Compile visual AI workflows into production-grade streaming pipelines. One runtime for AI agents, data processing, and enterprise integrations.
The Problem
Every enterprise is building AI systems. Few are running them reliably. The gap between a working prototype and a production deployment is filled with fragile prompt chains, custom glue code, and months of engineering.
Platform Architecture
From visual design to production execution in one compiled pipeline.
How It Works
Drag nodes onto the canvas or describe your pipeline in natural language. The AI Assistant generates a complete WorkflowDefinition from a single sentence.
The compiler validates your workflow (cycle detection, schema checks), performs topological sort via Kahn's algorithm, and dispatches each node to its registered NodeCompiler.
The compiled DAG runs on Magister Engine's in-memory streaming engine. MVEL expressions are pre-compiled for zero per-record overhead. Camel connectors handle external I/O.
Live SSE-powered dashboards show per-node throughput, latency heat maps, and error tracking. The Jet DAG viewer renders the actual execution graph with real-time metrics.
Industry Solutions
Each solution includes pre-built pipelines, sample data, and live dashboards. Deploy in minutes, customise to your requirements.
Fraud detection, KYC, trade surveillance, invoice processing
Clinical triage, drug monitoring, claims processing, patient routing
Ticket routing, SLA enforcement, sentiment analysis, escalation
Sentiment, inventory, recommendations, product categorisation
Log anomaly, API health, security events, DevOps automation
Contract analysis, GDPR screening, regulatory monitoring
Content moderation, editorial triage, copyright detection
Demand forecasting, supplier risk, logistics optimisation
Claims triage, underwriting, fraud detection, policy analysis
Vessel tracking, customs processing, yard management
Student engagement, curriculum analysis, admissions processing
Differentiation
Most AI orchestration tools execute workflows step-by-step at runtime, interpreting each node as they go. Magister compiles your visual workflow into a distributed in-memory DAG before execution. The result is a distributed, in-memory streaming pipeline with compile-time validation, cycle detection, and optimisation — not a fragile chain of HTTP calls.
Magister unifies four categories that typically require separate tools:
The built-in AI Assistant can generate entire pipelines from natural language. But every pipeline it creates is a standard WorkflowDefinition — fully visible, editable, and debuggable in the visual builder. No black boxes. The AI accelerates design; the compiled runtime ensures reliability.
Deploy via managed SaaS, self-hosted Docker Compose, or fully air-gapped with Ed25519 offline licensing. Your data never leaves your infrastructure unless you tell it to. Bring your own LLM provider — OpenAI, Anthropic, or local models via Ollama.
Book a 30-minute demo with our team, or explore the live pipeline demos yourself.