Rethinking Enterprise AI with Fujitsu and Cohere: Private. Powerful. Purpose-Built

Why the Vision Stalls

In most organizations, sensitive data lives behind strict firewalls, while AI models are typically trained far beyond enterprise borders. This disconnect creates real concerns. Leaders rightly worry about exposing proprietary information or infringing third-party IP. Even when those risks are addressed, grafting a new technology stack onto established CRM, ERP or service-management workflows can feel like open-heart surgery—complex, costly and disruptive. More often than not, successful technology demonstration of PoCs (proof-of-concepts) falter in the transition to real-world deployment due to practical difficulties with integration, scalability, and security.

Furthermore, GenAI amplifies existing ethical risks such as inherent biases in training data, not to mention their ability to hallucinate – which makes it easier to exploit vulnerabilities and spread misinformation or carry out targeted attacks. As a result, executives struggle to link AI investments to measurable gains in customer experience, efficiency or compliance.

Common pitfalls and how to avoid them

A clear path to addressing data and IP sovereignty is to develop customized LLMs (large language models), that exploit the the organization’s proprietary data, and hosted on private or hybrid-cloud deployments, rather than leverage the publicly hosted equivalents. Customization or fine-tuning is the process of taking a pre-trained base model and further training on a smaller, task-specific dataset to adapt it to a particular domain, use case, or organization’s unique needs. However, this results in a trade-off – private deployments require the models to have a smaller footprint, which typically results in reduced accuracy.

Manual and customized integration between fine-tuned LLMs and the existing technology stack is risky, time-consuming, and prone to future problems. Leveraging pre-built connectors makes this process less error-prone and predictable.

Security is an extremely important variable to focus on at the strategic level, much before implementation planning. Being aware of potential risks is necessary to securing your LLMs. It is fundamentally imperative to deploy anti-hallucination techniques, leverage Retrieval-Augmented-Generation (RAG) to keep the LLM grounded in its source training data, as well as implement methods that can detect, and subsequently prevent, bias, vulnerabilities, and security threats.

It may be painfully obvious, but all of this must be done in a way that doesn’t undermine the business case or worsen the ROI from deploying Generative AI. Proper project management, change management, and adequate training are necessary to avoid such common pitfalls that are inevitable in such transformative journeys.

AI that works the way your business works

Fujitsu recognized early how such critical enterprise requirements for successful GenAI implementations remained unmet, especially in global, mulitlingual, and highly regulated environments.

Take a common problem in the non-English speaking world. Almost all popular LLMs are created natively for the English language and such models typically fall short in catering to the customization and tuning requirements of enterprises outside the anglosphere. Fujitsu’s answer to this problem is Takane, our first joint model developed in partnership with Cohere. Takane is fine-tuned specifically for Japanese-language enterprise use cases. It delivers best-in-class comprehension of nuanced local terminology while retaining the core reasoning strengths of Cohere’s Command models. But Takane has value beyond Japan’s borders – it is a proof-of-concept for scalable, cost-efficient fine-tuning tailored to any language, industry, or dataset in accordance to enterprise requirements. By applying the same parameter-efficient techniques, we can replicate this approach for any language, domain or dataset—whether translating ancient land registry documents in Old English or generating audit-ready compliance reports with traceable citations.

This ability to tailor LLMs to specific enterprise contexts is only part of the equation. And the LLMs from our joint offering with Cohere are designed to precisely address the challenges covered earlier.

When deployed inside an enterprise or trusted private cloud, they keep your data where it belongs—within your own security perimeter. With a footprint small enough for private deployments, but scalable and efficient enough for high accuracy, these LLMs are purpose-built for enterprise tasks. Cohere also indemnifies clients against third-party IP claims, ensuring peace of mind for even the most regulated industries. Out-of-the-box connectors - with North (Cohere’s secure AI workspace platform) - lets you link the power of AI directly with the systems employees already use, maintaining continuity while unlocking new levels of automation, insight and personalization.

Fujitsu + Cohere: Sum greater than the parts

Enhanced with Fujitsu technologies such as Retrieval-Augmented Generation (RAG)-based anti-hallucination methods, and GraphAI, Cohere’s LLMs deliver more accurate and grounded responses, with deep contextual reasoning, improving explainability, and ensuring transparent AI-driven ROI. Knowledge Graphs capture context and relationships, not just content, creating distinct "signatures" for similar data, ensuring accuracy. This comes in handy, for example, in the manufacturing industry, GraphAI can distinguish between thousands of near-identical welding specifications—ensuring engineers get precise, reliable search results without errors.

Furthermore, complementing the solution with advanced Fujitsu bias detection and mitigation techniques, as well as LLM vulnerability scanning capabilities, enhances its trustworthiness and security. The Generative AI models from our joint offerings are ideally applicable for single domain use cases – such as banking and finance, or defense and national security – where customers in regulated industries require a private, secure, and trusted environment.

Looking Ahead

With Fujitsu’s deep industry expertise with developing secure, trusted, bias-free AI, and Cohere’s enterprise-first LLM technology, organizations can deploy generative AI that is secure by design, seamlessly integrated, and tailored to sector-specific needs. Whether you choose a fully managed SaaS API or a private-cloud installation behind your own firewall, you get the flexibility to match your infrastructure strategy—without compromising on performance or governance. Our experts can guide you through defining clear objectives, setting up data management and AI governance frameworks, and orchestrating roadmaps that align technical pilots with strategic business goals.

The promise of generative AI is real—and within reach. Let’s turn your pilot into production-grade value. Contact Fujitsu today to start your Generative AI journey with confidence and discover what the Fujitsu-Cohere partnership can unlock for your organization.