Advanced Data & Model Orchestration

Manage complex data flows and ML models in a low-code environment – without stitching together separate open-source tools and hyperscaler services.

• Integrated data store for operational, structured and semi-structured data used in scoring scenarios 
• Native REST / SOAP API integrations with internal systems and external data providers 
• Message queues and streaming platforms (incl. Kafka) for asynchronous and event-driven processing 
• Rich data type support: objects, blobs, dictionaries, vectors, arrays 
• Stream processing with a tightly integrated feature store for real-time aggregation and profile updates 
• Built-in data quality layer: input validation, standardization, enrichment 
• Flexible model import: PMML, Java, Python, Groovy and R scoring artifacts 
• Support for advanced ML/AI models: neural networks, classifiers, segmentation and rule-based models 
• Model/scenarios versioning and testing via UI or API, including safe rollouts and A/B comparisons 
• Parallel execution inside a single scenario, enabling multiple models to be scored in one request – without hand-crafted containers or custom microservices 

Powerful Decision Workflow Engine

Design, orchestrate and expose ML-powered decision workflows in hours instead of days – including all integrations and business logic around the model:

• Visual low-code editor for end-to-end workflows – from data access and feature transformations, through ML models and rules, to external system calls in a single, transparent flow 
• Prebuilt nodes and connectors for databases, message queues, APIs, feature store and files – no custom glue code or separate workflow engine to maintain 
• Native scripting support (Groovy, Python, R) for advanced logic where needed, without losing transparency and governance of the visual scenario 
• Built-in decision rules framework for fraud checks, cross-checks, thresholds, overrides and business policies co-located with the ML model 
• Seamless JSON/YAML import/export for definitions, test cases and integration contracts 
• Real-time testing, what-if analysis and monitoring via UI or API, enabling fast iteration with business stakeholders 
• Modular deployment – one scenario as one production service, automatically exposed as an API endpoint, without hand-crafted containers, custom microservices or complex MLOps plumbing 

Seamless Production Deployment

Move ML-powered decision workflows from design to production with a single click – without DevOps, containers or manual infrastructure management:

• One-click deployment of workflows and ML models as production-grade services, without building and maintaining your own MLOps layer 
• Automatic provisioning of a secure, scalable execution environment – no need to manage Docker/Kubernetes, application servers or infrastructure scripts 
• Enterprise-grade security by design: built-in mechanisms for authentication, authorization and data encryption, aligned with high security standards and regulatory requirements 
• Out-of-the-box integrations for data exchange and action triggers, including REST/SOAP APIs, message queues, webhooks and external systems 
• Configuration promotion across DEV/TEST/UAT/PROD via UI or API, with consistent versioning, audit trail and rollback options 
• Standardized data access in workflows through REST, SOAP and other APIs – without writing custom glue code for integrations 

Reporting, Monitoring & Continuous Improvement

Ensure full transparency, performance and auditability across your ML workflows – from single-model experiments to large-scale production scoring.

• Real-time monitoring of models and processes with a built-in metrics stack based on InfluxDB and Grafana dashboards 
• Step-level execution logs for every workflow node, capturing inputs, outputs, timings and status for each request – enabling deep debugging, root-cause analysis and regulatory audit trails 
• Configurable streaming export of analytical and decision data to relational databases (RDBMS) for downstream analytics, BI and regulatory reporting 
• Access to full and partial computation results for explainability, champion–challenger experiments and offline analysis 
• Output and intermediate-result caching for faster processing and reduced infrastructure load 
• Non-disruptive updates of running processes, models and scenarios – apply changes without downtime and with full version control 
• Scalable architecture for growing workloads and new use cases, from batch processing to high-volume real-time scoring 
• KPI and SLA reporting for both technical and business metrics, including latency, throughput, error rates, conversion, loss and fraud-detection performance 
• Integration with BI and analytics tools for advanced reporting, dashboards and self-service analytics 
• Tight integration with AutoML (ABM) to support continuous model optimization and automated challenger testing in production 

Cost-Efficient

Optimized for maximum efficiency without sacrificing model accuracy – across infrastructure, operations and time-to-production.

• Cut infrastructure expenses by serving more traffic per node and reducing the number of components you need to run   
• Reduce resource usage and license footprint with a high-performance, compute-efficient scoring engine   
• Accelerate time-to-production with low-code workflows and one-click deployment, eliminating most custom Dev and DevOps work   
• Simplify maintenance with a single, integrated MLOps layer instead of a patchwork of open-source tools and hyperscaler services

High-Performance

Engineered for ultra-low latency and high-throughput ML scoring at scale.

• Reactive JVM engine based on Vert.x with asynchronous, non-blocking event processing – a single node can handle hundreds of concurrent requests and thousands of scoring calls per second.  
• End-to-end execution in a single optimized runtime – data access, feature engineering, ML models, business rules and integrations run within one JVM process, minimizing network hops and serialization overhead typical for Docker + Python + MLflow microservice stacks.  
• Vertical and horizontal scaling via a distributed event bus cluster, allowing you to add JVM processes or nodes without redesigning workflows or duplicating complex infrastructure.
• Built-in back-pressure, component isolation and fault tolerance, keeping latency low and throughput stable even under peak loads and bursty traffic 

3.5× more requests per second than MLFlow Server with over 10× faster response times

3.5× more requests per second than MLFlow Server with over 10× faster response times