Data is only as valuable as the decisions it enables. For decades, the localization industry has relied on retrospective metrics to measure success. Businesses would complete a project, wait for a third-party review, and receive a pass/fail report weeks later based on sample checks. This approach is like driving a car while looking exclusively in the rearview mirror: it tells you where you have been, but it offers no insight into the road ahead or how to optimize the journey in real time.
Performance intelligence: A new standard for quality
Performance intelligence represents a fundamental shift in how we evaluate localization. It moves beyond the binary question of “Was this translation correct?” to the systemic question of “How effective is our entire translation process?”
This approach offers a holistic view of the localization workflow by combining quality, efficiency, and business impact into a single actionable framework. Unlike traditional methods that rely on manual tracking or delayed Errors Per Thousand (EPT) inspections, performance intelligence leverages near-real-time data to predict outcomes and flag potential issues long before they affect final delivery.
To achieve this, we must look at two distinct but complementary dimensions:
the efficiency of AI and the accuracy of humans.
The role of Time to Edit (TTE)
A core component of modern performance intelligence is Time to Edit (TTE). As organizations evolve toward a model of Human-AI Symbiosis, understanding the interaction between AI and the linguist becomes essential.
TTE measures the average number of seconds a professional translator spends editing a machine-translated segment to bring it to human quality. It is an objective, immediate indicator of translation utility:
- Low TTE means that the AI—such as Lara , Translated’s proprietary translation model built for full-document context—generated output requiring minimal effort to verify or refine.
- High TTE signals that the model struggled with terminology, context, or nuance, forcing the linguist to rewrite portions of the text.
By tracking TTE, providers and their enterprise customers (where exposed) gain quantitative insight into content complexity, translator effort, and model performance—transforming what was once subjective intuition into measurable, actionable data.
Benchmarking with Errors Per Thousand (EPT)
While TTE measures efficiency, Errors Per Thousand (EPT) serves as a primary metric for linguistic accuracy within linguistic QA (LQA). EPT tracks the number of categorized errors per 1,000 words and allows organizations to benchmark:
- Translator accuracy
- Source text clarity
- Terminology consistency
- Overall linguistic quality
Using TTE and EPT together provides a full picture of both the machine’s performance and the human’s accuracy.
Analytics implementation: From data to decisions
The obstacle for most enterprises is not a lack of data, but a lack of integration. Valuable information—translation memory logs, QA spreadsheets, vendor emails, turnaround times—often lives in isolated silos.
An AI-first platform like TranslationOS breaks down these silos. Acting as the operational backbone for the localization workflow, TranslationOS automatically captures key data at every stage of the lifecycle. Because the platform manages everything—from content ingestion and linguist assignment to delivery and invoicing—it can correlate data points that disconnected tools cannot.
TranslationOS provides near-real-time visibility into the health of the translation pipeline, showing how specific content types and language pairs perform, where delays cluster, and where quality risks emerge. Clear dashboards transform raw linguistic and operational data into insights that allow managers to shift from reactive problem-solving to proactive quality governance.
Optimization strategies: Turning insights into action
Data alone does not create value—action does. With a unified analytics foundation, optimization becomes continuous and targeted.
Optimizing translator assignments with performance data
Instead of relying solely on language pair and availability, platforms like T-Rank™ use historical performance metrics—including quality outcomes such as EPT and other reliability signals—to identify the best linguist for each subject matter. This ensures that the translator most capable of excelling in a given domain receives the assignment.
Improving MT models with TTE feedback loops
In a Human-AI Symbiosis model, every edit becomes a learning opportunity.
A spike in TTE for specific content signals that the AI (Lara) is underperforming—perhaps due to new terminology or shifts in tone. These highly edited segments can be fed back into the model, enabling it to adapt and improve. Over time, this reduces TTE and increases translator velocity.
Identifying source content that requires special attention
High TTE or EPT often reflects issues in the source text, not the translation. Performance intelligence helps identify:
- Ambiguous phrasing
- Inconsistent terminology
- Content that repeatedly slows down production
Localization teams can then refine instructions or collaborate with content creators to improve upstream quality—preventing issues from multiplying across dozens of languages.
Strategic impact: Driving global growth
The goal of quality analytics is not merely to reduce errors—it is to enable global scale.
Visibility into performance is essential for high-growth companies. As documented in the Asana case study, the company partnered with Translated and implemented TranslationOS to centralize workflows, integrate with their systems, and coordinate localization across multiple markets. Their success demonstrates how operational transparency unlocks scalability.
When localization leaders can demonstrate efficiency and quality with hard data, the function moves from a cost center to a strategic driver of international growth.
Continuous improvement: The future of quality management
The industry is shifting from “good enough” to precision at scale. Performance intelligence creates a cycle of continuous improvement—better data leads to better decisions, resulting in higher quality, lower effort, and faster global releases.
This approach transforms translation analytics from a retrospective audit into a strategic roadmap. It enables accurate forecasting, consistent quality across markets, and proactive governance that aligns linguistic output with business goals.
By connecting translation performance directly to global outcomes, performance intelligence ensures that your language operations evolve as fast as your market. Don’t settle for outdated metrics that describe the past—embrace the intelligence that illuminates your future.
Conclusion: Quality Intelligence That Powers Global Scale
The future of localization belongs to organizations that treat quality not as a final checkpoint, but as a continuously optimized system. With performance intelligence—driven by metrics like TTE, EPT, and real-time operational visibility—global companies gain the clarity, speed, and predictive power they need to scale confidently.
When every insight feeds back into your workflow, quality becomes consistent, risks shrink, and international expansion accelerates. This is how localization transforms from a reactive function into a strategic engine for global growth.
If you’re ready to move beyond intuition and fragmented reporting—and toward a modern, data-driven quality framework—Translated can help you get there.
Discover how TranslationOS and our Human-AI Symbiosis model deliver measurable quality, predictable performance, and true global scale.
Request a demo of TranslationOS today.