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30 Most Reputable Companies of the Year 2022

The Saas Technology Company Empowering People to Live Independently, Happily and Securely at Home for Longer: Intelligent Lilli

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"We aim to reduce strain on informal carers and health and social care providers by providing a new form of proactive health monitoring technology that enables better delivery of care."

Intelligent Lilli is a UK-based SaaS company that leverages machine learning technology to help individuals live independently, safely and happily within their homes. Using sensors around the home to build up a data picture, the company enables healthcare practitioners, families, or carers to support individuals with self-limiting health conditions to maintain their independence and remain at home for longer.

Intelligent Lilli was founded in 2014 and is headquartered in Woking, Surrey.

Gren Paull, CEO of Intelligent Lilli, spoke to The Silicon Review about how his company is utilizing technology to revolutionize the home care sector. Below is an excerpt.

Q. Where does Intelligent Lilli stand at the moment relative to other participants in the industry?

We are focused on serving the people who require care and the services that provide it. For instance, in the UK, the number of people living with self-limiting conditions is increasing, and this is placing an even greater strain on national health and social care resources. The requirement for round-the-clock monitoring for people who need care, combined with the significant shortages of staff across the sector, is a huge challenge. Caring for an aging population will only become more demanding over the next decade.

How we differ from our competitors is in our goal to lead the shift in the care technology market from a reactive needs-based service that currently exists in the form of alarm-based technologies to one where we can actively predict and prevent incidents that derive from a decline in health and reduce the strain that places our health and care services at risk.

For example, for elderly people, common incidents in the home such as slips, trips, and falls usually result in emergency services visits. However, these incidents are often the result of a decline in their well-being, resulting in an accident. Our solution identifies patterns and highlights any risks in deterioration before such incidents occur. Through our preventative machine learning approach, we have developed a platform that can learn the behaviors of an individual household through real-time data. This is generated using a range of sensors placed around the home which track factors such as movement, temperature, independence and eating and drinking patterns.

Q. How does Intelligent Lilli empower people to live independently at home?

We support people to live safely, independently, and happily in their own homes regardless of their age, condition, or financial situation. In doing so, we aim to reduce strain on informal carers and health and social care providers by providing a choice of care alternatives for those who want it. 

We do this through our smart and non-intrusive technology that monitors and learns behaviors within the home. The data captured then builds a profile of a person’s typical habits and routines. Using ML and AI, we can then identify each service user’s regular patterns and trends and generate useful insights and analysis highlighting a person’s progress, deterioration, or any causes of concern. Identifying this early can be addressed proactively before a condition escalates. Not only does this support carers in delivering better levels of preventative care, but it also helps highlight any risks earlier, reducing the potential of hospital visits or residential care, thereby also reducing physical and emotional trauma and costs. This allows them to choose to have the freedom to remain independent in their home for longer. Similarly, it also provides peace of mind to carers or loved ones when they are not there and helps them form an accurate understanding of their state and ability to look after themselves.

Q. What can you tell us about your technology platform in place? And how unique is it?

The Lilli platform is simple to set up and involves a set of discrete, unobtrusive sensors placed in the service users’ homes to monitor the patterns of daily life. The ML behind the sensors analyses vast amounts of data and extracts insights specific to an individual and their condition, significantly reducing false positives. The information is easy-to-understand for service providers and caregivers and interoperable with health systems to assist with integrating and streamlining cross-organizational care pathways. Service-users can, for example, be collated into one dashboard, with automatic generation of reports that provide alerts and actionable insights into a service user’s activity. Care staff can log in using any device and use Lilli to drill down into individual behavior patterns at the click of a button to help inform them and make better care decisions or resource allocation based on the data insights.

Lilli is unique in the market. It combines machine learning, behavioral analytics, and sensor technology in a cloud-based solution that shifts remote care from a reactive, alarm-based approach to a highly accurate, preventive methodology that prolongs service-user independence and reduces the need for more complex treatment or residential care.

Q. What are your other focus areas?

This is a fascinating time for us, with many positive things happening. We’ve recently onboarded four new clients, and we are very proud to have become an approved supplier to Her Majesty’s Government. That’s a testament to the team at Lilli and our product, considering we haven’t been established for long. On top of this, we have been granted our first patent, which highlights our credentials and drives to take our product further.

We will also be in an execution phase in 2022, where we will be looking to extend our offering to more housing associations. In particular, we emphasize reaching out to those in areas where there is a deficit in care provision. By deploying our solution so that vulnerable people can live in their own homes, we can help prevent them from being placed into a care home.

We’re also going to be expanding further into Europe next year, particularly in those places where Lilli can have the most impact. Looking ahead, we are discussing plans to also launch in the United States in the future.

Q. No doubt, Intelligent Lilli is charting new territories in this segment. Given how frequently circumstances change, what plans for transformation are you pursuing to remain relevant now and in the future?

We have several key developments already underway. For example, our hardware is sourced from the open market; as such, we can integrate with any stream of data and are currently under a confidential working agreement to integrate with a leading case management software provider.

We’re also planning integrations with wearables and connected home devices such as Apple Watches, Fitbit, and Amazon Alexa, which will result in additional capability and enable us to take advantage of the advances in consumer devices made by these tech giants.

The Visionary Leader Upfront

Gren Paull is the Chief Executive Officer of Intelligent Lilli. He is an experienced operational leader with a track record of building fast-growing businesses and shareholder value. Prior to his appointment, he was Chief Operating Officer at healthcare technology company, Visionable, where he achieved a 10-times increase in the company’s valuation within two years. As a director with Profit and Loss responsibility at customer engagement and loyalty programme provider Affinion International, he oversaw a 10-times increase in revenue.

With an impressive track record, Gren is passionate about building enduring healthy businesses and believes Lilli can make a real difference in the care sector.

“We combine machine learning, behavioral analytics, and sensor technology in a cloud-based solution that shifts remote care from a reactive, alarm-based approach to a highly accurate, preventive methodology.”