You asked and we answer: what is Maia and how is it changing Janus Worldwide?

In an era of digital transformation, companies seeking sustainable growth are turning to innovative solutions that streamline processes, increase productivity and deliver the highest quality of service. At Janus Worldwide, the answer to these challenges is the Maia project, an internal AI ecosystem that integrates artificial intelligence and key enterprise tools into a single entity.

What is Maia?

Maia is a multi-layered ecosystem that integrates AI tools into the company’s production, administrative and communication processes. It is based on the idea of creating a connected digital space where all data, services and interactions are brought together and aimed at supporting employees, improving customer experience and automating routine tasks.

The first stage of the project has already been implemented: solutions such as MemoQ integration with AI, RFI assistant for the sales team and Xbench-analysis-assistant for the QA department have been connected.

Architecture and key ecosystem components

Maia is of modular build, where each unit performs separate functions, yet they are all connected by a common infrastructure.

1. Ecosystem Database

The central storage and analytics core of the ecosystem:

• Stores all incoming and outgoing data, including users, instructions, languages, and models
• Maintains detailed logs through APIs and the user interface
• Provides administrative control and customization

2. Web Interface

The interface for users to interact with Maia provides:

• Assistants for RFI, Xbench, and ChatGPT
• Universal access to all AI functions
• Ease of use and intuitive task management

3. CAT integration

Integration with memoQ allows:

• Automation of TEP processes
• Increased machine translation quality
• Improved editing with AI

4. Mail Server Integration

Maia processes and analyzes emails:

• Automated responses and feedback analysis
• Accounts receivable management
• Support for freelancers and candidates

5. MT Hub

The module for pre-training and combined use of machine translation and AI allows:

• Improved MT quality
• Creation of thematic data sets

6. AI Collection

The AI models library includes:

• Basic models to fine-tuned solutions for specific clients
• Vector bases for working with TMs and glossaries

We got in touch with CTO Valery Bolshakov, who is leading the development of Maia, to find out what challenges the team faces in its work.

MAIA acts as a proxy service that connects memoQ (a leading translation management system) to OpenAI’s large language models (LLMs). It is important to understand the complexity and innovation behind this solution. While the end goal seems simple – using AI to translate content more efficiently – the development process itself is far from easy. Here are the key challenges our team faces.

1. Different worlds: memoQ and OpenAI
memoQ is a structured translation platform built for industry professionals and rigorous workflows. It handles documents, segments, term bases and detailed formatting rules, while OpenAI models are general-purpose AI tools designed to handle natural language in a flexible and creative manner. Deep technical development and a well-thought-out architecture are required for these two systems to be able to “talk” to each other seamlessly. It’s not just about connecting APIs. It’s about interpreting, adapting and sometimes reimagining the exchange of information between two very different ecosystems.

2. Content and quality management
memoQ handles content as segments, breaking text into small manageable chunks. However, LLMs handle long, cohesive pieces of text better. Sending segments one at a time results in poor quality translation. But combining segments carries different risks: AI might mix up context or insert unnecessary information. MAIA developers ate implementing intelligent grouping logic that balances context and accuracy to ensure high quality translation without breaking up the structure.

3. Result management
Another challenge is ensuring consistency and adherence to translation standards. Translators rely on accurate terminology and formatting (e.g. tags), and style. Unlike traditional machine translation systems, LLMs are flexible and may “hallucinate” and add words or formatting not present in the original. MAIA implements AI control mechanisms with specialized prompts, terminology integration and post-processing to ensure that the output meets professional requirements.

4. Performance, cost and compliance
There are practical issues associated with the use of LLM. Every OpenAI API call uses tokens and costs money. To keep MAIA efficient and cost-effective, we are implementing smart bundling, usage tracking and optimization strategies. In addition, regulatory compliance is critical. As translations often involve sensitive data, MAIA is built with data protection and GDPR compliance in mind, ensuring that no data is stored or compromised.

5. Seamless integration into workflows
Finally, from a user perspective, everything should “just work”. But behind the scenes, MAIA manages project files, language pairs, segment metadata and various memoQ project types. It handles bugs, automatically repeats requests when necessary, and provides real-time feedback, all in the name of seamless collaboration for translators and project managers.

Using Maia in everyday work

Maia is used extensively in various departments across the company. The following are the key tasks Maia is used for.

• Production & QA: automatic file checking, labor cost estimation, and sorting projects by language
• Sales: writing PM and marketing messages, and RFI requests
• IT: technical updates, risk management; and report automation

Project team and support

A multifunctional team has been formed to fully implement Maia.

• Developers (PHP, Python, C# and front-end) create the architecture and connect external tools
• QA and testers ensure the quality and stability of the system
• Implementation specialists and prompt engineers train employees and create effective instructions
• Project managers coordinate the development and scaling of the project

A look to the future

The Maia project is developing according to a phased Roadmap, and is covering more and more of the company’s processes. Next steps include expanding assistants, rollout to sales and HR, and the creation of a fully functional AI Hub to train models based on internal data.

Maia is not just a technology platform, but a key driver of Janus Worldwide’s evolution. Through the smart integration of AI and enterprise tools, the company is gaining a powerful lever to increase efficiency, improve service quality, and strengthen its competitive position in the global marketplace.