7 AI tools your localization team needs to master in the GenAI era

3. Translation memory (TM)

Translation memory is a database that stores previously approved translations and automatically reuses them when similar or identical content appears in future localization projects.

Translation memory has always been a core part of localization team software. In the GenAI era, it becomes even more important because it acts as the foundation for context.

AI models generate fluent translations, but without grounding, they default to generic output. That’s where TM comes in. It ensures that what your team has already approved continues to shape every new translation.

When TM isn’t properly integrated into your AI localization tools, teams run into familiar problems:

• Approved translations get rewritten unnecessarily, creating inconsistency
• Terminology and phrasing drift over time, especially across markets
• Post-editing effort increases, because AI output doesn’t reflect past decisions
• Costs rise, as teams spend time fixing work that should have been reused

How Lokalise helps

Lokalise integrates translation memory directly into its AI capabilities using a RAG (Retrieval-Augmented Generation) approach. At runtime, the system retrieves relevant TM matches and injects them into the AI generation step.

As a result:

• Repeated content is automatically reused instead of retranslated
• AI output aligns with your existing terminology and phrasing
• Post-editing time drops significantly

4. Glossary and terminology management

A localization glossary is a structured list of approved terms and their translations, ensuring consistent use of brand terminology, product-specific language, and industry jargon across all markets and AI tools.

AI models generate fluent text, but they don’t inherently know your product language, brand voice, or market-specific nuances. Without a controlled glossary, even strong translations can introduce subtle inconsistencies or incorrect terms.

These issues tend to show up quickly at scale:

• Key product terms are translated differently across markets, creating confusion for users
• Brand voice becomes inconsistent, especially in marketing and UI copy
• Industry-specific language gets simplified or misinterpreted, reducing clarity and trust
• Review time increases, as linguists spend time correcting terminology instead of refining meaning

A well-maintained glossary solves this by acting as a source of truth for terminology. It ensures that every translation, human or AI-generated, uses the same approved language.

How Lokalise helps

Lokalise integrates glossary and terminology management directly into its AI capabilities. Through its RAG-based approach, glossary terms are automatically retrieved and applied during the translation process. Teams can upload, approve, and manage terminology in one place, and AI consistently follows those rules as it generates translations.

This ensures:

• Consistent terminology across languages, teams, and content types
• Reduced review effort for linguists
• Accurate, on-brand translations at scale

Let’s say you’re localizing a digital banking app. It includes a lot of industry-specific terminology, along with product feature names that shouldn’t be translated at all. Now take the term “current account.” It might need to be translated into Spanish as “cuenta nómina” for a specific market.

Without a glossary, AI might default to a more generic or incorrect equivalent. With a glossary in place, that choice is enforced every time.

5. AI translation quality scoring (LQA)

AI translation quality scoring (LQA) is an automated system that evaluates translation quality against linguistic standards, categorizes errors, and flags low-confidence segments for human review—eliminating the guesswork from localization QA.

As AI localization tools for teams speed up translation, quality becomes the main bottleneck. The challenge lies in knowing what’s good enough to publish and what still needs attention.

Without a scoring system in place, teams face a difficult tradeoff:

• Review everything, which slows down delivery and creates unnecessary work
• Review selectively without clear criteria, which risks errors slipping through
• Rely on intuition instead of data, leading to inconsistent QA decisions

I used to stress that wrong machine translations were released before anyone could review them… Since last month, it's over. AI automation provides 90% translations right the first time.

~ Roman Dagan, Senior Product Manager at Florence

At scale, this doesn’t hold. Teams need a way to prioritize review effort and make quality decisions based on clear signals. You can learn more about LQA by watching the webinar below.

AI translation quality scoring turns QA into a structured, data-driven process. Instead of treating all content equally, it identifies which segments are high confidence and which require human review. In modern GenAI tools for translation teams, this becomes a key layer of control.

How Lokalise helps

Lokalise's Translation Scoring flags which translations need human review and which are ready to publish. It relies on the MQM (Multidimensional Quality Metrics) framework, a widely used standard for evaluating AI translation quality. This is how Lokalise automatically detects and categorizes linguistic issues, suggests corrections, and integrates directly into workflows.

That means:

• High-scoring translations can move straight to publication
• Low-scoring segments are automatically routed for human review
• Teams focus their time where it actually adds value

The impact is immediate. With Lokalise’s AI capabilities, only around 20% of content requires post-editing, allowing teams to scale output without scaling review effort.

6. Custom AI profiles (powered by RAG)

Custom AI Profiles are personalized LLM configurations that adapt AI translations to a brand’s specific voice, tone, terminology, and past approved translations using Retrieval-Augmented Generation (RAG).

AI models are good at producing fluent translations. But fluency isn’t enough. For localization teams, the real requirement is consistency. This means using the right terminology, matching brand voice, and aligning with how content has been translated before.

Without that layer, teams run into familiar issues:

• Translations sound generic, especially in marketing and customer-facing content
• Brand voice shifts across markets, depending on how the model interprets tone
• Previously approved phrasing is ignored, even when similar content already exists
• Review effort increases, as linguists rewrite content to match brand standards

How Lokalise helps

Lokalise’s Custom AI Profiles use a RAG-based approach to retrieve relevant translation memory, glossary terms, and style rules at runtime. That context is injected into the LLM before it generates the translation. The result is output that reflects your brand voice, terminology, and past decisions, consistently.

Normally, the overall process would have taken a minimum of 2 months and up to 3, depending on resources. Of course, I had to train the AI with our glossary and style guide and everything, but it was amazing. We finished and we were able to get to the finish line despite all the holidays.

~ Joaquín Muñoz, Localization Manager @ Life360

For localization teams, this significantly reduces the gap between AI-generated content and human-reviewed quality.

The impact is clear:

• Custom AI Profiles achieve ~90% acceptance rates from human reviewers
• Separate word quotas allow teams to manage usage independently from standard AI translations

By leveraging translation memory and AI translation for repetitive content and email templates, DepositPhotos cut email translation costs by 90%.
~ Tetiana Rublova, Localization Manager @ DepositPhotos

If you’re evaluating AI localization tools for teams, this is a key differentiator. Custom AI Profiles allow you to scale translation without losing the consistency and quality your brand depends on.

7. CAT tools for human linguists

Computer-assisted translation (CAT) tools are software environments that help human translators efficiently review, edit, and approve AI-generated translations using translation memory, glossary, and in-context previews.

Even with the best AI localization tools in place, human review is still mandatory. AI can handle volume and speed, but it doesn’t fully replace judgment. Especially when nuance, tone, and cultural context matter.

In practice, most teams see 80–90% of AI translations accepted as-is. The remaining 10–20% still needs human input. That’s where machine translation post-editing (MTPE) plays a key role.

Without a structured CAT environment, teams run into friction:

• Review happens across disconnected tools, leading to copy-pasting and version issues
• Context is missing, making it harder to assess how translations will appear in the product
• Terminology and past decisions aren’t visible during review, reducing consistency
• Linguists spend time on low-impact edits, instead of focusing on what actually needs attention

CAT tools solve this by giving human linguists a dedicated space to review and refine translations with full context. Instead of translating everything from scratch, linguists focus on:

• Validating AI-generated content
• Refining tone and cultural nuance
• Reviewing high-stakes content like UI, legal, and marketing copy
• Ensuring final quality before publication

This is what “humans in the loop” looks like in practice. AI handles scale while humans focus on quality.

How Lokalise helps

Lokalise’s editor functions as a built-in CAT tool within its localization platform. Linguists can review, edit, and approve AI-generated translations directly in the same environment where content is managed, without switching tools or breaking the workflow.

That means:

• No copy-pasting between systems
• Full visibility into translation memory and glossary during review
• In-context previews for better decision-making
• A seamless transition from AI output to final approval

For localization teams, this keeps human review efficient and focused, without becoming a bottleneck.

Is your localization team set up for the GenAI era?

Use this checklist to quickly assess where your current workflow stands:

• A centralized TMS to manage your full localization workflow
• AI orchestration that routes translations to the best-performing model
• Translation memory integrated into your AI workflow to reuse approved content
• A glossary that enforces consistent terminology across all markets
• AI translation quality scoring to identify what needs review and what’s ready to publish
• Custom AI profiles that align translations with your brand voice and past decisions
• CAT tools that support efficient human review and machine translation post-editing (MTPE)

If you’re missing even one of these, your workflow likely has gaps, whether that’s slower delivery, inconsistent output, or unnecessary costs.

If you’re checking all seven, the next question is how well they work together.

A GenAI-ready localization workflow depends on how context, automation, and quality controls connect. The visual below shows how these seven tools come together as a single system.

How to build a GenAI-ready localization stack

A GenAI-ready localization stack works as a system. Each tool that’s part of the system plays a specific role, and the value comes from how they connect.

At the center is the translation management system (TMS). It acts as the hub where all localization workflows are managed. On top of that sits AI orchestration, which routes each task to the best-performing model based on language pair and content type.

From there, translation memory, glossary, and Custom AI Profiles provide the context layer. They ensure every translation reflects approved terminology, past decisions, and brand voice so AI output stays consistent across markets.

Next comes translation quality scoring, which acts as the quality gate. It determines what’s ready to publish and what needs human review, allowing teams to focus effort where it matters.

Finally, CAT tools enable human linguists to review, refine, and approve translations efficiently, closing the loop between AI speed and human judgment.

Together, these seven tools form a single, continuous workflow inside Lokalise. Content moves from ingestion to translation, review, and publication without switching systems or losing context.

Lokalise gives your team all 7 tools in one AI-native platform. Start your free 14-day trial or book a demo with a localization expert.