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
- Event sources send JSON messages to the Receiver endpoint
- The Receiver publishes messages to the stream processing pipeline
- Each message is matched to workflows by organization and environment
- The Workflow Engine traverses the workflow tree, executing entities in V8 isolates
- Results are written to S3 (success, fail, or ignore)
- 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:
| Component | Purpose |
|---|---|
| Model Providers | Supported AI platforms (OpenAI, Anthropic, Bedrock, Google, xAI) |
| Model Definitions | Specific models within a provider (e.g., GPT-4o, Claude 3.5 Sonnet) |
| Model Credentials | API keys and authentication (system-wide or per-organization) |
| Model Configurations | Ready-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
- Set up the Admin Console to create your organization and configure environments
- Configure model credentials for your preferred AI provider (OpenAI, Anthropic, etc.)
- Create model configurations combining a model definition with your credentials
- Build workflows using the visual canvas editor
- Add entities (events, prompts, actions) with conditions and scripts
- Test with the Prompt Builder to validate AI prompts before deployment
- Set up schedules for automated recurring workflow execution
- Monitor results in the Processed Messages screen
Next Steps
- Dashboard — Overview of your organization
- Workflow Canvas — Build workflows visually
- Prompt Builder — Test and analyze AI prompts
- Scripts Reference — Available script functions
- API Reference — REST API documentation