When localization teams start implementing AI, one of the first things they notice is that the results aren’t consistent. Quality may look just fine in one area but becomes inconsistent as soon as the content, languages, or context change.
Localization teams—ours included—have spent years working with standardized systems that behave in predictable ways. The tools we’ve traditionally used, such as machine translation, tend to act consistently from one project to the next, which makes them easy to plan around. But so far, AI won’t stay consistent because models respond differently depending on the content and task.
When inconsistencies show up in one content set after another, it throws off how teams estimate review time and plan their workloads, not to mention introducing a host of other operational issues. It’s natural for teams to want to pin down what’s driving that instability and keep the workflow steady.
To explain our approach, we spoke with Erik Vogt, Solutions & Innovations Director at Argos Multilingual. In the Q&A below, he explains how we evaluate model behavior, trace the causes of variation, and design workflows that stay reliable over time.
What do clients usually expect when they first ask for an AI-enabled localization workflow?
Typically, it’s three things: faster turnaround, lower cost, and a consistent level of quality that matches or improves their current process. Most come in assuming AI is a plug-and-play upgrade, something that can be layered on top of their existing workflow without much adjustment. Because a lot of people have seen impressive generative AI demos, they expect uniform quality regardless of domain or language. The early conversations are usually about resetting those expectations.
What factors have the strongest influence on how an AI localization workflow performs?
The conversation around AI localization often focuses on picking the right model, the prompt design and instruction, and terminology assets. All these are important, but the single biggest factor is the clarity and context of the source content. AI quality is determined by a combination of source clarity, correct model selection, clean terminology assets, and thoughtfully designed workflow routing.
When results vary, how do you pinpoint whether the cause lies in the data, the model, or the workflow setup?
The first step isn’t guessing—it’s structured diagnostics. We isolate each part of the system and look for signature error patterns that tell us where the root cause most likely sits. In practice, we rely on a combination of pattern analysis, controlled tests, and process-of-elimination reasoning. AI variability isn’t random. Data issues look one way, model issues look another, and workflow issues have their own signature. If you test methodically, the root cause becomes obvious.
What does customization look like in practice for a client project?
Customization means we design the workflow around the client’s content, risks, and goals, rather than forcing the content into a fixed process. These are the concrete steps we take.
- Content & risk profiling: First, we classify the client’s content types—elements like UI, marketing, support, or regulatory material. We assign risk levels so each category follows an appropriate path.
- Workflow branching: Different content types may use different models, prompts, review intensities, or automation layers. A single client may have 3–5 workflow variants depending on complexity.
- Model & prompt tuning: We select and configure AI models based on domain, language pair, and performance. Prompts are tailored with terminology rules, tone guidance, and domain context. Argos uses both dynamic and static prompting models. Dynamic prompts are built by an AI model that creates a prompt based on the content being processed, but can yield less predictable results. Static prompts are consistent but not necessarily as effective.
- Terminology & reference asset alignment: We integrate the client’s TM, glossary, and style guidance directly into the workflow through retrieval, constraints, or pre/post-processing.
- Language-specific adjustments: Some markets require more guardrails, stricter terminology control, or different AI routing depending on how the model performs for that language.
- Human-in-the-loop calibration: Post-editors validate early batches and help refine the workflow, capturing the feedback that informs iteration.
- Monitoring & improvement cycles: Once in production, we track quality, edit effort, and model behavior over time and adjust the workflow as data changes or quality evolves.
We’ve found that reliability comes from tailoring the system to the content, not forcing the content into a standard template.
How do you explain that AI workflows can’t always be standardized across content types or markets?
We emphasize that AI behaves more like a variable system than a fixed engine. The most successful programs are the ones where the workflow flexes. This means using different models, prompts, and review paths depending on what’s being translated and the level of risk involved. That’s how you achieve consistency at scale: by adapting the process to the content and the market.
How do you balance automation with human quality control?
Here’s how we look at it: automation handles the predictable parts of localization, which are elements like terminology checks, formatting, and low-risk content. That frees up humans to focus on areas where judgment truly matters, such as nuance, cultural interpretation, and regulatory implications. We maintain purposeful human review for those high-risk scenarios. Our routing logic also ensures each content type receives the right intervention.
Ultimately, automation provides consistency and scale, while humans provide accountability and nuance, and the workflow is designed to deliberately balance both.
What does a customized workflow enable that a fixed process cannot?
A customized workflow allows us to design the process around the client’s specific content and risk requirements, which a fixed workflow cannot do. This allows us to tailor model selection, review depth, terminology controls, and routing rules. As a result, high-risk content gets the oversight it needs, while lower-risk content flows through a lighter, faster path. It also lets us account for language-specific differences, model behavior, and domain nuance in a way a single standardized process can’t.
Customization creates room for continuous improvement, meaning we can tune the workflow as we learn from data, incorporate feedback, and adapt to new content types or regulatory changes. It delivers more reliable outcomes because they’re built around the client’s actual quality expectations and business goals.
This approach is illustrated in a recent case study with a global equipment manufacturer that redesigned its multilingual marketing workflow using MosAIQ. By tailoring AI models, prompts, and human review to the specific risks and scale of marketing content, the team achieved higher throughput, more predictable quality, and a workflow that scaled reliably across growing volumes and languages.
What are the most common misconceptions about consistency or cost in AI localization?
The biggest misconceptions we hear are that AI will automatically make localization both cheaper and perfectly consistent. Clients think AI will save money across all kinds of content when it really depends on the domain, the languages involved, and how much human oversight is needed.
People also think AI behaves like a predictable, stable MT engine. But generative models vary more by context, input structure, and model version, and tend to look at each segment as a whole rather than within the context of say a fuzzy match suggestion, so consistency has to be engineered through workflow design, not assumed.
How do clients measure success once their workflow is tuned to their content?
Once a workflow is tuned to their content, clients look first at edit effort. Are reviewers spending less time fixing outputs, and are the corrections more predictable? They also track turnaround improvements, especially for high-volume or fast-moving content. For many teams, brand and terminology accuracy becomes a key benchmark since that’s where AI variation can be seen most.
In regulated environments, success often shows up as fewer compliance flags or SME escalations. Across the board, clients pay close attention to stability over time—whether the workflow continues to perform consistently as volumes grow, content shifts, or model versions change.
Are there examples where customization clearly improved quality, speed, or predictability?
Yes, this comes up frequently. One example is a global tech company where we introduced different models and prompts for different content types, plus language-specific review levels. This resulted in significantly improved post-editing effort and turnaround times.
In another case, a regulated healthcare client gained strong predictability by splitting their content into risk tiers: low-risk content moved through a lighter, AI-first workflow, while high-risk materials received a controlled, SME-involved process. That simple change reduced compliance-driven rework and made delivery schedules far more reliable.
When the workflow reflects the content, the quality stabilizes, the speed increases, and surprises go way down.
If you would like to know more about how customized AI workflows can support your team’s goals, contact us to start the conversation.
Argos Multilingual
14 min. read
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