The Problem
Enterprises struggle to operationalize AI on live data streams. The tools available today force painful tradeoffs:
Visual tools are too simple
Drag-and-drop builders that can't handle branching, windowing, or distributed execution.
Streaming engines require Java expertise
Flink and Kafka Streams are powerful but demand specialist engineering teams.
AI orchestrators can't handle real-time data
LangChain and similar tools work request-response, not on continuous event streams.
An agentic AI builder that compiles to production-grade streaming pipelines with AI nodes as first-class citizens. No tradeoffs.
Our Mission
We believe the gap between prototyping an AI model and running it on live production data should be minutes, not months. Every team - from data engineers to business analysts - should be able to build, deploy, and monitor streaming AI pipelines without writing distributed systems code.
Our Vision
We envision a world where every enterprise can react to events as they happen - where fraud is caught in real time, support tickets are triaged instantly, and supply chains adapt autonomously. Magister makes that world possible.
Under the Hood
Production-grade technology stack designed for real-time AI at enterprise scale.
Distributed stream processing engine. Sub-10ms latency, 100K+ events/sec per node, exactly-once semantics. Runs embedded in the platform.
Records for immutable schemas, virtual threads for concurrent operations, pattern matching for cleaner code. Spring Boot 3 for dependency injection and lifecycle management.
Kahn's algorithm for topological sorting. Pluggable NodeCompiler registry. Visual workflows compile to distributed in-memory DAGs with cycle detection and validation.
Compile-once, execute-per-record expression evaluation. Sandboxed execution with configurable timeouts. Safe user-defined predicates without raw Java compilation.
Zustand for state management with Immer middleware. ReactFlow for DAG visualization. Dual-mode interface: engineering workbench and physics-based canvas.
Built-in AI assistant for pipeline generation via natural language. Model Context Protocol server enables integration with any AI client. 14 purpose-built AI nodes.
Platform Scale
Financial, healthcare, retail, legal, and more
Battle-tested pipeline templates
First-class pipeline citizens
End-to-end processing
Book a 30-minute demo with our team or explore the live studio yourself.