About

Data transformation rules, learned from examples.

Latentmachine is a browser-based transformation engine for structured data. You show what your data looks like now, show what it needs to become, and it infers a deterministic symbolic rule. The rule is inspectable, correctable, and exportable as code you own.

The tool also tells you when it cannot infer a reliable rule. If examples are contradictory, ambiguous, or insufficient, Latentmachine explains the issue and asks for the next useful example instead of guessing.

The Problem

Developers and automation builders often need to translate structured data between systems: a webhook payload needs to become an accounting CSV, a spreadsheet row needs to become an API payload, or an API response needs cleanup before import.

Writing a script is reliable but slow for one-off jobs. Asking a language model is fast but hard to trust on every row. Visual mapping tools work for simple field renames but become tedious with nested structures, type conversions, and arrays.

Latentmachine is a fourth option: demonstrate the translation with examples and let the engine synthesize the smallest rule that explains them.

How It Works

The engine parses each input-output pair, converts supported formats into an internal structure, compares the data, generates candidate operations, validates them across all examples, and selects the simplest program that exactly fits the evidence.

Before returning a result, it runs diagnosis checks for contradictions, ambiguous mappings, missing fields, and unseen values. That diagnosis is part of the product: the output should be useful, but the rule should also be understandable.

What It Can Infer

• Field mapping, renaming, removal, nesting, and flattening.
• String cleanup, concatenation, splitting, case changes, and template-style merges.
• Type conversion between strings, numbers, booleans, and common date formats.
• Value maps that stop safely when an unseen value appears.
• Numeric operations and basic unit conversions.
• Array projection, extraction, counting, joining, and simple lookups.

What It Does Not Do

Latentmachine is for structured, repetitive data translation. It is not designed for free-text rewriting, language translation, external lookups, arbitrary business logic, database queries, or transformations that depend on information outside the input data.

Who It Is For

Automation builders

Build a workflow in n8n or Make.com, paste the payload you received, paste the shape you need, and export the inferred rule as JavaScript.

Developers integrating APIs

Use examples to infer the glue code between structured formats and shapes, including nested JSON, flat CSV rows, YAML configs, and composed operations.

People moving or cleaning data

Clean exported records, reshape CMS content, flatten API responses, or turn messy fields into consistent import-ready data.

Exports

Once a rule is marked safe, Latentmachine can copy the translated output, download JSON, CSV, YAML, or .env, copy the rule program, or export JavaScript for n8n, Make.com, and standalone scripts.

// Latentmachine inferred transformation
function transform(input) {
  return {
    name: [input.user.first, input.user.last].join(" "),
    accountId: input.account.id,
    logins: Number(input.login_count || 0)
  };
}

Privacy

Latentmachine runs in the browser. Your examples, inputs, outputs, and saved rules stay on your machine unless you choose to copy or export them. There is no account system and no server-side transformation step.

Status

Latentmachine is in active development. The current translator supports JSON, CSV, YAML, and .env through a deterministic engine that is stable across the included benchmark suite and is free to use.