Golden Thread building safety platform
In early 2022, a London Borough of Camden faced the challenge of issuing Secure Building Safety Assurance Certificates for high-rise buildings, in line with upcoming building safety regulations. Recognising the complexity of this task, they required a centralised data platform across their housing system databases to achieve not just one-off compliance, but to establish a ‘Golden Thread’ building safety case platform for ongoing housing needs fulfilment.
Aten has been collaborating closely with Camden, leveraging our extensive product and data expertise to craft a solution that serves the greater good. Our focus is on helping construct a platform that benefits a wide array of stakeholders. This includes local residents, contractors, suppliers, emergency services, landlords, and various housing management sectors, along with regulatory bodies. Access to this centralised platform will ensure unparalleled transparency, enhanced efficiency, and strict compliance, aligning with the diverse needs and expectations of all parties involved.
Our commitment is to deliver an inclusive, accessible, and effective solution that stands as a testament to our dedication to community-focused innovation and excellence.
Creating a climate intelligence risk proposition
Aten Consult provided product and data services to Cervest for their new climate intelligence risk platform feature set. This engagement had a particular focus on big data for buildings at risk to climate hazards, such as extreme heat, strong wind and flooding. We worked with the Cervest team to identify the critical climate data points which led to the creation of minimum viable product, prototyping and user research.
Our discovery phase used the double diamond framework to understand organisation objectives, financial and competitor landscapes. We then ran workshops to identify problems worth solving and talked to stakeholders and prospects to discover the user’s needs. Further sessions identified primary use cases (using user story mapping) which were prioritised to build out high-level epics along with the product backlog.
Aten worked with data scientists and extreme weather data modellers to support the development of data models for the climate value at risk metric. The data model was developed by a multidisciplinary team and led by an Aten product consultant. The prototypes included a climate ratings data pipeline based on live building asset data with an assets database mapped to financial securities.
Delivering a sports financial intelligence platform
In early Jan 2019 ClubView had a mission – to make financial intelligence accessible to sports clubs of all sizes. They wanted to be ground-breaking in their use of real-time data and technology, enabling sports organisations to significantly improve their financial stability. Aten Consult led the product development for ClubView’s financial intelligence platform using a user centred design approach.
Aten started by understanding the company objectives and reviewed the football finance landscape along with any potential competitors. Subsequent workshops identified problems worth solving and interviews with football stakeholders were carried out to discover their needs. A technique called user story mapping enabled us to identify the primary use cases and high-level business requirements in a matter of days rather than weeks. These use cases were prioritised and translated into high-level epics and the start of a product backlog in Jira.
The Aten product team used standard rituals that included daily stand-ups, iteration planning, reviews (show and tells) and retrospectives to ensure a smooth software delivery.
The consultancy team managed to build an MVP within 6 months from conception stage to shippable product. The completed FinTech SaaS based product was sold to its first customer within 2 years.
Crafting a retailer bot using natural language
Aten worked with social media and tech agency Beamly to produce a chat bot using natural language processing for global retailer Coty.
The bot was designed to help consumers pick a fragrance, providing personal recommendations to guide their choice
Our area of focus was to develop the scripted flow – similar to a quiz – that shoppers could follow. It was part of a built-in design that learnt through interactions, creating data and authority on fragrance and user preferences the more it was used by shoppers.
The bot had the facility to be inframed into a retailer website, to be tailored for individual retailer branding and had inbuilt behavioural analysis all the way through to shopping basket.
The bot was tested among 7.5k users, and conversation enhancements were built into future iterations.