Map source data to the schema your system expects.

EDI Platform · TransformsA Transform is a reusable rule that turns a field from a source document into the shape your target system expects. Three layers cover everything from a 1:1 field copy to deeply nested arrays, with AI-generated code based on your real documents.

Why hand-rolled mappings stop scaling at partner #20

1 partner

Per script, per dev

Each new supplier means a new transform script - written, reviewed, and maintained by an engineer who knows that partner's quirks.

Silent

Schema drift

A partner adds a field, renames an attribute, or flips a unit - and the mapping fails downstream without a clear error.

Fork on every quirk

No shared rules

Partner-specific weirdness leaks into the global rules, or every partner ends up with their own copy-pasted fork.

How a transform gets built

  1. Define the target schema once

    Step 1

    Tell Chainfill what your ERP expects - field names, types, required vs optional, nested item lines. One schema, reused across every supplier.

  2. AI proposes the mapping from real docs

    Step 2

    Chainfill reads the supplier's actual files and proposes a transform - direct copies, concatenations, splits, array iterations, coercions, null handling - all visible in a diff view.

  3. Review, tweak, ship

    Step 3

    Approve the proposal, override any field by hand, or refine in the AI assistant. Calibration learns from every correction so the next supplier comes together faster.

  4. Promote across scopes

    Step 4

    Keep the partner-specific quirks at the supplier scope; promote anything reusable up to the company scope so future suppliers inherit it automatically.

Possibilities

Three layers, one engine - visible in the editor

Direct mapping, concatenation & splitting, array-aware paths - every transform sits in the same editor with the AI's proposal, your overrides, and a one-click ship.

app.chainfill.ai/authenticated/transforms/order-intake

Transforms · Order intake

Mapped from ACME EDIFACT D.96A → ERP order schema

Approve & ship
MappingsSchemaTestHistory
Direct mapping·Supplier · ACME
Approved
OrderIdorder_idstring · req
Concatenation & splitting·Supplier · ACME
AI proposed
FirstName+LastNamefull_namestring · req
Array-aware path·Supplier · ACME
AI proposed
Items[*].WeightKgline_items[].weight_kgnumber · array
Direct mapping·Company
Approved
Total · 412,50 €total_eur · 412.50number · coerce
Direct mapping·Per-mapping
Approved
SupplierNamesupplier ?? "unknown"string · null-safe

What AI-generated transforms actually save

Before - hand-coded scripts

Days to weeks

Engineer reads partner spec, writes mapping code, handles edge cases, ships, then maintains it forever.

With Chainfill Transforms

Reviewed mapping

AI proposes the mapping from real documents. Ops reviews, approves, tests against the partner's real schema, and ships.

Quality compounds across suppliers

Every correction trains the AI for the next mapping - so onboarding partner #50 is faster than partner #5, not the other way around.

Ready to remove the manual work?

See where Chainfill can save time in your document, email, and system workflows.