RinicomAI-powered safety for rail platforms

Rinicom developed Yellowline AI to improve safety at rail platform edges. Fujitsu supported its journey to market through strategic consulting, defining the business case, shaping the go-to-market strategy and designing a scalable deployment model.

Challenges

Rinicom had developed a promising AI-driven safety solution but needed the commercial, market and sector insight required to bring Yellowline AI successfully to the UK rail market.

Solutions

Fujitsu acted as a strategic consulting partner, developing a data-backed business case, defining a targeted market-entry strategy and shaping a scalable deployment model aligned with UK rail industry requirements.

Outcomes

  • Clear commercialization and go‑to‑market strategy
  • Stronger positioning within the UK rail ecosystem
  • Foundations in place to scale Yellowline AI beyond Proof of Concept

“Fujitsu was very proactive and assigned a dedicated team to work closely with us, which gave us real confidence that they were genuinely invested in the project.”

Garik Markarian, CEO, Rinicom

About the customer

Rinicom is a UK‑based, research‑driven company specializing in wireless communications and AI‑powered visual analytics. The company develops innovative safety and security solutions, particularly for the rail sector, leveraging academic research and advanced engineering expertise.

Bringing rail safety innovation to market

Rinicom developed Yellowline AI to improve rail safety at the platform edge, using real-time computer vision and behavioral analysis to detect unsafe situations and trigger context-aware alerts. The solution showed strong technical potential and clear safety benefits in early testing, and was recognized as a winner in the UK Department for Transport’s First of a Kind (FOAK) 2025 competition.

However, moving from innovation to deployment required more than technical validation. Rinicom needed a clear business case, a focused go-to-market strategy, and a deployment model aligned with the operational and regulatory complexity of the UK rail sector. Understanding where to start and how to engage the right stakeholders was just as important.

Shaping a commercially viable proposition

Fujitsu partnered with Rinicom as a strategic advisor, helping translate a promising concept into a structured, market-ready proposition.

This included developing a robust, data-backed business case, supported by detailed financial modeling to quantify value such as avoided delay penalties, operational cost savings and reduced safety incidents. This gave Rinicom a more credible foundation for discussions with rail operators and stakeholders.

In parallel, Fujitsu carried out market and competitive analysis to identify where the solution could deliver the greatest impact. By segmenting the UK rail market and assessing risk profiles across different station types, Fujitsu helped Rinicom prioritize underserved environments based on vulnerability to incidents and the potential for immediate impact, particularly where automated intervention could add value without significant operational overhead.

Fujitsu also supported a more targeted engagement approach through stakeholder mapping, helping Rinicom better understand the rail ecosystem and position Yellowline AI accordingly.

On the technical side, Fujitsu advised on a scalable deployment model, including an AI-as-a-Service approach based on an API-first architecture. This enables easier integration with existing systems and supports future expansion.

Creating a clear path to adoption

With Fujitsu’s support, Rinicom moved forward with a clearer and more structured path to market. Yellowline AI evolved into a well-defined proposition, backed by a strong business case, a focused go-to-market strategy and a scalable deployment model.

This led to a successful MVP and Proof of Concept, while identifying realistic entry points within the UK rail network. It also strengthened Rinicom’s ability to position, introduce and scale the solution within a complex, risk-sensitive industry.

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