A Fortune 500 global e-commerce organization selected Welo Data to deliver high-accuracy Machine Translation Post-Editing (MTPE) and structured quality governance for its internal MT program — a system used to generate customer-support agent–facing content at scale. 

Because these strings directly shape how associates communicate with customers, linguistic precision, tone consistency, and variable safety were critical to both operational reliability and ongoing model improvement. 

The engagement began with a Polish pilot and was built from the ground up to scale across 15+ additional language pairs, spanning markets across Europe, the Americas, Asia, and the Middle East. From the start, Welo Data’s mandate was clear: deliver post-edited output that was not only production-ready, but structured to generate the model-improvement signals the client needed to continuously refine its MT system. 

The client required a partner who could combine expert linguistic judgment with a quality governance architecture purpose-built for multilingual AI training data — and who could do so across diverse markets without sacrificing consistency. 

The Challenge

Evaluating and correcting machine-translated content for customer-support use cases is more demanding than standard MTPE. These strings don’t exist in isolation — they inform real associate-to-customer interactions, making tone mismatches, terminology errors, or segmentation issues directly impactful to the customer experience. 

The client’s existing MT output suffered from recurring patterns of over-literal phrasing, inconsistent application of brand tone, and terminology drift across language pairs. What was missing wasn’t just corrected output — it was a structured methodology for identifying and classifying systemic MT errors in a way that could inform model retraining. 

Welo Data was tasked with building a two-layer quality workflow capable of delivering both production-ready MTPE and the kind of structured, annotated feedback that machine learning teams could act on directly. The critical design requirement: a governance architecture that caught errors at the pattern level — classifying systemic failure modes, not just correcting individual strings. 

The Approach

Welo Data implemented a two-layer quality model designed to protect production readiness while generating structured model-improvement signals at every batch. 

The first layer — Primary Linguist MTPE — placed experienced linguists directly inside the client’s translation management system. Each linguist performed full post-editing, applying tone normalization aligned to the client’s customer service guidelines, enforcing terminology and segmentation standards, validating variables and placeholders, and assigning issue-level severity scores on a 1–5 scale. Alongside corrected output, linguists documented recurring MT issues, flagging terminology drift, clarity problems, and structural patterns that signaled deeper model deficiencies. 

The second layer — Senior Reviewer QA — operated as a sampling-based governance pass. Senior reviewers validated fidelity, tone, terminology, and formatting; cross-checked contributor severity scoring for consistency; and synthesized systemic error patterns at the batch level. Their commentary went directly into structured reporting delivered to the client after each batch. 

A Quality Manager oversaw cross-language KPI stability, managed calibration sessions, and ensured that quality standards remained consistent as additional language pairs were onboarded. 

Key Project Components:

• Pilot Language: Polish (pl-PL) — 1,000-word batch, 5-day turnaround from receipt of access and guidelines 
• Expansion Roadmap: 15+ language pairs including Dutch, Korean, Swedish, German, French, Japanese, Arabic, and more 
• Two-Layer Workflow: Primary linguist MTPE + sampling-based senior reviewer QA 
• Severity Framework: 1–5 issue-level ratings across six quality dimensions: Meaning Accuracy, Tone & Style, Terminology Accuracy, Fluency & Naturalness, Segmentation & Formatting, and Completeness 
• Tooling: All editing performed directly within the client’s translation management system (ATMS), with Welo Data’s internal environment supporting QA logs, findings documentation, and reporting preparation 
• Throughput: ~2.0 hours per 1,000 words (MTPE); ~1.0 hour per 1,000 words (Senior Reviewer QA) 
• Acceptance Standard: The Polish pilot achieved 99.6% quality accuracy, exceeding the program’s ≥95% acceptance standard and validating the workflow prior to multi-language expansion. 

The design paired structured execution with adaptive quality governance — giving linguists clear frameworks while enabling Senior Reviewers and the Quality Manager to surface and escalate systemic issues in real time. 

Outcomes and Impact

The pilot cleared the program’s quality threshold with room to spare, delivering production-ready MTPE output within the agreed five-day window alongside structured severity scoring, findings documentation, and a batch-level MT quality insight summary ready for the client’s model-improvement workflows. 

By combining linguist-level error documentation with senior reviewer synthesis, the program produced a structured error taxonomy the client’s ML team could act on directly — not just cleaner strings, but a classified signal for model retraining that mapped exactly where and how the MT system was failing. 

What made the pilot particularly valuable was that it was designed from day one to carry the full multilingual roadmap. The calibration workflows, escalation paths, and quality oversight mechanisms built during a 1,000-word Polish batch were deliberately architected to hold at 15+ language pairs — meaning the client’s expansion wasn’t starting over, it was activating infrastructure already proven in production. 

Key Results:

• Achieved 99.6% quality accuracy, exceeding the ≥95% acceptance standard and validating the workflow for multi-language expansion 
• Structured severity tagging across 6 quality dimensions per batch 
• Systemic MT error patterns identified, classified, and documented for model refinement 
• Scalable two-layer QA framework established and ready to extend across 15+ language pairs 
• 5-day pilot turnaround achieved from receipt of access and guidelines 
• Batch-level MT quality insight summaries delivered to support ongoing model improvement 

Why It Matters

Machine translation is only as reliable as the human oversight built around it. For enterprise organizations deploying MT in customer-facing or associate-facing contexts, the stakes of linguistic error extend beyond quality scores — they affect customer trust, operational accuracy, and brand consistency across every market. 

This engagement demonstrated that production-ready MTPE and model-improvement data generation are not separate workstreams — they can and should be unified within a single, well-governed workflow. By pairing expert linguists with structured severity frameworks and senior reviewer synthesis, Welo Data delivered output that served both immediate production needs and the longer-term goal of improving the underlying MT system. 

As the program scales across additional language pairs, the infrastructure pre-built during the pilot will ensure consistent quality benchmarks, predictable throughput, and locale-specific linguistic fidelity at a level that generic MT correction workflows simply cannot provide. 

This is what responsible MT deployment looks like at scale — where human oversight isn’t a checkpoint, it’s the system. 

Welo Data

The human layer behind enterprise AI evaluation.