Built for rapid time to value
Decisions at scale
Get the information you need, when you need it to make decisions in real time.LEARN MORE
Petabyte scale data
Easily manage large volumes of data in a lakehouse that operates at petabyte-scale in production environments.LEARN MORE
Tuned ML algorithms
Effective and efficient, designed for specific vertical applications and workflows at scale.LEARN MORE
A fundamentally different approach to AI
Packaged AI applications from SymphonyAI drive rapid value in production environments. No building from scratch. No expensive services deployments.
Successful AI requires
Scale, security, performance, cloud and on-prem deployment
SymphonyAI uniquely drives rapid value in production environments
A range of advanced ML algorithms at scale, with feature engineering applied to specific use cases for proven results. The Eureka ML platform uses various advanced ML algorithms at scale, with feature engineering applied to specific use cases for proven results. SymphonyAI applications use the right supervised, semi-supervised, or unsupervised machine learning tuned and optimized to address specific industry use cases.
For time series and demand forecasting for industrial manufacturing and retail assortment and promotions.
For risk detection and compliance in financial services.
For marketing and merchandizing insights and customer predictions in retail/CPG.
Computer vision-based predictions
For quality applications in industrial and forecasting for retail store intelligence.
The Eureka AI platform
Best-in-class AI technology
Next-gen predictive and generative Eureka AI architecture
The Eureka ML platform supports all open-source libraries, and Python SDK to help ML engineers and data scientists move fast to tune, train, and deploy ML as needed. The platform runs training and inference loads, using both CPUs and GPUs as needed.
The advanced Eureka ML platform’s purpose-built MLOps tools manage ML algorithms and models so applications are accurate and powerful as the day they went live, and learn over time.
- Model drift management: Simple and easy to manage model drift and retraining
- Automatic Feedback: Built in tools help tune ML models to make sure they are operating within set constraints
- Dynamic learning: On historical data or fresh, real-time data to unearth new elements that should be incorporated