Json infrastructure

Every company integrates APIs. Every integration creates another JSON translation layer.

Most teams solve it with custom code. Eventually that code becomes infrastructure.

Forge Json turns that layer into a reusable product — clean, reshape, validate, and standardize the JSON flowing between partner APIs, AI systems, and applications.

See it run View API documentation

The pain

Partner API chaos

Bank API A

Stripe

Salesforce

Internal API

LLM Output

↓

Different schemas

Different enums

Different nesting

Different validation rules

Every integration team builds the same adapter layer — differently, repeatedly, scattered across services. The knowledge ends up trapped in whichever engineer last touched it.

The category

Why existing solutions break down

Approach	What goes wrong
Custom code	Expensive to maintain; knowledge trapped in individual engineers.
One-off scripts	No reuse; no contract; breaks silently when partner APIs drift.
ETL tools	Heavyweight for API-shape transformation; not built for the per-request path.
LLM-only	Output isn't deterministic; can't be a contract.
Forge Json	Contract + transformation + API. Same pipeline runs in UI, via API, via MCP.

A concrete example

Adding a new banking partner

Partner A

account_number
balance_cents

Partner B

acctNo
balance_dollars

Partner C

account.id
attributes.balance

↓

{
  "accountId": "...",
  "balanceCents": 12345
}

Every new bank partner ships its own shape. Without a transformation layer, every team writes another adapter, then another mapper, then debugs another field-name regression. With one pipeline, the same canonical contract holds — and every downstream system (warehouse, app, AI agent) stays unchanged when partner four arrives.

“The first integration is a project. The tenth integration is a platform problem.”

Live proof

See it run

One pipeline definition. Two real wedges. Same primitives. Edit the input and watch the canonical contract update.

Output✓

{
  "Customer_Status": "ACTIVE",
  "Account Balance": "1234.50",
  "Last_Login": "2024-03-12T09:14:00Z",
  "tier": "Gold",
  "extras": {
    "preview_text": "blah blah"
  },
  "internal_note": null
}

Output✓

1{
2  "customerStatus": "active",
3  "accountBalance": 1234.5,
4  "lastLogin": "2024-03-12T09:14:00Z",
5  "tier": "gold"
6}

Keys normalized, enum lowercased, stringified number parsed, null and unused fields dropped. The same pipeline runs on every LLM response — the contract holds even when the model output drifts.

Architecture

One shared transformation layer

Sources

Partner APIs
LLM output
Webhooks
Internal services

→

Forge Json pipeline

Clean
Reshape
Validate
Standardize

→

Canonical contract

One schema
One vocabulary
One source of truth

One shared transformation layer, not N hand-rolled ones. Application, analytics, and AI agents all read from the same canonical shape — the pipeline absorbs the partner-side drift.

Adoption

What happens after the first integration

Partner two onboards in days, not weeks

The canonical contract is already there; only the partner-side mapping changes.

Contracts are reusable across teams

Whichever team owns the pipeline owns the truth — app, analytics, AI agents all read from the same shape.

Drift gets caught at ingest

Validation runs before the data reaches the warehouse or the prompt.

Same pipeline everywhere

UI, API, MCP — one definition, three execution surfaces.

Standardize the layer

One pipeline. Every partner.

Run the live demo above, then read the API docs to wire it into your stack.

See it run View API documentation →