Global translation & interpreting

Add Lingo Ledger to your LLM in three ways

Connect your AI agent to certified translation sourcing through an MCP server, a command-line tool or a TypeScript API. Matching works on brief validation and registry exports, and a certified reviewer approves the result.

Choose an integration Open the atlas

NAATI · CATTI · ATA · ITI schemes
Practitioner matching from registry exports
Brief validation and translation scoring
Human reviewer approves every result

0Countries and territories

✓

0Sourced

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0Partial

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0Not yet researched

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0Verified for government use

Integrations

Three ways to integrate

Each one uses the same brief validation and matching logic.

MCP server

Lets an AI agent call Lingo Ledger tools over stdio, from any MCP-compatible client.

validate_project_brief
search_certified_translators
naati_directory_steps (no requests to NAATI)
score_translations

# install, then start the server
pip install -e ".[dev]"
lingo-mcp

Command line

Validate a brief, search certified practitioners, or score translations from a terminal.

lingo brief validate
lingo search (optional --registry export)
lingo naati-steps
lingo eval

# check a brief, then search a registry export
lingo brief validate brief.json
lingo search brief.json --registry practitioners.jsonl
# print steps to search the NAATI directory by hand
lingo naati-steps brief.json

TypeScript API

Import the functions directly into your application.

validateBrief(brief)
searchPractitioners(brief, practitioners)
naatiSearchSteps(brief)
scoreTranslations(examples, hypotheses)

import { validateBrief, searchPractitioners } from "@lingoledger/lingo-ledger";

const check = validateBrief(brief);
const matches = searchPractitioners(brief, practitioners);

Status. Practitioner search over registry exports, brief validation, scoring and the review workflow are built. Live search of the public NAATI, CATTI or ATA directories is not built yet.