Real-time AI Translation Expands Tactical Intelligence for Defense Agency

Challenge

A defense agency must translate large volumes of open-source intelligence data into multiple languages as part of producing reports that keep partner-nation stakeholders apprised of emerging threats. 

To create these reports, analysts collect and translate intelligence from dozens of foreign-language sources. But manual processes limit the number of reports analysts can produce, slowing the flow of information to decision-makers. 

The agency initially evaluated standard on-premises AI platforms to accelerate real-time, multilingual translation. However, it quickly discovered a key gap: most platforms lacked support for specific linguistic and dialectal distinctions critical to the agency's operational environment.

This presented both cultural and operational challenges, as local teams were less likely to trust or act on information that didn't reflect native-sounding output.

Cloud-based solutions offered broader language support but were not an option due to strict security policies governing air-gapped environments. An air-gapped environment is a security measure in which a computer or network is physically isolated from unsecured networks to prevent unauthorized access and cyber threats. 

The agency needed a solution designed for sensitive environments that was capable of capturing regional linguistic nuance and fast enough to keep pace with real-time intelligence flows.

Solution

Seeking a secure, innovative approach to real-time translation, the agency partnered with WWT to develop a prototype combining speech translation, document translation and automated information retrieval.

To bring this vision to life, we assembled a cross-functional team of application developers, infrastructure engineers, UX specialists, defense experts and native-language consultants.

Our prior experience building WWT's digital human, Ellie,  gave us a running start. In fact, some of the same engineers worked on both projects, bringing deep expertise in speech modeling and system integration. 

The solution was built on a customizable translation framework and developed inside WWT's AI Proving Ground, which provided secure access to GPU infrastructure and technical expertise.

From framework to field demo: Developing a mission-ready prototype

We started with a high-performance, GPU-accelerated framework capable of automatic speech recognition (ASR) and text-to-speech (TTS). From there, we customized it to support a target regional language and adapted output quality to meet the expectations of local users.

While we began with an existing software base, making it production-ready required custom engineering.

Our application developers worked closely with infrastructure engineers to adapt the framework to fit the client's environment, resolving compatibility issues, extending capabilities and tuning performance for a live demo.

Standard voice outputs reflected a generalized dialect, so we fine-tuned the TTS voice model to reflect regional tone, cadence and pronunciation. 

Native speakers were brought into the loop for iterative testing and feedback, enabling real-time speech-to-speech translation that captured both linguistic accuracy and cultural nuance.

Beyond speech, we introduced a document translation workflow that allows users to upload open-source intelligence reports and receive translated versions within seconds.

We also developed an automated system to collect, translate and organize open-source content from preapproved sites, allowing users to ask natural-language queries like "What happened last week?" and receive timely, translated open-source intelligence summaries. 

The translation system was deployed in an air-gapped environment and optimized to run GPU-based inference locally, delivering real-time performance without cloud dependencies. This met strict data security requirements while maintaining the responsiveness expected of a live translation workflow.

The entire solution was built, tested and refined in just three weeks thanks to the rapid provisioning of high-performance compute via our AI Proving Ground, close coordination across engineering teams and iterative feedback from native-language consultants. 

A successful live demonstration was delivered securely from our U.S.-based data center to stakeholders in the region.

Results

Though still in the pilot phase, the project demonstrated the feasibility of real-time, multilingual communication inside a secure, disconnected environment. The client saw firsthand how the solution can:

• Reduce manual translation burden: A process that once took hours per report now takes minutes, freeing human translators to focus on quality assurance instead of full production.
• Scale intelligence operations: By automating data collection and translation, analysts can process exponentially more data sources, expanding the breadth and timeliness of reports delivered to decision-makers.
• Support mission-specific nuance: Unlike standard AI models, the customized system preserves local dialect and military context, making outputs more relevant and actionable.
• Operate offline and securely: All capabilities run on-premises, without requiring internet access or third-party cloud connections, meeting stringent data security requirements.

The architecture is capable of scaling to support additional languages, documents and data sources without compromising latency or control. It also opens the door for future PDF ingestion, deeper model tuning and integration with battlefield communication systems.

The defense agency now has a proven starting point for secure, accelerated multilingual intelligence that it can adapt and expand as mission needs evolve.