Can Custom-Built GPT Solutions for Translation Remain Sustainable Over Time?

An increasing number of companies are developing their own GPT solutions that can write and translate in their brand’s tone of voice. It’s a smart way to explore how AI can support content creation and streamline workflows.

These internal solutions often produce quick and impressive results. For straightforward content and internal communication, they perform exceptionally well, offering speed, accessibility, and ease of use.

The next step is to harness this potential for the long term. To truly build on the results, companies need structures that can learn, evolve, and scale globally.

Build the Foundation for Successful AI Work

As AI becomes more widely used within the organization for translation and content creation, the demand for common standards and well-defined processes increases.

Otherwise, it can easily lead to:

• Fragmented workflows. Much of the work is done manually because the systems are not integrated or automated.

• Inconsistent language use. The results vary depending on who writes the prompt and how the task is formulated.

• Manual quality assurance. Ensuring translation accuracy and tone often requires extra time and effort from internal teams.

• Limited reusability. Previous translations, terminology, and other language data is not stored in a way that makes them easy to build on.

• Unclear data management practices. Many in-house solutions lack well-defined policies for storing and safeguarding information, leading to uncertainty and potential risk.

When systems aren’t integrated and much of the work is done manually, you lose momentum and the ability to build on what already works. The next step is therefore to create connected structures where AI, technology, and people collaborate within the same workflow.

From AI Experiments to a Scalable Ecosystem

The real value emerges when AI becomes a natural part of the organization’s language workflows, where technology, processes, and people work together.

In this kind of ecosystem:

• Systems such as CMS, DAM, and translation tools are connected to reduce manual work and create secure, efficient workflows.

• Language data is continuously maintained and leveraged to refine models aligned with the brand’s language, resulting in improved output and lower costs.

• Workflows are customized for different content types and markets to ensrue the right balance between speed, cost, and quality.

The result is a scalable and secure way of working that delivers both speed and consistency, where AI truly creates business value.

The Right Approach for the Right Content

As organizations scale their use of AI for translation, it becomes essential to adapt workflows to the type of content, risk level, and market. Different kinds of content demand different approaches to achieve the right balance of efficiency and quality demand.

For example:

• Regulated or rule-based texts are handled efficiently with neural machine translation (NMT) and human expertise to ensure accuracy and consistent terminology.
• For more creative and brand-defining content, agentic AI workflows connected to language data are used. They operate in multiple steps based on defined requirements, with specialists involved when needed to ensure relevance, voice, and impact.

This structured approach ensures that every type of content achieves the right balance between speed, cost, and quality, and that AI contributes real value where it matters most.

From Fragmented to Connected

When translation workflows are connected, work becomes not only faster but also more consistent, secure, and cost-efficient.

Early AI initiatives plays an important role in this, but it’s not the whole solution.

Real value emerges only when technology is integrated with language data, human expertise, and clear processes that allow companies to fully realize the potential of their AI initiatives.

At Comactiva, we help companies take the next step, from early AI initiatives to language workflows that generate real business value.

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