Riptide Workflow Engine
Overview
The Riptide Workflow Orchestration Engine is an enterprise-grade platform designed for automated document processing, AI-powered data extraction, and complex business process automation. Built on clean architecture principles with asynchronous execution and distributed job processing, it supports thousands of concurrent workflow executions with horizontal scaling, comprehensive observability, and flexible integration with AI models and business systems.
Purpose
Modern document processing and business automation requires orchestrating complex interactions between file systems, databases, AI models, human reviewers, and notification systems. The Riptide Workflow Engine solves this by:
- Providing visual workflow design with JSON-based definitions that integrate seamlessly with the workflow-designer tool
- Supporting asynchronous execution at scale with reliable background job processing
- Abstracting AI model complexity through named interfaces that business users can reference without technical knowledge
- Enabling human-in-the-loop workflows with email-based task orchestration, work queues, and secure callbacks
- Integrating with diverse data sources through SQL query nodes, file download nodes, and PDF processing capabilities
- Maintaining complete execution history with automatic archival for performance and compliance
Key Capabilities
Visual workflow design — JSON-based definitions created in the workflow-designer tool and deployed via REST API. Nodes, edges, and execution logic are declared in a format that supports versioning, import/export, and pre-deployment validation.
AI model abstraction — Administrators configure named model interfaces (e.g. "Local Vision Model", "Claude Document Processing") that hide provider endpoints, API keys, and parameters. Workflow designers reference models by name.
Asynchronous execution — Distributed job processing with persistent storage, automatic retry, and priority queues. The API returns a tracking ID immediately while nodes execute in the background across horizontally scaled workers.
Human-in-the-loop tasks — Workflows pause for manual intervention via email notifications with secure callback links. Configurable work queues (Expedite, Same-Day, General, Finance, HR, etc.) carry assigned priorities, SLA deadlines, and team ownership.
Rich node library — SQL queries, Azure Blob and HTTP file downloads, PDF page extraction and merge, dynamic expression evaluation, JSON transforms, and multi-channel notifications (email, SMS, webhooks, Teams, Slack).
End-to-end observability — Every node execution is tracked with inputs, outputs, duration, and errors. Correlation IDs tie operations together across distributed systems, complemented by health checks, Prometheus metrics, and execution history APIs.
Why Riptide Workflow Engine
Ship process changes in hours, not weeks. Design workflows visually, test them, and deploy via API—no code changes required to modify process logic, update AI prompts, or adjust routing decisions.
Scale without rearchitecting. Horizontal worker scaling, distributed job processing, and automatic result archival sustain thousands of executions per day while keeping the database lean.
Swap AI providers freely. Named model interfaces decouple workflow logic from provider details. Move between Azure AI, OpenAI, Anthropic Claude, or self-hosted models (Ollama, vLLM) without touching workflow definitions.
Balance automation and accuracy. Confidence-based routing sends clear-cut results straight through and flags uncertain extractions for human review—keeping people in the loop only when their judgment matters.
Extend with clean abstractions. Pluggable node implementations, DynamicExpresso expression evaluation, and full Riptide SDK integration (identity, logging, configuration) let developers add custom capability without fighting the framework.
Workflow Orchestration System
The Riptide Workflow Engine supports sophisticated automation patterns.
SQL Query Nodes execute parameterized queries against business databases, supporting INSERT, UPDATE, DELETE, and SELECT operations with dynamic parameter binding from workflow context variables.
File Download Nodes retrieve documents from Azure Blob Storage, HTTP/HTTPS endpoints, and local file system paths with flexible authentication options. PDF Processing Nodes extract specific pages, split documents into individual pages, merge multiple PDFs, and extract metadata for downstream processing.
AI Extraction Nodes send documents or images to configured model interfaces with custom prompts and field definitions. The engine manages provider-specific request formats, authentication, rate limiting, and error handling—returning normalized results with confidence scores and extracted field values.
Decision Nodes evaluate dynamic expressions at runtime, routing workflows based on confidence scores, field values, business rules, or any condition expressible through the expression engine. Transform Nodes manipulate JSON data structures, mapping fields, aggregating values, and preparing data for subsequent nodes.
Human Task Nodes pause execution and generate email notifications with customizable templates, placeholder substitution for task details and due dates, and secure callback URLs. The engine validates callbacks, checks expiration against SLA deadlines, and resumes workflow execution when users complete tasks.
Notification Nodes send alerts via email, SMS, webhooks to external systems, and chat platforms including Microsoft Teams and Slack. Error handling supports automatic retry with configurable attempts, custom error routes in workflow definitions, and manual intervention when automatic recovery is exhausted.
Integration Points
Applications interact with the Workflow Engine through a REST API that creates workflow definitions, starts executions, queries instance status, retrieves execution history, manages human tasks, and cancels running workflows. The API supports import/export of workflow definitions from the visual workflow-designer tool for seamless deployment.
Configuration management uses a hybrid approach: workflows, work queues, and AI model interfaces can be seeded from configuration files on startup and then managed through admin APIs. This enables version-controlled configuration in development while supporting dynamic updates in production without redeployment.
Database architecture separates business data from workflow engine data. Business databases contain application-specific tables accessed via SQL Query Nodes, while the workflow engine database stores execution state, job queue data, human tasks, and configuration. This separation enables independent scaling and backup strategies.
Riptide SDK integration provides identity management for authentication and authorization, structured logging with correlation IDs, configuration management with environment-specific overrides, and health check patterns for monitoring. The engine leverages SDK abstractions for consistent behavior across the Riptide platform.
Container deployment supports containerization with flexible migration strategies, health check endpoints for orchestration integration, metrics export, and horizontal scaling through multiple worker instances sharing a common database.
Scheduled execution supports recurring workflows with flexible scheduling, one-time scheduled executions with start date/time, and timezone-aware scheduling across multiple regions.
Event-driven triggers can initiate workflows from external systems via webhook endpoints with configurable authentication.
Common Use Cases
Organizations use the Riptide Workflow Engine to automate document-intensive and AI-powered business processes.
PDF document processing workflows download documents from storage, extract specific pages, send images to AI models for stamp detection and field extraction, route low-confidence results to human reviewers in work queues, and save validated data to business databases—all without manual intervention until human judgment is required.
AI-powered data extraction handles forms, invoices, contracts, and government documents by routing images through appropriate AI models, validating confidence scores through decision nodes, and escalating uncertain extractions to work queues with appropriate SLAs and team assignments.
Multi-stage review workflows coordinate document intake, automated field extraction via AI, confidence-based routing to expedite or standard review queues, parallel review when multiple approvals are required, and final disposition with notification to stakeholders—maintaining complete audit trails for compliance and quality assurance.
Scheduled batch processing runs nightly workflows to process accumulated documents, calculate fees based on extracted data, update business databases with processing results, archive completed workflows automatically, and send summary notifications to operations teams—enabling lights-out processing at enterprise scale.
Human-in-the-loop automation blends AI extraction with human verification by sending documents to AI models first, evaluating confidence scores to determine when human review is needed, routing tasks to appropriate work queues based on document type or priority, and resuming automated processing after human validation—achieving optimal balance between automation and accuracy.
Cross-system orchestration coordinates activities across document storage, AI providers, business databases, email systems, and chat platforms—breaking down integration complexity through declarative workflow definitions and unified execution monitoring.