Has your business adopted AI translation to improve efficiency—only to see the expected time and cost savings wiped out by extensive post-editing?
The problem isn’t necessarily the quality of your AI translations.
It’s a question of governance: instead of relying on post-editing to fix quality issues, it’s more effective to prevent those issues from arising in the first place.
At Rubric, our approach is to build quality into the localization process from the outset. We do this through good content governance, careful terminology management, risk-based workflows, and quality frameworks that are tailored to your brand and requirements.
The result is less post-editing effort, and more confidence in your AI translations—with faster delivery and lower localization costs.
More post-editing is not the answer
Many organizations apply a simple workflow to all AI translation:
Translate → Review everything → Publish
The risk here is that you simply replace translation costs with review costs.
Rubric takes a more considered—and effective—approach:
Govern → Translate → Target review → Publish
Establishing a solid quality framework before translation begins can significantly reduce the level of human intervention that is required later in the process.
This means you get more value from AI translation, and you can scale your global content without compromising on quality.
Quality starts before translation begins
Investing in some preparation upstream before you start translating can dramatically improve AI translation quality and reduce post-editing effort.
We work with you to establish:
- Quality expectations: Does your audience need this content to be perfect, or just understandable? What does “acceptable” quality look like?
- Terminology management: Robust glossaries of key terms ensure consistent translations and prevent ambiguities.
- Brand and style guidance: We can train the AI to follow your rules, so your brand voice is protected.
- Content risk classifications: The right workflow for your content depends on its level of business risk.
- Review criteria: Setting clear standards ensures consistent and objective review across markets.
- Governance processes: How will you continue to monitor, evaluate, and improve translation quality?
With these foundations in place, AI produces higher-quality translations and greater consistency across languages and assets.
So, rather than fixing avoidable errors, human reviewers can focus on genuine business risks.
A risk-based post-editing strategy
Not all content carries the same level of risk: a product launch or legal document will likely have more business impact than a support article or an internal communication.
We encourage our clients to classify content according to its risk level and business impact, so it can be handled through the appropriate workflow.
Our Assured AI solution gives you the perfect balance of AI translation, customization, automated quality controls, and human review. This eliminates wasted effort and focuses your resources where they add most value.
When is post-editing still required?
Even as AI translations improve, post-editing remains an important part of most localization strategies.
But remember that post-editing is not proofreading, and doesn’t necessarily involve a translator checking every word. The key is to define what level of post-editing is required for your content, and to apply it in a way that adds value.
Light vs full post-editing
Light post-editing simply makes sure your content is understandable and fit for purpose. It’s used when speed and efficiency are more important than linguistic perfection.
Light post-editing checks typically focus on:
- Major translation errors
- Critical terminology issues
- Omissions and additions
- AI hallucinations
- Severe fluency problems impacting understanding
It’s best suited for:
- Internal communications
- User-generated content
- High-volume, lower-risk content
While light post-editing can deliver acceptable quality quickly and at scale, bear in mind that translations may not fully match your brand voice or display perfect fluency.
Full post-editing aims to bring machine translations up to a human quality standard. It’s designed for content where translation quality is critical to business outcomes.
Full post-editing checks typically focus on:
- Accuracy
- Terminology (adherence to in-house glossaries)
- Brand voice and house style
- Readability and fluency
- Cultural relevance and appropriateness
- Formatting and consistency
It’s best suited for high-impact, high-visibility content such as:
- Websites
- Marketing content
- Customer-facing materials
- Product documentation
Full post-editing can provide peace of mind, but it adds time and cost—so be sure your content really needs this level of scrutiny. The upstream localization improvements mentioned earlier will reduce the effort involved.
Translation quality scorecards: a framework for quality and value
To scale your global content, you need to be able to measure quality and replicate it consistency across markets.
A translation quality scorecard provides a standardized framework for measuring, monitoring, and evaluating translation quality.
It sets out clearly defined measurement criteria such as:
- Accuracy
- Terminology use
- Fluency and readability
- Grammatical correctness
- Brand alignment
- Cultural appropriateness
- Formatting
Implementing a quality scorecard for all translation activity helps your business to:
- Define quality expectations
- Measure performance objectively
- Improve consistency
- Identify improvement opportunities
- Compare performance across teams, providers, and languages
Crucially, this objective measurement tool provides confidence in your translations, demonstrating that quality standards are being applied consistently across markets and content types.
Reduce post-editing effort with improved AI translations
Improving the quality of your AI outputs from the outset reduces human post-editing effort, resulting in faster turnarounds and lower overall spend.
There are many ways we do this at Rubric, including:
- Content-specific AI optimization (training the models on your industry and brand)
- Use of translation memories (drawing on already-approved translations)
- Bespoke quality frameworks built around your needs
- Automated quality checks, highlighting issues requiring human input
- Risk-based governance
We help you build a localization framework where AI can perform at its best, and human expertise is directed where it matters. So you can have confidence in your translations, without paying for more than you need.
The Rubric approach: Better quality with less editing
While other providers focus on post-editing AI translations, we focus on reducing and targeting post-editing effort.
For us, AI translation is part of a framework that combines governance, automation, terminology management, quality scorecards, and human expertise. We bring all of this together to help you:
- Accelerate multilingual content delivery
- Reduce localization costs
- Improve translation quality and consistency
- Minimize translation risk
- Scale AI translation with confidence
Ready to reduce post-editing and scale smarter?
If lengthy post-editing means you’re not realizing the savings you expected from AI translation, it’s time to get to the root of the problem.
With better governance, clearer quality standards, and smarter translation workflows, you can reduce the need for post-editing and balance quality, speed, and cost in your localization.
Talk to our experts about a free proof of concept, and start building a risk-based localization framework that targets human expertise where it matters—helping you scale your multilingual content quickly, confidently, and within budget.