An order-to-ERP pipeline that escalates only real exceptions
Connecting the systems an order touches into a self-healing n8n pipeline that validates every record into the ERP and escalates only genuine exceptions to a person.
- 0
- malformed records to ERP
- Hours
- reclaimed weekly
- Exceptions
- only, reach a human
The problem
Orders arrived across several disconnected systems and a person re-keyed them into the ERP by hand, reconciling mismatches after the fact. It was slow, error-prone, and impossible to scale: a single mistyped field could desynchronize inventory and accounting for days before anyone noticed.
What we did
We orchestrated the whole path with n8n. Each order is pulled from its source, normalized, read by an AI agent for the genuinely ambiguous fields, and validated against a strict schema before it can post to the ERP. Operations are idempotent so a retry never creates a duplicate order, transient failures retry automatically, and only records that cannot be resolved are routed to a person, with observability across the pipeline end to end.
Techniques
A workflow tool that connects your systems and runs multi-step jobs on its own. Self-hosted, so your records never reach a third-party vendor. Large Language Model. The kind of AI that reads and writes text. Useful for extraction, but it proposes rather than decides. ERP integration Designing an operation so running it twice does the same thing as running it once. It is what makes a retry safe in payments. Logs, metrics, and alerts that show what a live system is actually doing. It is how you find a problem before your users report it.
The cost of a manual hand-off
Re-keying orders across systems is slow, and the errors are expensive: one mistyped field can desynchronize inventory and accounting for days, and the reconciliation to fix it costs more time than the original entry. The process also could not scale, because throughput was capped by how fast a person could type.
An autonomous path with a strict gate
n8n orchestrates the route from source system to ERP, and an AI agent handles the ambiguous parsing that rules struggle with. The decisive design choice is the gate: a strict schema is the contract that decides what may post. The model proposes; the schema decides, so a confident-looking but wrong record cannot land in the ERP.
Self-healing, with people on the exceptions
Every step is idempotent and transient failures retry automatically, so the pipeline absorbs the normal turbulence of integrations without human attention. Only records that genuinely cannot be resolved escalate to a person, which means staff spend their time on real exceptions instead of routine typing.
Questions on this project
How is this different from a simple integration between two apps?
A point-to-point integration moves data; an autonomous pipeline validates it. Every record is checked against a strict schema, retried on transient failure, and escalated to a person only when genuinely ambiguous, so bad data never posts to the ERP.
What stops the automation from posting a wrong order to the ERP?
A strict schema sits between the pipeline and the ERP as the contract. The AI agent only proposes values; if a record does not validate exactly, it is held and routed to human review instead of guessed.
Does autonomous mean no people are involved?
No. People stay on the exceptions. The system handles the high-volume, unambiguous work end to end and escalates only the records that need human judgment, which is where senior time is actually worth spending.