Future Labs

London North Eastern Railway, Transpennine Express, Northern Trains, and Southeastern joined forces to launch Future Labs, the UK’s first collaborative rail programme dedicated to driving innovation within their operations and across the broader industry.

Future Labs is a first-of-its-kind collaboration between four leading UK rail operators – London North Eastern Railway, TransPennine Express, Northern Trains and Southeastern – to drive innovation and address key industry challenges. Designed by  L Marks, the programme explored new technologies in the rail industry and provided startups and scaleups with a unique opportunity to test their ideas and products across all four operators.

The categories selected for Future Labs were:

• Performance & Operational Excellence: Focused on discovering solutions to streamline communication, enhance information sharing, support predictive resource planning, leverage real-time data for decision-making, and drive overall performance improvements.
• Enhancing Customer Experience: Dedicated to identifying innovative solutions that deliver real-time disruption updates, improve accessibility for passengers with hidden disabilities, enhance travel safety, and allow customers to access critical information through their preferred communication channels.
• Developing People and Talent: Centered on uncovering solutions to attract and retain top talent, analyse workforce trends, forecast future needs, identify skill gaps, and enhance talent development, staff engagement, wellness, and onboarding processes.
• Wildcard: Designed to explore unexpected and groundbreaking technologies or approaches.

Over 489 startups were scouted worldwide and 107 finalised their application to the programme. A total of 17 suppliers were invited to Pitch Day in York in August 2024, where 9 were ultimately selected for the 12-week programme.

The selected suppliers spanned across the defined categories, including 4 teams in Performance & Operational Excellence, 2 in Enhancing Customer Experience, 2 in Developing People and Talent, and 1 in the Wildcard category.

Each supplier was paired with sponsors and mentors from leads and supporting TOCs to refine and develop their use cases during the Lab. The teams worked alongside the mentors and showcased their work at the Expo event on the 5th of December. After Expo, all the teams had exploratory calls with the four TOCs, where they explored what the next steps would look like and are still in conversation with the TOCs.

We’re here [Future Labs Expo] to celebrate the innovative spirit and collaborative efforts that have driven this programme to, quite frankly, new heights – Ross Welham, Lead Digital Research & Innovation Manager at LNER

STARTUPS

Chata.ai

Chata.ai is revolutionising data decentralisation by enabling organisations to harness their data through self-service analytics. Through unique offerings like data alerts and a data messenger, non-technical business users can access real-time insights simply by asking questions in natural language. Supporting over 100 languages and seamlessly integrating with popular tools such as Microsoft Teams and Excel, the platform ensures wide-ranging and inclusive user engagement

As part of a 12-week Innovation Lab, Chata.ai collaborated closely with their mentor to design and trial a self-serve data alert system. The objective was to improve performance and streamline operational efficiencies by enabling Performance Teams to access critical data without reliance on manual processes.

By addressing key pain points, the system was designed to reduce the hours teams spent on reporting, optimise resource allocation, and ultimately enhance customer satisfaction.

In the first few weeks of the Programme, the Chata.ai team conducted interviews with end users from LNER to gain a deeper understanding of the needs and challenges their solution needed to address. These included tracking passenger numbers to analyse customer volumes, improving staff planning to ensure optimal resource allocation, and responding proactively to customer assistance requests.

While Chata.ai’s solution proved promising to both LNER and Northern, further discussions are yet to take place.

1Huddle

1Huddle is a coaching and development platform designed to enhance workforce training through quick-burst mobile games. Organisations also have the option to create personalised content, shifting training from a one-time onboarding process to an engaging, continuous development tool that keeps employees motivated and skilled.

1Huddle worked with Southeastern to build a bespoke suit of gamified learning content aimed at engaging colleagues, benchmarking and assessing knowledge/skill gaps. The first phase focused on designing quiz-like games based on Southeastern’s learning content and branding the platform to align with Southeastern’s identity. Content included policies on grievance, recruitment and interviewing skills as well as general knowledge quizzes to drive engagement.

Initial efforts also focused on establishing a benchmark of employee knowledge via baseline contests, with participants playing games designed to measure and improve their understanding. The contests incorporated leaderboards and non-monetary rewards, such as personalised song choices, which fostered healthy competition.

The solution achieved high levels of engagement, with 97.3% of users who downloaded the app playing at least one game. In the first round, knowledge scores improved significantly in key areas, such as grievances, which saw a 37% boost. Across the Programme, employees played an average of 14.1 games per week, spending over 40 minutes weekly on the platform. Feedback highlighted the platform’s ease of use, its ability to identify learning gaps, and its motivational design. Contest winners appreciated selecting music as a reward, and participants found the games to be both engaging and effective in reinforcing knowledge.

As part of the trial, 1Huddle introduced its Manager Certification programme to demonstrate how easily games could be created for teams, further empowering Southeastern to expand training efforts internally. Metrics indicate substantial engagement, with 60% of registered users completing all games and 91% exploring both company-specific and general development content.

While the trial delivered positive results, it was felt that the solution wasn’t unique enough to offer value to LNER, TPE and Northern. 1Huddle is creating a proposal for Southeastern and they will have further discussions in the near future.

Cleancore Intelligence

Cleancore Intelligence is a 100% digital automated IoT solution for tracking cleaning activity and building footfall to direct cleaning resources effectively. This system allows building managers to save time, money and resources by only cleaning when and where it is needed.

Cleancore worked closely with their Southeastern mentors to develop a solution focused on cleaning station touchpoints using NFC tags which could enable them to hit Service Quality Regime (SQR) audit targets by ensuring station cleanliness.

The objective was to provide real-time alerts and monitoring for key areas within stations that required regular and thorough cleaning. It was agreed that the tags would primarily use NFC touch technology, with QR codes as a backup. To ensure the system met the needs of all stakeholders, Cleancore began building a framework incorporating the perspectives of a working group of Station Managers and Mentors.

The system aimed to improve visibility and efficiency in station cleaning by allowing station managers to track cleaning activities in key areas. Instead of relying on a whiteboard that was wiped weekly, Managers could access historical cleaning records digitally. Cleaning staff could also use the app to log larger issues such as graffiti, structural damage, smashed windows, or damaged posters. Additionally, SQR teams at HQ gained oversight of cleaning activities, enabling them to take proactive measures to prevent SQR failures.

Following Expo, additional use cases were reviewed by the TOCs, and no immediate steps are planned.

iqast

iqast is a software solution provider offering the latest research in AI/ML for fully automatic logistics forecasting.

Taking advantage of the flexibility of the solution, Iqast worked closely with their TPE and Southeastern mentors to explore a variety of use cases. After discussing the options, they decided to focus on two main areas: Revenue Management for Southeastern and Strategic Workforce Planning for TPE.

The success of both use cases lay in their ability to provide forecasts that supported critical decision-making processes. The demand forecasting for Revenue Management showed impressive accuracy with fewer than 6% errors, while the unavailability forecasts allowed TPE to refine their workforce planning strategies. These efforts in data ingestion, predictive modelling, and real-world validation set a strong foundation for using AI-driven insights in operational and strategic decision-making.

Following the programme, Iqast have submitted a proposal for further work to Southeastern. This proposal is currently being reviewed internally with the aim of starting work before the end of the financial year and will be discussed in the near future.

TPE are also exploring options internally and have appetite to continue working with Iqast to further develop the Strategic Workforce planning piece.

Cognition24

Cognition24 offers data analytics, machine learning, and process automation services. They create solutions to meet clients’ unique needs, working closely with them to address specific challenges and goals.

For the Lab, Cognition24 was tasked with developing the initial designs for ‘Rail Pal,’ a self-service and wellbeing travel tool primarily aimed at neurodivergent customers but accessible to all passengers. The goal was to showcase the potential of the solution by the end of the 12-week Programme, with a view to progressing to a full MVP post-lab.

Focusing on the station experience, the team set out to validate the need for the solution through research, interviews and observations. They worked on conceptualising the architecture and designing wireframes to illustrate its future potential. To kick-start this, Michael from Cognition24 conducted an observation and research trip, travelling on several services to and from York and making further journeys across the network to test the hypothesis himself. He also observed station environments, providing valuable insights that helped shape the overall design. Alongside this, the team carried out in-depth interviews with TOC colleagues with lived experience, and mentors played a crucial role in facilitating these online discussions.

With the need for the solution confirmed, Cognition24 collaborated closely with Salesforce from the Midpoint onwards, refining the Rail Pal design and providing weekly progress updates to mentors. By Expo, they exceeded expectations, moving beyond architectural designs to showcase a working demo. For this, they created a fictional persona with autism, chronic fatigue, and impaired vision, demonstrating her journey from Peterborough to King’s Cross. They demonstrated how Rail Pal could identify busy services and station areas and provide alternative route options tailored to her needs.

Building on the potential showcased by Cognition24, LNER are to fund an in-station project with Cognition24 that will include other participating TOCs.

Zing Data

Provider of a platform offering visual data analytics solutions. It provides a business intelligence platform that offers a visual query builder that lets users get answers by analysing data from their phone or browser to work from anywhere without the need for a desktop, SQL, or data scientist.

Zing Data partnered with Northern to improve insight and predictability in train crew scheduling and optimising maintenance depot utilisation, intending to show potential in reducing costs, improving resource efficiency, and enhancing both customer and employee satisfaction. Zing used Northern’s Snowflake data lake to serve as a foundation for building predictive models tailored to Northern’s operational needs. Zing utilised advanced AI techniques to develop regression models, aiming to achieve near-perfect accuracy (r² = 1), demonstrating the potential for these solutions to significantly enhance decision-making in key areas.  The Zing team also implemented a  ‘What if’ analysis which provided clear insights into how changes in factors like sickness, strikes, and seasonal trends could impact crew availability, helping with proactive decision-making. Zing also created a customised dashboard for depot managers, allowing them to easily explore and interact with operational data. The dashboard was designed with usability in mind and incorporated generative AI functionality, enabling managers to manipulate data and gain answers to complex operational questions in real time.

Zing’s solution proved promising to both LNER and Northern, however further discussions are yet to take place.

Treeva

Treeva provides highly efficient renewable energy from the turbulent airflow of passing transport. The modular design is easy to install and maintain on the side of roads/railways, makes use of readily available land and powers infrastructure, such as train lines and EV charging stations, to create net-zero transport systems.

Treeva was selected for the Lab due to its potential to harness renewable energy for rail infrastructure, such as signalling systems and trackside lighting. This innovation could reduce reliance on traditional grid power and support environmental targets in the future. As such It aligns with rail industry priorities by contributing to Net Zero targets through independent power generation and reduced dependence on the grid.

Over the 12-week Programme, the key goals were to develop a comprehensive feasibility study on emission reduction, explore the installation of Treeva turbines in a live rail environment and obtain trial certification for further deployment. With the aim of potentially installing a custom-made turbine on the network within the 12-week timeframe, the project was recognised as ambitious from the outset. With a mentor team comprising of Sustainability and Operational Leads from all four TOCs, along with valuable input from Network Rail, big strides were made towards this goal. As a reflection of the collaborative efforts during the programme, Treeva won the People’s Choice award at Expo.

The trial will continue with the overarching goal of turbine installation.

PotentialU

​​PotentialU is a revolutionary platform that combines nearly 80 years of insights from human psychology and psychometric testing with the latest GenAI coaching technology.

After internal discussions around the complexities of involving TOC frontline staff due to union involvement, PotentialU worked on a tool to provide more in-depth development for Managers than the Insights® Discovery tool currently offers.

As the Behavioural Framework for LNER is set to be updated in March 2025, it was also agreed that the solution would link to the TOC’s respective values. For the pilot, the aim was to have 3 or 4 teams, each consisting of one Manager and their direct report from the 3 participating TOCs trialling the solution, which for ease would be branded for LNER. For the initial app, mentors gave positive feedback on the intuitive design, the positive language, and its alignment with the brand values of all TOCs, despite the design being LNER-focused for the purposes of the trial.

On Week 6 PotentialU travelled to the UK to deliver a workshop in York with the aim of testing and refining the model they had created to date. They also interviewed participants to gain further insights into their requirements and needs and shadowed frontline staff to gain a better understanding of their roles. Subsequently, they conducted a pilot of the solution, creating celebrity personas for participants to engage with Tobi, PotentialU’s AI coaching tool.

The value metrics for the pilot were based on feedback, usage, and engagement, all of which PotentialU exceeded. Users responded positively, expressing appreciation for the personalised support and development opportunities through direct feedback and before-and-after surveys. The platform maintained a Weekly Active User (WAU) rate of 23% indicating solid engagement and consistent user interaction with 85% of users regularly interacting with Tobi. Additionally, 27 participants achieved a 75% activation rate, highlighting strong engagement throughout the pilot.

As a testament to their hard work, PotentialU was presented with the award for ‘Most Collaborative’ at Expo which was based on feedback from mentors and TOC Innovation Leads on how they proactively worked with their mentors and how flexible they were in agreeing a use case.

Following on from the Lab, PotentialU will continue working directly with LNER on a potential MVP.

Moonbility

Moonbility is an Innovate-UK-funded digital twin company, specialising in predicting and simulating asset failures in the transport sector. The core of Moonbility’s offering lies in their pioneering auto-healing algorithm. This AI-powered technology continuously identifies missing or inaccurate data and automatically corrects errors, ensuring that the digital twin remains perpetually updated.

Moonbility aimed to address the need to give passengers better information to plan journeys in the event of disruptions. They did this by tackling the challenge of presenting consolidated, refined, and prioritised quantitative and qualitative data to Customer Operations staff. This would allow for better articulation and prediction of disruptions across all communication channels to customers.

Moonbility were chosen for the Lab as their solution is able to sit above Darwen (the national database of disruptions ) and Tryell system which increases identification and the accuracy of disruptions. This could automate the data, reducing the need for manual processes. The overall goal was to ensure increased quality and consistency of information, which in turn would lead to more passenger trust and improved customer satisfaction.  During the 12-week Lab, the Moonbility team worked closely with their LNER mentors to gain access to Darwin and LNER Tyrell systems, analyse Tyrell data to incorporate it into Moonbility AI solution, and develop both a user interface and a dashboard for end-users. Within this their AI solution predicted delays and propagation of disruptions across customer journeys.

Alongside this, they were tasked with providing qualitative data summarising the root causes and likely outcomes of early-stage disruptions. This was to be used by Customer Operational Control teams and Network Rail, with translations available in 90 languages. At the moment, only 11% of LNER disruptions are known by LNER customers. In order to increase this metric, Moonbility worked toward demonstrating that their solution could raise it to 70%. They trailed this on the network for the Durham – Edinburgh line to produce a prediction more accurate than Darwen

Feedback on their solution was very positive, with LNER seeing the value of Moonbility’s solution. LNER decided to devise a further scope of work to begin after the Lab.