3D engine and synthetic data tooling support computer vision training use cases through simulation-based dataset generation. It is a broader incumbent in the rendering and simulation space whose tools could be used by competitors or customers to replicate aspects of Synetic's offering.
• NVIDIA (Omniverse / Cosmos): NVIDIA provides simulation and rendering platforms (Omniverse, Cosmos) used to generate synthetic data for AI training. While NVIDIA positions these as rendering infrastructure rather than a data source, it is the largest player capable of expanding down the stack into Synetic's data layer, and Synetic's work runs on NVIDIA hardware.

Market position

Strengths5 records

Weaknesses5 records

Competitive moat4 records

Key risks6 records

Key highlights6 records

Customer concentration

• LinkedInlinkedin.com/company/synetic-ai

Revenue estimate

Valuation estimate

Number of profiles

Profiles3 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 Synetic