What Rockfish Data does

Rockfish Data is a generative AI software company that develops an enterprise-grade synthetic data platform for creating privacy-safe tabular and time-series operational data. Founded to commercialize research from Carnegie Mellon University, the platform addresses data silo and privacy challenges that prevent organizations from sharing or operationalizing sensitive data for AI/ML training, analytics, and testing. Technical co-founders include Vyas Sekar and Giulia Fanti from Carnegie Mellon, alongside CEO Muckai Girish and co-founder Nathan Haugo.

The platform supports multiple proprietary generative AI model architectures — TimeGAN (DoppelGANger-based), TabGAN, Tab Transformer, and Time Transformer — with a configurable Workbench, Python SDK for developer integration, comprehensive privacy evaluation metrics (linkability, inference attack, memorization), and a Model Store for continuous training. Distribution occurs through three channels: a Python SDK for direct developer use, a Docker container registry for hybrid and on-premise enterprise deployments, and the Snowflake AI Data Cloud Marketplace (announced March 2026). The company maintains a dual go-to-market approach combining developer-led adoption with enterprise field sales, with primary customer segments in telecom operators, network technology providers, and broader enterprises plus public sector organizations facing data silo constraints.

Rockfish operates as a cloud-native SaaS platform with subscription-based revenue mechanics; no pricing tiers are publicly disclosed. The company has raised approximately $6 million in total funding through a pre-seed round led by TEN13 in mid-2024 and a $4 million seed round led by Emergent Ventures in January 2025, with Foster Ventures and existing customers also participating.

Name

Rockfish Data

Legal name

Rockfish Data

Website

https://rockfish.ai

Company type

Private

Founded year

2024

Operating status

Operating

Headcount range

11–50 employees

Short description

Rockfish Data is a generative AI company building an enterprise-grade synthetic data platform for generating privacy-safe tabular and time-series operational data, primarily serving telecom operators, network providers, and public sector organizations facing data silo constraints.

Ownership category

akta.pro rank

Product category

Synthetic Data Platform

NAICS

Software Publishers (5132)

SIC

Services-Computer Programming, Data Processing, Etc. (7370)

akta.pro primary industry

Synthetic Data Generation Platforms (HDAAALAA)

akta.pro secondary industries

Synthetic Data & Data Augmentation for Foundation Models (HDAAACAK), Data Sharing, Data Exchange & Data Marketplace Platforms (HDAEABAH)

Keywords

• Synthetic data generation
• Generative AI software
• Privacy-preserving analytics
• Enterprise data sharing
• Tabular data synthesis

Headquarters

HQ city

San Ramon

HQ country

United States

HQ region

North America

Markets served

GTM type

B2B

Offering type

Software

Cost components

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

Revenue model

1. SaaS Platform Subscription: Rockfish Data operates as a cloud-native SaaS platform offering synthetic data generation capabilities. The platform is accessed via SDK and API, suggesting a subscription-based model with usage-based components for enterprise deployments.

Go-to-market motion2 records

Distribution channels3 records

Marketing channels4 records

Core offering

Rockfish Data develops and sells a generative-AI synthetic data platform that converts real tabular datasets into privacy-safe, statistically representative synthetic data for sharing, analytics, and AI model training. The platform exposes multiple model architectures (GANs and transformers), supports no-code use via a Workbench, programmatic use via a Python SDK, and integrates natively with the Snowflake AI Data Cloud.

Product overview

Rockfish Data offers the Rockfish Synthetic Data Platform as its core enterprise-grade offering for generating synthetic tabular and time-series operational data. The platform consists of the Rockfish Workbench (core platform), the Rockfish Python SDK for developer integration, and multiple generation models including Rockfish DG (DoppelGANger for time-series), Rockfish Tabular GAN (for fast tabular generation), Rockfish Tabular Transformer, and Rockfish Time Transformer. A Snowflake AI Data Cloud integration enables telecom operators to generate privacy-safe synthetic network data. The platform is based on research from Carnegie Mellon University.

Differentiator

Problem solved

Functional benefit

Products and services

• Rockfish Synthetic Data Platform Enterprise SaaS platform that uses generative AI to generate privacy-safe, statistically representative synthetic data from real tabular datasets, with built-in privacy and quality evaluation. Targeted at telecom operators, enterprises, and public-sector data teams that need to share, analyze, or train AI models on sensitive data.
• Rockfish Python SDK A Python software development kit that allows data scientists and engineers to programmatically generate synthetic data using Rockfish's generative models, integrate synthetic data generation into pipelines, and tune model and privacy parameters via code.
• Rockfish / Snowflake AI Data Cloud Integration A packaged integration that delivers Rockfish's synthetic data generation capabilities inside the Snowflake AI Data Cloud, enabling Snowflake customers to generate and consume synthetic data without moving data out of Snowflake.

Quantifiable outcome

• Synthetic data can match traditional research results with 95% alignment in validation studies
• +1 more outcomes

Named customers2 records

Segments3 records

Ideal customer profiles2 records

Technology focussed Yes

API detail

Has API

Yes

API docs

API detail

Core technology

AI maturity

App detail

Integration1 record

AI capability2 records

Feature7 records

Partnerships

One partnership is on record.

• SnowflakecoreTechnology or Integration · 2 March 2026Rockfish Data announced integration with Snowflake's AI Data Cloud platform to provide telecom operators and network technology providers with privacy-safe synthetic data. The solution generates realistic network telemetry and observability data enabling validation of AI/ML models and automation workflows before deployment to live networks. Available on Snowflake Marketplace.

Scale indicators3 records

Recent moves4 records

Expansion highlights5 records

Direct peers

• Tonic.ai: Enterprise synthetic data platform focused on de-identifying and synthesizing production data for testing, development, and ML training. Closely matches Rockfish's enterprise SaaS model and tabular data focus, with comparable privacy and fidelity evaluation tooling.
• Gretel AI: Synthetic data platform offering APIs and SDKs to generate synthetic tabular, time-series, and text data with privacy guarantees. Directly comparable to Rockfish in generative AI-based synthetic data generation, though Gretel has historically emphasized developer APIs and a broader horizontal customer base.
• Hazy: Enterprise synthetic data provider for financial services and other regulated industries, focused on generating synthetic tabular datasets that preserve statistical properties while protecting privacy. Comparable in vertical-enterprise positioning and model architecture.
• Mostly AI: Synthetic data platform specializing in privacy-safe synthetic tabular and time-series data for enterprise AI and analytics use cases. Highly comparable technology stack and target buyer profile, with a stronger presence in European regulated industries.
• YData: Synthetic data and data quality platform offering tools for generating, profiling, and augmenting tabular and time-series data. Direct overlap on generative AI for structured data, with comparable developer SDK and enterprise deployment options.

Emerging players

• Synthesis AI: Synthetic data company focused on computer vision and facial data generation. Tangentially comparable as a synthetic data specialist using generative AI, but differentiated by modality (vision rather than tabular/time-series).
• MOSTLY AI (Synthetic Data SDK / open source alternatives): Open-source synthetic data libraries and emerging SDK-based vendors offering tabular GAN-based synthesis. Represents the developer-friendly, lower-cost alternative path that could compete with Rockfish's SDK-driven PLG motion.

Broad incumbents

• Snowflake: Cloud data platform that now offers synthetic data capabilities through its AI Data Cloud and Marketplace. Relevant as both a distribution partner and a potential competitive incumbent that could absorb synthetic data functionality into its native platform.
• Databricks: Unified data and AI platform with synthetic data and Unity Catalog features for data sharing and ML training. Represents the broad incumbent threat as enterprise buyers increasingly expect native synthetic data tooling from major data platforms.
• AWS SageMaker (and other cloud provider synthetic data services): Hyperscaler cloud platforms that increasingly offer synthetic data and data augmentation features within their ML platforms. Relevant as the platform-level competitor that enterprise buyers may default to when evaluating synthetic data tooling.

Market position

Strengths4 records

Weaknesses4 records

Competitive moat5 records

Key risks5 records

Key highlights6 records

Customer concentration

• Xx.com/rockfishdata

Revenue estimate

Valuation estimate

Number of profiles

Profiles4 records

Funding overview

Funding rounds3 records

Investors5 records

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 Rockfish Data