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When AI strategy starts with people, not products, organizations retain ownership of the value AI creates

Aimbition supports Saarni Cloud, a SaaS and cloud software company serving nearly half a million users across 1,500 organisations, in building AI capability that remains under its own control.

Rather than buying a ready-made AI package or outsourcing a critical capability, Saarni Cloud is developing the skills, infrastructure, and operating model needed to use AI on its own terms. The collaboration has helped Saarni Cloud move from fragmented experimentation toward a clearer shared direction, stronger internal competence, and a sovereign technical foundation for future AI development.

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Starting point

When Saarni Cloud began evaluating AI solutions, the market offered plenty of options. Many were black-box, subscription-based products where AI capability sat largely with an external vendor. While these tools offered speed and convenience, Saarni Cloud’s technology team recognised the importance of control, resilience, and negotiating power. What happens if a vendor changes direction, raises prices, or becomes less reliable? How much flexibility remains when a core AI-driven process depends on someone else’s platform?

At the same time, AI adoption across the organisation had been fragmented and individually driven. Different teams were experimenting in their own ways, creating momentum but also making alignment harder and limiting cross-team learning. Saarni Cloud saw a chance to move from scattered experimentation toward a more coordinated, capability-building approach.

The group’s position became clear quickly. For a technology-driven company, one of the most important capabilities is knowing how to build software with AI. Developing that knowledge internally strengthens ownership of processes, cost structures, and competitive position. Sovereignty at the core of agentic processes was not just a preference. It was a strategic requirement.

Solution

Before Saarni Cloud could make bigger decisions about AI, its teams needed a shared understanding of what AI means in day-to-day work. Aimbition began with training for key sales and technology teams, helping them build the confidence to talk about AI, assess where it could help, and apply it in practice.

The impact showed up quickly. Customer conversations around AI became sharper, and internal decisions could be made with greater clarity. Instead of treating AI as a collection of disconnected tools, Saarni Cloud began developing a shared language and a more deliberate path for adoption.

From there, the collaboration moved into advisory support and technology stack design. Saarni Cloud had already secured private GPU capacity, and Aimbition’s role was to help turn that capacity into a usable software engineering foundation. Working with Saarni Cloud’s technology teams, Aimbition helped define the stack for deploying GPUs efficiently, including model services, monitoring, and integrations designed to support internal AI development without creating dependency on a single vendor.

The agentic workflow platform is now being developed and validated step by step, with Nepton acting as the proving ground. This phased approach supports both technical quality and developer experience: the goal is not to force a sudden organisation-wide shift, but to build confidence, improve everyday engineering workflows, and scale what works.

Early technical validation has already shown strong promise. Shortly before the holiday period, Saarni Cloud combined a vector database, reranker, and an agent-based second-stage filtering approach against a complex codebase. In week-long testing, the setup returned highly relevant results in hundreds of milliseconds, with the most useful findings typically appearing within the top results after reranking. For the development teams, this was an important signal that the chosen architecture can support fast, practical, and accurate AI-assisted software work.

Rather than outsourcing a critical capability, Saarni Cloud is building the knowledge, infrastructure, and operating model it needs to keep developing AI on its own sovereign terms.

Results

Saarni Cloud has moved from fragmented, individually driven AI experimentation toward a structured and sovereign approach to AI adoption. The organisation now has a clearer direction, stronger internal competence, and a technical foundation that gives it more autonomy over how AI is developed and used.

The rollout is intentionally phased. Nepton is being used as the proving ground for agentic workflows, allowing Saarni Cloud to validate the platform in real engineering contexts before scaling it more broadly. This gives the teams room to learn, improve developer experience, and build adoption in a way that supports the people doing the work.

The business impact was visible early. By investing in its own GPU infrastructure, Saarni Cloud created a more sustainable foundation for inference costs and AI tokenomics. The architecture also gives the company greater flexibility: core AI-enabled workflows do not need to depend entirely on a single external vendor’s roadmap, pricing, or reliability.

The most important result is strategic autonomy. Saarni Cloud now has the direction, competence, and infrastructure needed to keep building AI capability internally. The value created by AI can stay inside the organisation, not just in the tools it buys.

Technologies used

 

  • Temporal.io
  • Nvidia H200
  • LiteLLM

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Kalle

Kalle Mäkelä


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