What's covered in this article:
Who are we?
Re:infer Ltd. was born in 2015 as a spin-out from the world-leading A.I. research lab at UCL where our founders, Machine Learning PhDs and professors, met. Our Chief Scientific Officer, Professor David Barber, leads UCL's A.I. research lab to this day.
We are backed by top tier investors such as Crane, Seedcamp, and Touchstone. Since 2015 we have gorwn into a diverse team that includes cosmologists, entrepreneurs, consultants, and mathematicians. We are continuing to grow our team as we expand to fulfil the needs of our clients.
Why do we exist?
The most successful companies are the ones that understand their customers and teams, and streamline their processes to serve their customers the best.
But every large business is faced with a mountain of digital communications data that is typically:
- Too high volume to keep pace with each day, leading to businesses haemorrhaging time and resources to process even a fraction of the communications they receive
- Growing exponentially as employees and customers communicate more than ever with each other digitally and remotely
- Multi-channel with customers and employees communicating through various different channels - e.g. email, chat, phone call, survey responses, support tickets
- Understood and actioned manually by staff through inaccurate and inefficient processes that are incredibly time consuming and costly
In-house or custom-built solutions that attempt to shift some of the burden of understanding and actioning this data have required business users to rely on Engineers and Data Scientists to build models capable of interpreting their data. These are often slow to create, brittle, slow to update, and can provide limited insights or actionability at a high expense.
This mountain of communications data represents both a company’s most valuable resource, and its biggest source of inefficiency. Most companies miss out on the wealth of knowledge and insights hidden in their communications, and create massive operational inefficiency just trying to keep up with the problem of actioning them.
For example, we conducted a time and motion study at a Tier 1 Investment Bank and found that 70% of employee time was spent just actioning and responding to emails.
There’s nothing more wasteful than the resources expended trying to understand enterprise comms and nothing more valuable than the insights that lie within them.
Customers and clients are already telling you what they need, but you simply can’t hear them, because so much of that communication falls through the cracks.
In a world where we communicate more than ever, we are actually less connected than ever before, creating a chasm between internal teams, companies and their customers.
We call this problem the Connection Deficit.
At Re:infer, we exist to help businesses overcome the connection deficit.
This problem isn’t specific to any industry, and neither are we. We’re agnostic to what each business does, but we want to help them do it better.
We help them understand and action all of their communications data in real time, and use this information to drive efficiency and elevate customer experience.
Our purpose, mission and vision
Re:infer was created to help the world connect better. We use Machine Learning technology to understand massive amounts of communications data instantly - data that was previously getting lost or ignored as businesses didn't have the means to process it all.
Now, we discover the value of every communication, and use it to bring teams together and businesses closer to their customers. By building closer connections, we drive efficiency and elevate customer experience.
Re:infer is a spin-out from the world leading A.I. research group at UCL and builds on the decades of first-class research of the founding team.
The UCL A.I. Centre carries foundational research in A.I.. As we transition to a more automated society, the core aim of the Centre is to create new A.I. technologies and advise on the use of A.I. in science, industry and society.
World leading AI research
The team's research has been published in world leading peer reviewed conferences and journals: