Forge Json

Reliable AI data flow

Your data breaks before AI. And after it.

Transform, validate, and sanitize JSON before and after your AI calls.

No fragile prompts. No regex parsing. No surprises.

Messy JSON

Pipeline

Safe JSON

LLM

Validated Output

Problem

Why LLM JSON breaks in production

AI workflows often fail at the boundaries: data arrives messy, the model returns a slightly different shape, and application code has to guess what is safe to trust.

  • JSON outputs are inconsistent
  • Fields go missing or change type
  • You end up writing fragile parsing code
  • Sensitive data can leak to AI APIs

Solution

Fix it with a deterministic pipeline

Forge JSON treats AI-bound data like a system boundary. The pipeline checks the source shape, transforms it into the expected contract, redacts sensitive fields, and produces only the JSON the AI path should see.

Input JSON
Contract
Transform
Redact
Safe JSON

Live tool

Normalize LLM JSON before it reaches production

Below is a real pipeline that normalizes messy order JSON before it enters an AI workflow. The contract step proves the payload is raw, the transform step creates stable fields, and the redaction step removes data the model should not receive.

Every AI-generated pipeline is inspectable before execution, step by step.

LLM JSON pipeline live tool

Run the example pipeline

{
"order_id": "ord_1049",
"customer": {
"name": "Maya Chen",
"email": "maya@example.com"
},
"payments": [
{
"status": "failed",
"amount": 66
},
{
"status": "paid",
"amount": 66
}
],
"debug": {
"rawWebhook": "...",
"internalNote": "VIP customer"
},
"tracking": {
"carrier": "DHL",
"status": "delayed"
},
"items": [
{
"sku": "tee_black_m",
"qty": 2,
"price": "24.00"
},
{
"sku": "cap_white",
"qty": 1,
"price": "18.00"
}
]
}
Output✓
1{
2  "order_id": "ord_1049",
3  "customer_name": "Maya Chen",
4  "customer_email": "****",
5  "payment_status": "paid",
6  "shipping_status": "delayed",
7  "item_count": 3,
8  "total_amount": 66
9}

Love the result?

Use this exact pipeline in your app, backend, or LLM workflow.

No setup needed. Works with curl, Node, Python.

Uses example data. For edited input, copy from the playground.

Read integration guide

Pipeline steps

deterministic
  1. 1Contract ValidationChecks that required fields exist before the pipeline spends work transforming data.
  2. 2Normalize OrdersDerive totals, counts, and statuses with repeatable rules.
  3. 3Pick AI FieldsKeep only the fields the AI path needs.
  4. 4Redact Sensitive FieldsMask customer data before it can leave the pipeline.

Failure mode this prevents

Prevents running transformations on bad or already-processed data before it corrupts outputs or breaks downstream systems.

Examples: re-processing already structured JSON, missing required fields, or partial webhook payloads.

How this was generated

Validate raw order data, skip if already processed, compute key fields, and output a clean, AI-ready JSON with sensitive data redacted.

AI Draft Review turns the prompt into inspectable pipeline steps before you run them.

View prompt

This pipeline was generated from a natural-language instruction — then converted into a deterministic, validated workflow.

Full generation instructions

I'm preparing this messy order JSON for an LLM and need a stable, flat, AI-ready shape.

Validate that the input still looks "raw" — it must contain customer.name, customer.email, a non-empty items array, a non-empty payments array, and tracking.status (all strings except the arrays). If the input already has flattened fields like customer_name, customer_email, item_count, total_amount, payment_status, or shipping_status, treat that as a sign it's been pre-processed and skip the rest of the pipeline.

Then compute these derived top-level fields from the validated input:

  • customer_name from customer.name
  • customer_email from customer.email
  • payment_status = the status of the first payment whose status === "paid"
  • shipping_status from tracking.status
  • item_count = sum of items[].qty
  • total_amount = sum of payments[].amount where status === "paid"

After the derived fields exist, keep only order_id, customer_name, customer_email, payment_status, shipping_status, item_count, total_amount and drop everything else (the nested customer, items, payments, debug, tracking).

Finally, redact the email value at customer_email so it doesn't leak into the LLM prompt.

Pre-LLM shaping

Normalize and structure your data before sending it to AI.

PII Redaction

Remove sensitive data before it reaches the model.

Post-LLM validation

Enforce schema, fix types, and drop invalid fields automatically.

API

Simple enough to drop into your AI path

The same workflow can be opened in the editor or wired into an application endpoint. That makes the demo more than a visual example: it is a reusable data contract.

await runPipeline({
  input: data,
  pipeline,
  llm: {
    model: "gpt-4.1",
    prompt: "Summarize orders"
  }
})

Works with a portable JSON pipeline format. View the specification

Built for developers working with real production data.

Deterministic. Inspectable. Safe.

Try a pipeline on your messy input. Or see how to make AI JSON safe end-to-end.