Multilingual data platform

You publish in more languages than your team can read.

Every translation produces decisions. Someone chooses a term. Someone rejects a phrase for a certain market. Someone corrects what the AI got wrong.

Those decisions are your language data. This is the platform for it.

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Where this starts

Between the machine and the agency, there was nothing.

Somewhere in your company, someone approves content they cannot read. Just because nobody on the team speaks Polish.

A wrong translation reads exactly like a correct one. Clean sentences. Correct grammar. Professional tone. And the word for load-bearing quietly replaced by the word for loading. Fluency is not evidence. Fluency hides the mistake.

The hard part was never the translation. The hard part is knowing whether it is right. You need certainty on the content that carries risk, and lower costs on the content that does not. Above all you need a way to tell which is which.

I was able to utilize the files TextUnited provided and our project could proceed. Comparing to ChatGPT translations there were significant improvements in translations by TextUnited.

Mark Decker · VP of Product, AxisCare

⛔

Machine only

Fast, cheap, and invisible. Nothing checks the translation against the original. You find out it was wrong when a customer or a regulator does.

⛔

Send it to an agency

You get a file back and someone's word that it is correct. You pay the full rate on every line. And what the agency learned about your content stays with the agency.

Meanwhile this is what crosses your desk in a single quarter.

An internal announcement	Nine languages, out today. Nobody is harmed if a comma is wrong.
A product page carrying a load rating	One number, and it is a liability the moment it drifts.
A fall-protection module	Arabic and Hindi, for people who will act on it. Arabic runs right to left. Devanagari needs more line height, or the vowel marks are cut off. Neither one gets retyped by hand.
A supplier catalogue in InDesign	German runs 30% longer than the English and the fixed text frames break.
Ten years of technical documentation	Nobody has read it since it was written. Now an AI assistant uses it.

Five tasks, and each one needs a different level of human attention. The market sells two options: everything, or nothing.

Start with one file →

What has to change first

You cannot decide how much attention something needs until something measures it.

AI translates all of it. Then every sentence is scored against the original. Sentences below the threshold you set go to a specialist who reads that language and knows your product. Everything above the threshold can be published safely.

The translations are fast, precise, and always preserve the original formatting, even in complex documents with tables or technical terminology.

Wojciech Lemański · Manager, KBR Poland

Quality estimation

Which segments need a person

Every segment scored against its source, before anyone reads it. An announcement and a safety module do not get the same attention.

The person at the other end of the threshold reads the language and knows the work. Your person, or ours. Bring the translators and agencies you already trust, or order from our linguists in the same place.

Text gets longer. Some scripts run right to left. Some need more line height. Some words sit inside graphics. All of it is handled here.

InDesign/IDMLPowerPointFigmaXMLHTML emailPDFSRT/VTTWordMarkdown

Before you believe any of this

Send us a page you already published.

One live URL. Any language. We check it against the original and send back what we find: missing text, wrong terms, off-brand wording. Most people send a product page in a language nobody on the team reads.

Request the report

What measurement leaves behind

Five things accumulate alongside your translated files.

The files are what you ordered, and they leave the moment they are delivered. These five are produced along the way. Here is what each one holds, and what it is good for.

Terminology	Approved and rejected terms, per market, with the reason attached	agents, authors, CMS, MCP
Translation memory	Sentence pairs humans have already signed off	reuse, cost control
Style guides	Machine-readable, per brand and per product line	generation, review
Review decisions	What the AI got wrong and what a specialist changed	audit, tuning
Quality signals	Scores, findings and severity over time, by content type	planning, spend

Ten years of translation. What do you own now?

What that makes possible

Once the data is organized, your systems can read it.

Language data stops being something only the people who order translations touch. It becomes a source your systems can query, which is what makes the word platform mean anything.

Query it and change it from anywhere

Terminology and memory reach your AI tools through the MCP server, readable and writable, so a term gets settled where the work is happening, not in a separate system.

Create in a market, not just translate into it

Your tools draft on wording your company has already approved, instead of starting from a generic model and correcting it afterwards.

Ground your internal assistants

They answer from your approved data and knowledge, rather than from a plausible guess.

Open the archive nobody has read

The manual from 2019 becomes searchable in every language it exists in.

Why you are not starting from zero

You already have ten years of files and projects.

They just aren't usable yet. Nothing here begins empty and nothing fills one job at a time, hand over what you already own and most of it lands in the first weeks, not the third year.

Day one

What you already own

Existing documentation, parallel texts from past suppliers, glossaries, reference material, whatever came back from the agency over the years. Ingested in bulk and aligned into terminology and memory.

→

Every job after

What gets settled from here

A specialist resolves a term, a reviewer rejects a phrasing, a threshold gets tuned. Each of those is a decision, and it accrues in the same place rather than leaving with the file.

Bulk ingestion gives you volume. Your own past content also contains your own past mistakes, so nothing becomes canonical until a person who knows the work says it is.

How it reaches your content

However it arrives, it lands in the same place.

One language data layer underneath, four ways in, depending on how technical your team is. There is no proprietary workflow builder here, deliberately. Learning ours would be one more thing we ask of you, and it is the first thing that goes stale.

Use it · Self-service

Teams who just need a translation project done, no developer required. SSO, any department.

Compose it · Automation

Build your own pipeline visually, in the automation tool your team already uses.

Build on it · API

Direct, code-level integration. Every language version stays current automatically.

Query it · MCP server

Your language data retrieved as context at the moment of generation.

What it takes to run

One platform, every department, your own models.

Communications, L&D, technical documentation and product rarely want the same thing, and they almost never share a budget. That is a setup question, not a reason to buy four different tools.

Isolated per tenant

Your language data is sealed to your account. It is never used to train anything outside it, and it never becomes another company's advantage.

Built for the whole company

Several business units, each with its own administrator, cost centre and billing, on one underlying record. Departments stay independent; the terminology does not fork.

Runs against your own models

Connect the AI your company has already approved, including private deployments and models that never leave your network. Hosted on Microsoft Azure, or inside your own environment where policy requires it.

Plainly

Three things we are not going to pretend.

AI translation is not good enough to publish unread.

That is the entire reason the measurement layer exists. Anyone telling you otherwise is selling you the first setting.

Output is only as good as the data behind it.

An old archive carries old decisions, including the wrong ones. Ingesting it gives you volume on day one, but nothing in it is authoritative until someone who knows the work has confirmed it, and that part cannot be bought or hurried.

Some content will always need a full human pass.

It will cost what it costs. The saving comes from no longer paying that rate on material that never needed it.

Changing supplier? Data stays yours.

An agency keeps what it learns about your content. Ten years in, you have the files and they have the knowledge. Terminology and memory come out in the standard interchange formats, not a proprietary dump, but files a competing system can open. Leaving is a decision, not a negotiation.

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