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AI localization that keeps your terms and meaning

Every language your market needs, in your own terms

For MindSonar we automated 14,000 report translation variations across 8 languages, with native-speaker quality as the bar.

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localization

  1. Product managerYour team

    The new report template is approved in English. Can it go out in German, French and Arabic this week?

  2. Localization agentAgent

    1. cms.get_source approved English template
    2. glossary.get_terms product terms per language
    3. llm.translate German, French and Arabic
    4. qa.check_terms glossary terms and placeholders

    All three are drafted. Two French terms differ from your glossary, so the French version needs a reviewer.

    Needs a personFrench terms differ from glossaryReport template, French version
    Approved by French reviewer

  3. Localization agentAgent

    1. cms.publish three language versions

    Published in all three languages. The reviewer's choice is added to the glossary for next time.

Example data. An agent at work in a team channel, with a person approving the step you choose.
Mixam: conversion to a final sale from about 20% to about 40%
Where agents help

New markets wait for the same content in a new language.

Agents draft each language version from your glossary and past work, and native reviewers sign off where you choose.

  1. 01

    Where it stallsEach new language multiplies the translation work.

    What we do An agent translates every variant of your content in one pass and keeps structure and placeholders intact.

  2. 02

    Where it stallsMachine translation gets words right and meaning wrong.

    What we do The agent translates meaning in context and follows your glossary for product and domain terms.

  3. 03

    Where it stallsNobody checks consistency across hundreds of versions.

    What we do Code checks terms, numbers and placeholders in every version and flags mismatches for a reviewer.

What we build

What we build for localization

Whatever comes in, every project is built from the same parts below, set up for your files and your systems.

Content we localize

  • Product interfaces
  • Help centres
  • Marketing pages
  • Product catalogues
  • Legal and policy text
  • Emails and notifications
  • Training material
  • App store listings

How we build it

  • Translation that follows your glossary

    LLM translation guided by your terms, style rules and approved past translations.

  • Multilingual answers from one source

    Agents that answer customers or staff in their own language from one set of documents.

  • Connections to your content systems

    Source content pulled from your CMS or product data, and finished versions published back.

  • Review rules for native speakers

    New terms, legal text and sensitive markets go to a native reviewer before anything is published.

Not seeing your case? Bring the content that holds up your next launch and we start from that.

Tell us about your workflow
How we build agents

Agents that work as part of your team

They work in the tools your team already uses, take on whole tasks and stop for a person where you decide.

  1. Routines: agents that start on a schedule, on new GitHub issues or on an API delivery.
  2. Connected systems: GitHub, Linear, Notion, Slack, Jira and other tools an agent may call, each connected with its own sign-in.
  3. Approvals log: each action that waited for a person, with who asked, who decided and when.
Routines: agents that start on a schedule, on new GitHub issues or on an API delivery.
Connected systems: GitHub, Linear, Notion, Slack, Jira and other tools an agent may call, each connected with its own sign-in.
Approvals log: each action that waited for a person, with who asked, who decided and when.

Screens from the workspace our agents run in, with example data.

Where it runs

Teams and industries that use it

Each page shows where agents fit in that team or industry.

Engineering in the open

We help build what agentic AI runs on

Open standards, open-source tooling and published research, all public, so you can check our engineering before you hire us.

Shaping agentic AI

Agentic AI Foundation

Vstorm was the first agentic AI engineering company to join the Agentic AI Foundation and received its 2026 Open Agentic AI Innovation Award.

Read the announcement

Open source

We build open-source agent tooling that other engineering teams install

10M+ PyPI downloads
4,000+ GitHub stars
33 public repositories

Framework contributors

When the framework breaks, we fix it at the source

We maintain our own open-source agent framework and contribute fixes to the frameworks our agents run on, through official partnerships with their teams. Breaking changes reach us before they reach your codebase.

Research

Our engineers and PhDs publish peer-reviewed research

Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results.

Track record

Frontier AI teams now build on what we shipped first

Our client agents were in production before most of today's agent frameworks had a stable release. You get a team that already knows what breaks between a promising demo and a system people rely on every day.

Case studies
All case studies
Engagement models

Ways to start

Each path starts with a free call with a founder or senior AI engineer. A build goes ahead only after its Proof of Value meets the agreed threshold.

Start with one workflow

We map one workflow with your experts and agree a success threshold before any build.

Best whenYou want the success test agreed before you commit to a build.

Book a free consultation
  1. Process redesign and build Best whenYou want one team accountable for the outcome.
FAQ

AI localization, answered

What is AI localization? +
AI localization uses language models to adapt content for a new market: reports, product data, help content, emails and interface text. An agent translates meaning in context, sometimes called semantic translation, follows your glossary and style rules, and checks every version for terms and placeholders. Native reviewers approve the versions and terms you choose before they are published.
What content should we localize with AI first? +
Content with many variants and a stable vocabulary: report templates, product descriptions, help articles, standard customer emails. One approved source turns into many versions, so the gain is largest here. Start with one content type and two or three languages, agree a quality bar with a native reviewer for each, and measure the agent against translations your team already approved.
Have you built AI translation and localization before? +
Yes. MindSonar's mindset reports had to reach new countries, and native speakers were translating every variant by hand. We built an LLM pipeline that automated 14,000 report translation variations across 8 languages, with native-speaker quality as the bar. For ARIJ Network we built an agent that answers in English or Arabic from one knowledge base, and Senetic's sales email is answered in the customer's language.
Is AI translation accurate enough for customer-facing content? +
Yes, when quality is measured per language and enforced in code. The agent follows your glossary, and code checks every version for missing terms, changed numbers and broken placeholders. Anything new or sensitive goes to a native reviewer, whose corrections update the glossary and the test set. We agree the quality bar per language with your reviewers before the build.
How do you know it works before the full build? +
We agree a success threshold with your team before the build and test the agent on your own cases in a Proof of Value. It measures accuracy and running costs, and the build goes ahead only when the threshold is met. Corrections from your team go back into the test set, so every later version is checked against them.
Who owns the system, and which models does it run on? +
You own the code, prompts, evaluation sets and rules by contract. The agents run in your environment on hosted or open-weight models. The model sits behind an interface in code, so you can change it when accuracy, speed or your hosting rules change, without rebuilding the agent.
Start with one workflow

Test one workflow on your own data

A Proof of Value shows accuracy and running costs before the full build.

Book a free consultationRead case studies