An open-source platform harnessing AI to turn noisy, heterogeneous event streams into real-time, context-aware actions.
Why PredictStream?
Building on an AI native foundation
PredictStream transforms noisy, real-world event streams into actionable AI-driven insights and automated responses — bridging the gap between raw data and intelligent action.Semantic Event Processing
Turn messy streams into structured, meaningful narratives.
Real-Time Action
Close the loop from insight to automated intervention.
Modular AI Agents
Pluggable pipelines for flexible, domain-specific solutions.
Self-Improving System
Feedback loops enable continuous learning.
How PredictStream Works
Event Sources
Ingest events from any source - files, APIs, databases, message queues, etc.
Ingestion
Incoming events are normalized, validated, and deduped.
Enrichment
Agents (e.g., LLM backed) enrich events with context, infer domains, extract entities, or perform summarization.
Prediction
Prediction agents leverage LLM's or other models to forecast next events, classify anomalies, or generate recommendations.
Action
Actions are triggered based on predictions or recommendations.
Feedback
Agents learn from feedback to improve predictions and recommendations.
What are some use cases?
Action-Oriented Applications
Automated incident remediation.
Real-time cart abandonment fixes.
Automated patient reminders and scheduling.
Proactive maintenance triggers.
Freeze suspicious transactions in real time.
Adapt lesson difficulty.
Reroute shipments or trigger alternate suppliers.
Balance loads, predict failures, or activate backup power.
Route high-priority issues to agents, auto-suggest fixes, or proactively contact frustrated users.
Frequently Asked Questions
What is PredictStream?
PredictStream is an open-source platform that transforms raw, real-time event streams (e.g., IoT sensors, transaction logs, user interactions) into actionable AI-driven insights and automated responses. It leverages modular AI agents to clean, enrich, predict, and act on events as they happen.
How is PredictStream different from traditional data pipelines?
Unlike batch-processing systems, PredictStream:
- Operates in real time, closing the loop from insight → action.
- Uses AI agents to handle messy, unstructured data (e.g., imputing missing values, inferring context).
- Supports self-improvement through feedback loops and operational telemetry.
What industries can benefit from PredictStream?
Any domain with event-driven workflows! Examples:
- IT/DevOps: Auto-remediate system failures.
- E-Commerce: Real-time cart abandonment fixes.
- Healthcare: Automated patient reminders and scheduling.
- Industrial IoT: Proactive maintenance triggers.
- Financial Services: Freeze suspicious transactions in real time.
- And many many more!
Do I need labeled data or predefined schemas to use PredictStream?
No! PredictStream’s zero-knowledge learning agents can infer structure and semantics from raw streams, making it ideal for unlabeled or heterogeneous data. Custom agents can also integrate existing rules.
How does PredictStream ensure transparency and auditability?
Every agent emits structured feedback, and all actions are logged as event streams themselves. This enables:
- Real-Time monitoring: via dashboards.
- Auditability: Comprehensive logs for insights.
- Human-in-the-loop overrides where needed.
Is PredictStream scalable for high-velocity global data streams?
Yes, but with caveats. The modular architecture supports horizontal scaling, and lightweight agents minimize latency. However, extreme-scale deployments (e.g., planetary IoT) may require custom optimizations - a focus of ongoing research.
Ready to Operationalize Event Streams?
Join the open-source community or deploy PredictStream today.