The Engine Behind Pulse

    The Distributed Event Mesh Behind Pulse

    Every Pulse application runs on StreamFlow — from a single laptop to a multi-region cluster.

    StreamFlow is the distributed event engine that powers Pulse applications in production.

    The Problem

    Building real-time AI systems today is too complex

    To build intelligent, real-time systems today, you need to combine multiple tools:

    • Streaming platforms for data
    • Workflow orchestrators for coordination
    • AI runtimes and agent frameworks for decision-making
    • Custom infrastructure for scaling and resilience

    It works. But it's fragmented, hard to maintain, and difficult to scale.

    The Solution

    Why StreamFlow Exists

    The distributed data plane for Pulse.

    StreamFlow is the distributed event engine underneath every Pulse application:

    01

    Events & Topics

    02

    State & Replay

    03

    Replication & Failover

    04

    Kafka Compatibility

    Pulse builds the application. StreamFlow makes it run at scale.

    What StreamFlow Provides

    High-throughput event mesh

    Shard-per-core execution

    Elastic partition rebalancing

    Durable state with replay from any offset

    Raft-based replication & instant failover

    Multi-region geo-replication

    Observability: metrics, tracing, health

    Native Apache Kafka protocol compatibility

    Source & sink connectors

    Powered by StreamFlow

    One engine. Many applications.

    Every application in the StreamFlow ecosystem runs on the same distributed event engine.

    Pulse
    Available
    RevenueOS
    Coming soon
    Resonance
    Coming soon
    Your application
    Build with the SDK
    ↓ ↓ ↓ ↓
    StreamFlow
    Events
    State
    Replay
    Replication
    Kafka
    Benchmarks

    Real runs. Real disk. Reproducible.

    Measured on the same hardware, with the official Kafka tools, against Apache Kafka and Redpanda. No mocks.

    491K
    Produce rec/s · parity with Kafka
    824K
    Consume rec/s · ~0.95× Kafka
    9.0 ms
    Produce p99 latency
    4.13 M/s
    Native produce · 1.06 GB/s
    9.3 M/s
    Disk engine · multi-shard

    How It Works

    StreamFlow Topics
    order.created
    sensor.alert
    fraud.check
    events arrive
    Pulse Agent 1
    LLMMCP ToolAPI
    Pulse Agent 2
    LLMMCP ToolAPI
    Pulse Agent 3
    LLMMCP ToolAPI
    agents reason & decide
    route.optimized
    alert.resolved
    tx.blocked
    decisions published
    StreamFlow Topics
    continuous loop
    Architecture

    Under the Hood

    StreamFlow combines:

    01A high-performance event mesh
    02Elastic partitioning for dynamic scaling
    03Direct I/O + gRPC communication
    04Raft-based replication
    05A stateful execution engine
    06Durable state with replay from any offset

    Use Cases

    Supply Chain

    Real-time coordination of global logistics and operations

    Telecom & Infrastructure

    Continuous monitoring and self-healing systems

    Fraud Detection

    Real-time decision-making at scale

    AI Ops

    Autonomous infrastructure management

    Cyber Defense

    Threat detection and automated incident response in real time

    Smart City

    Intelligent coordination of urban systems and IoT networks

    When do you move to Enterprise?

    Same runtime, same code. What changes is what you have to guarantee to other people.

    Stay on Pulse if…
    • — One machine, or a small cluster you own.
    • — A handful of agents, restarted by hand.
    • — Local logs are enough to debug a run.
    • — No formal uptime commitment to anyone.
    Explore Pulse
    Move to Enterprise when…
    • — Agents must survive node loss and upgrades.
    • — Several teams share the same event backbone.
    • — You need SSO, RBAC and a tamper-evident audit trail.
    • — An SLA, a runbook and a named contact are required.
    Talk to us

    Vision

    We're moving from

    Systems that process data

    to

    Systems that continuously think and act on events

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