Raw data comes in. Clean, enriched, classified data goes out.
Transform, enrich, classify, route, and deliver. Build the entire pipeline on one visual canvas. No scripts. No cron jobs. No glue code.
A webhook fires with a raw order payload.
You need to enrich it with customer data from your CRM.
Classify it by risk level using your LLM.
Transform the schema for your data warehouse.
Route high risk orders to fraud review.
Deliver the rest to fulfillment in real time.
Today, that takes five microservices and a prayer.
One fails silently. Nobody knows until a customer complains.
Pipes puts all of it on one visual canvas.
Every step visible. Every failure recovered. Every event accounted for.
Enrich. Transform. Route. Deliver. One canvas.
Raw data arrives from a webhook. Enriched with CRM data. Classified by your LLM. Transformed for your warehouse. Routed by business rules. All on one visual canvas.
Classify, extract, and route with your own LLM.
Drop an AI node at any step. It sends the event to your LLM with your prompt, and outputs structured data to the next node. Risk classification, entity extraction, intelligent routing.
Classify by risk
Every order is scored by your LLM. High value, high risk, suspicious. Risky orders are routed to fraud review automatically.
Extract what matters
Names, addresses, amounts, references. The AI extracts structured data from free text, PDFs, and webhook bodies.
Route intelligently
Approved orders go to fulfillment. Suspicious orders go to review. Transformed data goes to the warehouse. All automatic.
Multi step processes that recover automatically.
When a step fails, compensation actions run in reverse for all completed steps. No manual intervention.
Resume from exactly where it stopped.
Every node saves state. No reprocessing. No skipping.
Checkpoint
Workflow state is saved at every node.
Failure
The next node fails. The pipeline stops.
Resume
The system resumes from the exact checkpoint.
Complete
The pipeline finishes normally. Zero data lost.
Before and after Pipes
Without Pipes
- Five microservices for one pipeline
- Each one can fail independently
- No visual overview of data flow
- No single place to debug
- Manual error recovery
- Separate monitoring per service
With Pipes
- One canvas with every step visible
- Built in error handling per node
- Real time visual overview
- Unified debugging in the dashboard
- Saga compensation runs automatically
- Per node observability
npayload Pipes vs. building pipelines yourself
| Feature | npayload | Build it yourself |
|---|---|---|
| Visual builder with versioning | ||
| AI classification nodes | Months of work | |
| Saga with auto compensation | Complex to build | |
| Checkpointing and resume | Weeks of work | |
| Per node observability | Separate project | |
| Pre built connectors | Build each one | |
| DLQ with replay | Weeks of work | |
| Automatic backpressure | Complex to build |