How KDDI Made Buffmee a Reliable AI Agent in Weeks

In September 2026, KDDI, one of Japan's major telecommunications operators, launched Buffmee, a generative AI application built around the idea of a companion that helps users grow. The team chose to ground every response in over one hundred proprietary sources, including books, magazines, and web content, always citing the origin of the information.

The technical team faced a challenge common to those designing RAG systems for consumer audiences: balancing semantic quality and response speed across a large, heterogeneous content library. The final result reveals how a process decision, even before a technological choice, can determine the fate of a consumer launch.

The Context: A Case That Breaks From the American Playbook

Most AI enterprise deployment cases covered in international media come from U.S. companies. Buffmee shows a different pattern, rooted in the Japanese market, where KDDI operates as the leading carrier serving millions of consumer users.

The project confirms how AI enterprise results outside the United States follow their own logic, adapted to local context rather than replicating an imported framework. For those evaluating market benchmarks at board level, this geographic detail carries weight equal to the metric itself.

The Original Idea: Growing With Verified Content

Buffmee stems from a precise bet: by citing sources exclusively, a personal learning app can earn the trust of both publishers and users. The team ingested a heterogeneous library, composed of books, magazines, and web material, transforming it into a knowledge base queryable in real time.

The stated objective was to allow every piece of content, as soon as it was uploaded, to function immediately as an operational RAG system. Shunya Onoda, from KDDI's AI Product department, described this vision as the heart of the project, emphasizing the role of technical support from Google Cloud in making it real.

The Problem: Quality Against Speed

During development, engineers encountered two parallel obstacles. The first concerned latency: the system struggled to meet target response times, a critical factor for a consumer application where waiting discourages users.

The second concerned semantic reliability: with hundreds of heterogeneous sources, the risk of hallucinations grew proportionally to the variety of ingested content. Solving only one of the two problems would have compromised the other: accelerating generation could worsen precision, while refining precision risked extending response times.

This type of technical conflict recurs in almost every consumer RAG application relying on proprietary, diversified content libraries, from editorial texts to multimedia materials.

How They Built Automated Evaluation

Manual testing, though rigorous, shows a structural limit: it scales linearly with content volume, and a library of this scale makes it impractical. The team therefore designed a systematic evaluation process based on Gemini Enterprise Agent Platform Evaluation Service.

Using techniques like LLM-as-a-Judge and the method known as Rule of Hundreds, engineers built hundreds of automated test cases from the existing document corpus. This approach replaced artisanal testing with a repeatable process, capable of identifying performance bottlenecks in real time, section by section of the RAG pipeline.

The decision to automate evaluation, before even optimizing the language model, represented the true architectural decision of the project.

The Verified Results

The numbers tell a targeted and specific optimization story. According to Google Cloud^[1], KDDI reduced total application latency by 38%, reaching the performance target set in the design phase. Time-to-first-token, the metric measuring how long users wait before seeing the first portion of a response, improved by nearly 18%.

• Total application latency reduction: 38%
• Time-to-first-token improvement: approximately 18%
• Grounding base: over 100 proprietary sources, including books, magazines, and web content

These two numbers, together, define a credible case: they have an explicit denominator, a declared time horizon, and a citable primary source.

The Point of Friction

The central correction in this story concerns the evaluation approach itself. The team had initially focused on extensive manual testing, a familiar method for those working on complex editorial content.

The scale of the library made that approach unsustainable within launch timelines. The shift away from manual testing toward an automated framework constitutes, in fact, a course correction rather than a failure: the willingness to change method in the face of evidence is a sign of operational maturity, never weakness.

What We Can Take Away

For a CTO designing a RAG application with multiple proprietary sources, the Buffmee case offers a concrete playbook. First: automated evaluation scales where manual testing stops, and it's an investment to make before launch, never after.

Second: latency and semantic quality must be measured together, using methodologies that isolate each component's contribution to the pipeline. Third: citing sources, beyond building trust with end users, also offers an internal verification mechanism useful during development.

For those viewing the project from a budget angle, the message is equally direct: investing in an automated evaluation framework before launch costs less than chasing latency problems after public release. Those working on similar products can replicate this framework by simply adapting the evaluation framework's scale to their own content dimensions.

The Open Question

The KDDI case demonstrates how architectural decision, before language model selection, determines the fate of a large-scale consumer deployment. Should your organization be building a reliable AI agent in a matter of weeks, the question to ask remains the one KDDI faced: does the evaluation system scale alongside content, or does it become the bottleneck that slows everything else?