# VitraTM — Multimodal AI Translation Memory | Vitra.ai

> One translation memory across video, audio, images, documents, web and app. Exact, fuzzy and semantic matching in any direction, with write-back learning.

**Canonical URL**: https://www.vitra.ai/features/translation-memory
**Source**: This is the Markdown rendering of https://www.vitra.ai/features/translation-memory, generated at build time from that page.

---

Memory & Context

# VitraTM — Multimodal, AI-First Translation Memory

A legacy translation memory stores sentence pairs from one file format in one language direction. VitraTM holds the approved language of your whole organization — across video, audio, images, documents, web and app — matches it by meaning as well as by characters, and writes every approved result back so the next job starts further ahead than the last.

[Start creating free→](https://universe.vitra.ai/auth/sign-up)[Book a demo](https://sales.vitra.ai/meetings/akash-nidhi-p-s)

Free to start · 75+ languages · No credit card required

Capabilities

## What VitraTM does

### One memory, every modality

Video dubbing, subtitles, image and design files, audio, documents, website and app all read and write the same memory. Most stacks keep a separate memory per tool, which is why a term approved for the website never reaches the dubbed video.

### Semantic matching, not just character overlap

A legacy TM scores similarity on characters, so a rephrased sentence looks new and is paid for again. VitraTM also matches on meaning, which is what lets reuse survive an edit to the source copy.

### Bidirectional, row-per-language

Memory is stored a row per language rather than as fixed source-target pairs, so a match earned translating English to Tamil is available for Tamil to Hindi. Coverage compounds across the whole language set instead of one direction.

### Exact, then fuzzy, then semantic

A best-match cascade tries exact reuse, then fuzzy, then meaning-based matching, and calls a model only for what genuinely has no match. The model is the fallback, not the default path.

### Write-back learning

Every approved translation — including a reviewer's correction — returns to memory, so the next identical request is a free exact reuse and the same mistake is not made twice.

### Glossaries and pinned terminology

Product names, statutory phrasing and charge descriptions are pinned so they resolve identically regardless of context, and a general match cannot overwrite them.

### Brand kit and style guides

Tone, terminology and target-language font equivalents are applied at translation time rather than checked afterwards, with RAG-backed tuning for domain vocabulary.

### Interoperable, not a lock-in

Existing memories and glossaries import via TMX, and Phrase and XTM are selectable per organization where an external TM should stay the system of record. Reuse accrues to you rather than to the vendor.

### Organization-scoped and auditable

Memory is scoped per organization with role-based access, entries are auditable, and only approved content is written back. Agencies can run a separate memory per client under their own brand.

How it works

## Four steps, one orchestrated run

Every stage runs on the same platform, so nothing is exported, re-uploaded, or handed between tools.

- 01

### Import or start clean

Bring existing TMX and glossaries in, or let memory build itself from the work you run. Phrase and XTM are supported where an external TM stays the system of record.
- 02

### Match before generating

Every segment is checked against memory first, through the exact, fuzzy, semantic cascade — regardless of whether it arrived from a webpage, a subtitle track or a PDF.
- 03

### Generate only the gaps

Models are called only for segments with no usable match. That is what holds cost down and, more importantly, what stops approved wording being quietly regenerated into something new.
- 04

### Review, then write back

Approved translations and reviewer corrections return to memory, so quality and coverage compound with every job rather than resetting with each project.

Who it is for

## Teams using VitraTM

### One claim, every surface

The same product claim reads identically in the ad, on the site, in the app, in the terms PDF and in the dubbed explainer — because all five resolved it from one place.

### Cost that falls as you scale

Reuse is free. Repetitive estates — policy wordings, product catalogues, release notes — reach the point where each additional language and each revision costs a fraction of the first.

### Regulated terminology

Approved wording is enforced rather than hoped for, with an audit trail behind every change and pinned terms a general match cannot overwrite.

### Multi-team and multi-client scale

Ten teams producing in parallel still speak with one vocabulary, and an agency running forty clients keeps forty memories separate without forty disconnected workspaces.

FAQ

## Questions people ask

What makes VitraTM different from a standard TMS memory?
+

Three things. It is multimodal, so video, audio, images, documents, web and app share one memory rather than each tool keeping its own. It matches bidirectionally, because memory is stored row-per-language instead of as fixed source-target pairs. And it cascades exact, fuzzy, then semantic matching before calling a model, so reuse is the default rather than the exception.

What does 'AI-first' mean for a translation memory?
+

That matching is done on meaning as well as on characters. A conventional memory scores similarity textually, so rewording a sentence makes it look new and it is translated and paid for again. Semantic matching recognizes the rephrasing as the same content, which is what keeps reuse high on copy that changes wording between releases.

How does a memory reach video and images?
+

Because the transcript behind a dub, the text layer inside a design file and the body of a webpage are all segments once extracted. Running them through the same memory means a term approved on the site is the term spoken in the dub and printed on the banner, instead of three tools independently deciding.

Can I import our existing translation memory?
+

Yes. Existing memories and glossaries import via TMX, and Phrase and XTM are supported as alternate providers selectable per organization if you want to keep an external TM as the system of record rather than migrating.

Does reuse actually reduce what I pay?
+

Yes, and the effect compounds. An exact match is served from memory instead of being regenerated, so the marginal cost of repeated content approaches zero as memory grows. Estates with heavy internal repetition — policy wordings, product catalogues — see this most sharply.

What happens when a reviewer corrects a translation?
+

The correction is written back, so the next occurrence returns the corrected wording as an exact match. Review stops being a recurring cost on the same errors and becomes an accumulating asset.

Who controls what goes into memory?
+

You do. Memory is scoped to your organization with role-based access control, entries are auditable, and only approved translations are written back. Nothing enters memory on a model's judgement alone.

Does VitraTM work with our own models?
+

Yes. Provider keys are held per organization and fetched just-in-time, so the generation that fills memory gaps can run through your own LLM, TTS and STT vendors. Fully air-gapped deployments run the same cascade on your own GPU servers.

## Explore next

[Quality Control→Verify what memory cannot.](https://www.vitra.ai/features/quality-control)

[Document Translation→Memory across every format.](https://www.vitra.ai/features/document-translation)

[Vitra Memory→Facts, where this is wording.](https://www.vitra.ai/features/knowledge-base)

[The Platform→Where memory sits in the stack.](https://www.vitra.ai/platform)

## Start with VitraTM. Grow into the whole platform.

Everything in Vitra Universe shares one translation memory, one brand kit, and one quality bar — so the work you do here makes everything you do next faster.

[Start creating free→](https://universe.vitra.ai/auth/sign-up)[Book a demo](https://sales.vitra.ai/meetings/akash-nidhi-p-s)

---

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