Enterprise AI for Real-Time Predictions
For teams who need real-time predictions without batch delays or black-box models. The enterprise AI toolkit for real-time personalization, predictions, and intelligent automation at scale.
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Platform Capabilities
Client Pulse Responder
Always-on inference runtime. Scores events in milliseconds, supports continuous online learning, and runs 24/7 in production—no batch retraining required.
<50msContinuous Online Learning
Models update with every interaction in production. No scheduled retraining—predictions stay fresh as behavior changes.
Pre/Post-Predict Hooks
Plugin architecture for pre-predict data enrichment and post-predict business logic, API configuration, and action triggers.
Closed-Loop Feedback
Every prediction outcome is logged and fed back to the model automatically, closing the learning loop without manual intervention.
Run Experiments
Run dynamic experiments in parallel finding new winners exploiting known ones.
Time to Value
Launch live predictions in weeks not months
Connect & Configure
Connect data sources and load behavioral module templates in the Workbench.
Experiment & Iterate
Configure experiments, run first dynamic models, and validate against live data.
Go live
Production deployment via the Pulse Responder with continuous learning active.
Dynamic Experimentation
Multi-armed and contextual bandits run in production. More traffic goes to what's working, a slice keeps exploring, and you see results from real outcomes as they arrive—not from a fixed A/B that needs weeks before you can call it.
- Traffic shifts toward winners while a slice keeps exploring—less revenue left on the table.
- Rewards can be binary (e.g. convert/don’t) or scalar (value, margin)—aligned to how you measure success.
- Configure custom reward functions to enhance learning capabilities for your environment
Platform Components
Workbench
No-code web interface. Load solution modules, ingest data, configure models, deploy predictions—all without coding.
No-code web interface. Load solution modules, ingest data, configure models, deploy predictions—all without coding.
Core ML engine managing data engineering, model training, and inference. Worker architecture for plugging in the latest algorithms.
Always-on inference runtime. Scores events in milliseconds, supports continuous learning, runs 24/7 in production.
Integrated Jupyter environment with Python libraries. Custom model development, generative AI workflows, full API access.
Real-time monitoring of model performance, conversion metrics, A/B test results, and drift detection.
Design and deploy autonomous AI agents with predictive logic, prompt libraries, and fact-injection—no ML expertise required.
Self-serve analytics powered by Apache Superset. Explore data, build dashboards, and share insights across teams.
In-product data visualizations and analytics for testing strategies, modeling outcomes, and validating predictions before they go live.
Pre-built algorithms for spend personality, churn propensity, engagement scoring, and more—trained on real-world behavioral patterns.
Prediction Server
Core ML engine managing data engineering, model training, and inference. Worker architecture for plugging in the latest algorithms.
Client Pulse Responder
Always-on inference runtime. Scores events in milliseconds, supports continuous learning, runs 24/7 in production.
Notebooks
Integrated Jupyter environment with Python libraries. Custom model development, generative AI workflows, full API access.
Grafana Dashboards
Real-time monitoring of model performance, conversion metrics, A/B test results, and drift detection.
AI Agent Builder
Design and deploy autonomous AI agents with predictive logic, prompt libraries, and fact-injection—no ML expertise required.
Superset Dashboards
Self-serve analytics powered by Apache Superset. Explore data, build dashboards, and share insights across teams.
Simulations Lab
In-product data visualizations and analytics for testing strategies, modeling outcomes, and validating predictions before they go live.
Behavioral Algorithms
Pre-built algorithms for spend personality, churn propensity, engagement scoring, and more—trained on real-world behavioral patterns.
Platform Architecture
Workbench, server, workers, and real-time APIs in one architecture.
Integration partners
Platform capabilities
Integration partners
See all on Developer Portal →Continuous Learning Loop
Every prediction is an experiment that yields data to refine the next prediction.
Stream behavioral, transactional, and contextual data in real time
Score events via the Pulse Responder in <50ms
Serve personalized decisions to any channel via API
Log every outcome; monitor via Grafana dashboards
Feedback updates models instantly—no batch retraining
Strategies evolve as behavior changes
Stream behavioral, transactional, and contextual data in real time
Score events via the Pulse Responder in <50ms
Serve personalized decisions to any channel via API
Log every outcome; monitor via Grafana dashboards
Feedback updates models instantly—no batch retraining
Strategies evolve as behavior changes
Algorithms & Modules
In-house behavioral algorithms and pre-configured AI modules—mix and match to build any use case.
Spend PersonalityMoney PersonalityDigital PersonalityEcosystem RewardsEpsilon GreedyBayesian ProbabilisticQ-LearningNetwork Analysis (PageRank)Loss AversionRisk AversionProspect TheorySentimental EquilibriumCoverage-Aware ThompsonLong-Tail Boost MFGenerative ModelIndustry Applications
Use personality-based segmentation to match offers to customers' financial habits and preferences.
Deliver app-based interactions at the right time by understanding and acting on digital engagement patterns.
Enable conversational experiences that allow customers to complete banking tasks entirely through natural language interactions.
Integration & Extensibility
Connect to any system, extend with custom logic, deploy anywhere.
API Connections
Connect to any live or front-end environments with just a click of a button.
Plugin Architecture
Pre-predict, post-predict, and API configuration hooks for custom business logic.
Cloud-Native
Docker, Podman, and optional Kubernetes orchestration with AWS/Azure/GCP marketplace support.
Event-Driven
Real-time streaming for behavioral data pipelines and event-sourced architectures.