Language Quality Evaluation

Language that lands. Not just language that passes.

Your content can be grammatically correct and still feel… off. Too stiff. Too casual. Too “translated”. Too risky.

BeatBabel helps you measure and improve language quality at scale, across locales, channels, and AI systems, with human expertise and clear scoring you can track over time.

We evaluate: human translation, AI-generated text, MT post-editing, chatbots, UI strings, marketing copy, help centers, knowledge articles, and everything in between.

What makes BeatBabel different

  • We treat language as product quality, not an afterthought

  • We measure what matters: clarity, tone, cultural fit, and consistency

  • We deliver results you can track, compare, and defend (internally and externally)

  • We’re comfortable inside modern stacks: TMS workflows, AI pipelines, human-in-the-loop

  • We have been specializing on LQA for many clients over the past 16 years and have hand-picked our QA experts.

If your content is multilingual and user-facing, language quality is part of trust. Let’s evaluate what your users actually experience. Get a pilot scorecard in weeks, not quarters.

How it works
















Deliverables

Human judgment, structured like a system. We don’t do vague feedback. We do repeatable evaluation.
1) We align on your quality standard
Choose your flavor:

  • Your internal style guide, if you have one

  • A BeatBabel-ready rubric we customize

  • Or an industry framework (MQM-style categories, adapted to your needs)

2) We sample smart
We define the right sampling approach based on volume and risk:

  • Random sampling for general quality health

  • Targeted sampling for critical flows (checkout, onboarding, claims, support macros)

  • Regression sampling for “did the new model/vendor break things?”

3) We score and annotate
Every issue is tagged, graded, and mapped to impact so it’s not just “wrong”, it’s “here’s what to fix first”.

4) We turn findings into improvements
You get actionable output:

  • top error types

  • root causes (vendor, style guide gaps, glossary gaps, prompt issues, UI constraints)

  • fix recommendations

You can use these internally, with vendors, or as part of ongoing governance.

  • Quality Scorecard by language / locale / content type

  • Annotated error set with categories + severity

  • Trend reporting over time (great for AI releases and vendor management)

  • Glossary / style guide improvement suggestions

  • Executive summary for stakeholders who want the headline, not the weeds

  • Optional add-ons:

    • Linguistic sign-off for high-visibility launches

    • Vendor calibration sessions (so your LSP and reviewers score the same way)

    • AI prompt + system-message tuning based on evaluation findings

    • Ongoing monitoring (monthly/quarterly)

What we evaluate

Language quality is not one thing. We break it into signals you can actually act on:

  • Accuracy
    Meaning preserved. No missing info. No creative rewrites disguised as “localization”.

  • Fluency
    Natural grammar, idioms, and phrasing that sound native (not “international English in another language”).

  • Terminology & consistency
    Product terms, brand terms, and key phrases used consistently across content.

  • Style & brand voice
    Your tone, your register, your rules. Maintained across markets.

  • Locale correctness
    Dates, currency, formality, punctuation, address formats, and market expectations.

  • Clarity & usability
    Especially for UI, support, and instructional content: is it easy to understand and follow.

  • Compliance-sensitive language checks (for regulated or higher-risk content):
    Claims, disclaimers, medical/financial phrasing, and “this could get screenshot” moments.

Engagement options










Where teams use Language Quality Evaluation

Pilot

A fast baseline: where quality stands today, and what’s breaking most.
Or a through first evaluation to select the best AI engine or LSP to handle your content.

Ongoing Monitoring

A regular rhythm (monthly/quarterly) with trendlines and regression detection.

Launch & Risk Review

Focused evaluation for critical releases, high-stakes content, or regulated markets.

Global product and UX teams shipping UI in multiple languages

  • Marketing teams localizing campaigns across markets

  • Support teams running multilingual knowledge bases

  • AI teams evaluating MT/LLM outputs across locales

  • Localization managers benchmarking vendors and workflows


    Ask us for a quote!