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Introduction

Welcome to RocketWave Pulse — a real-time event stream processing platform that enables powerful workflow automation with AI/LLM integration, multi-provider model support, and transaction billing.

Overview

RocketWave Pulse is an Admin Console built with Next.js for managing your stream processing infrastructure:

  • Visual workflow builder with drag-and-drop canvas
  • Universal model system supporting OpenAI, Anthropic, AWS Bedrock, Google, and xAI
  • Prompt Builder for testing and analyzing AI prompts with sentiment analysis
  • Environment variable management with write-only security
  • Organization and team management with role-based access control
  • Transaction usage monitoring and billing enforcement
  • AI Assistant with RAG-powered entity creation
  • Image recognition support for vision-capable models

Architecture

┌──────────────────┐    ┌──────────────────┐    ┌──────────────────┐
│ Event Sources │───▶│ Receiver / │───▶│ Stream │
│ (APIs, webhooks)│ │ API Gateway │ │ Processing │
└──────────────────┘ └──────────────────┘ └────────┬─────────┘

┌──────────────────┐ ▼
│ Admin Console │ ┌──────────────────┐
│ (Next.js) │◀── Postgres/Redis ────────▶│ Workflow Engine │
│ • Workflows │ │ (V8 Isolate) │
│ • Models │ │ • Evaluator │
│ • Billing │ │ • Scripts │
└──────────────────┘ └────────┬─────────┘

┌────────▼─────────┐
│ Outputs (S3) │
│ • success/ │
│ • fail/ │
│ • ignore/ │
└──────────────────┘

Data Flow

  1. Event sources send JSON messages to the Receiver endpoint
  2. The Receiver publishes messages to the stream processing pipeline
  3. Each message is matched to workflows by organization and environment
  4. The Workflow Engine traverses the workflow tree, executing entities in V8 isolates
  5. Results are written to S3 (success, fail, or ignore)
  6. Redis tracks per-organization transaction counts for billing enforcement

Key Concepts

Workflows

Workflows define how events are processed. Each workflow is a directed tree of entities:

  • Events — Entry points that evaluate conditions against incoming messages
  • Prompts — AI/LLM execution steps using the universal model system
  • Actions — Custom JavaScript code for side effects (API calls, publishing, storage)

Universal Model System

The platform supports multiple AI providers through a unified configuration system:

ComponentPurpose
Model ProvidersSupported AI platforms (OpenAI, Anthropic, Bedrock, Google, xAI)
Model DefinitionsSpecific models within a provider (e.g., GPT-4o, Claude 3.5 Sonnet)
Model CredentialsAPI keys and authentication (system-wide or per-organization)
Model ConfigurationsReady-to-use configs combining a definition + credential + defaults

Scripts

Scripts are JavaScript functions injected into the V8 execution context. They provide capabilities like:

  • Calling AI/LLM services (multi-provider)
  • Image recognition with vision models
  • Posting to social media (Mastodon)
  • Sending emails (SendGrid)
  • Vector database operations (Pinecone)
  • Short-term memory (S3)
  • Jinja2/Nunjucks templating
  • Redis pub/sub messaging
  • Sports data (SportRadar NFL)

See the Scripts Reference for complete documentation.

Transaction Billing

Each organization has a transaction limit and overage policy stored directly on its subscription record. Defaults are set from PlanConfiguration when a plan is assigned and can be overridden per-organization by system administrators.

The Consumer enforces limits in real-time using Redis counters. When limits or plans change, the Admin publishes a notification via Redis pub/sub so the Consumer re-evaluates immediately rather than waiting for the next cache refresh. Messages exceeding a hard_limit are routed to the ignore queue; a warn policy allows processing with a logged warning. The Notifications service sends email alerts at 70% and 100% thresholds.

Environment Variables

Sensitive data like API keys are stored as environment variables at the organization level and securely injected into workflow execution contexts. Values are write-only and never exposed through the API or UI.

Getting Started

  1. Set up the Admin Console to create your organization and configure environments
  2. Configure model credentials for your preferred AI provider (OpenAI, Anthropic, etc.)
  3. Create model configurations combining a model definition with your credentials
  4. Build workflows using the visual canvas editor
  5. Add entities (events, prompts, actions) with conditions and scripts
  6. Test with the Prompt Builder to validate AI prompts before deployment
  7. Set up schedules for automated recurring workflow execution
  8. Monitor results in the Processed Messages screen

Next Steps