Engineering organizations are generating unprecedented volumes of sensor, test, and simulation data — yet many AI initiatives stall before production. Models and AI agents fail not because of algorithms, but because training data lacks coverage of rare, safety-critical, and failure scenarios. Physical testing can’t scale to fill this gap.
That’s why we’re excited to announce a new partnership between Viviota and Rockfish Data, a synthetic data platform focused on improving AI model and agent robustness through high-fidelity data generation and augmentation.
Together, we are combining production-grade engineering data analytics and machine learning pipelines with high-fidelity synthetic data generation and AI-driven augmentation to help engineering teams accelerate innovation, improve model quality, and validate AI agents and models under real-world and edge-case conditions across the automotive, aerospace, and advanced manufacturing industries.
While Viviota ensures engineering data is production-ready, Rockfish ensures AI models trained on that data are resilient — even when real-world edge cases are scarce or unavailable.
Operationalizing Real Engineering Data
Viviota’s Time-to-Insight® platform enables organizations to operationalize engineering data at scale. Rather than relying on ad-hoc scripts and manual workflows, Time-to-Insight automates ingestion, contextualization, analytics pipelines, feature extraction, and machine learning workflows across massive engineering datasets.
Engineering teams gain faster insight delivery, consistent analytics, and scalable pipelines that move beyond desktop analysis into production-grade workflows.
Expanding Coverage with Synthetic Data
Rockfish Data addresses one of the biggest bottlenecks in AI development: the availability and usability of high-quality training data. Using synthetic data generation and intelligent augmentation techniques, Rockfish expands real datasets to improve coverage of rare events, edge cases, and safety-critical scenarios — without waiting for costly physical testing or lengthy data-collection cycles.
For example, Rockfish can generate synthetic sensors and test data representing rare environmental conditions, failure modes, or operating regimes that may occur only once in millions of miles — enabling teams to validate models long before those conditions appear in the real world.
This enables faster iteration, improved generalization, and more robust models.
A Closed-Loop Data and AI Pipeline
Together, Viviota and Rockfish create a closed-loop workflow:
The result is faster development cycles, higher model accuracy, reduced testing costs, and improved engineering confidence.
Who Benefits
This joint solution delivers immediate value to:
Engineering Teams
Engineering Leadership
Target Use Cases
Learn More
If you’re interested in accelerating your engineering analytics and AI initiatives using real and synthetic data, we’d love to connect.
👉 Contact: barry.hutt@viviota.com
👉 Website: www.viviota.com