The Future Is Now: Dr Lewis Z Liu Of Eigen Technologies On How Their Technological Innovation Will Shake Up The Tech Scene
An Interview With Fotis Georgiadis
Execution is more important than strategy — There are so many ideas out there, but if you wait to execute until your entire strategy is laid out, it might just be too late. You will probably run out of time and money before you run out of great ideas.
As a part of our series about cutting edge technological breakthroughs, I had the pleasure of interviewing Dr Lewis Z. Liu.
Lewis co-founded Eigen Technologies, a global intelligent document processing (IDP) provider, in 2015. Eigen’s mission is to transform data into actionable insights, regardless of source, so all organizations can make better-informed decisions that drive the best outcomes for their people, customers and investors.
Having started his career as a consultant at McKinsey & Company in London, he then founded and led the Quantitative Finance & Strategies Division for Aleron Partners LLP, a boutique private equity advisory firm. He is also a former Senior Advisor to Linklaters LLP, where he co-founded the Tactical Opportunities Group, a deal origination team.
Lewis holds a Doctorate in Atomic & Laser Physics from the University of Oxford. During his studies at Oxford, Lewis invented a new class of X-ray laser, and the mathematics behind this invention was later abstracted into Eigen’s core technology. Lewis received Harvard’s first Joint Bachelors in Fine Arts and Physics, as well as a Masters in Theoretical Physics, during which he conducted antihydrogen research at CERN.
Thank you so much for doing this with us! Can you tell us a story about what brought you to this specific career path?
Of course, I’ll quickly tell you about the evolution of Eigen Technologies. After I graduated high school, I had a summer job at a tire manufacturing company in New Jersey before heading off to Harvard. The job required manually doing data entry from many boxes of printed documents into a digital storage system. After one day of doing this tedious work, I thought there had to be a better way to sort through this data. So, rather than spending the whole summer doing it, I coded a basic program, what I now jokingly call Eigen 0.0, that would do it for me. Unfortunately, what I thought was a genius idea meant I automated myself out of a job within the first three days.
Later, after graduating from Harvard, I began my career as a consultant at McKinsey, reconciling data for banks that were struggling at the height of the financial crisis. I quickly learned, to my astonishment, that companies had massive amounts of data, but no real way to make that data work for them. Studies from firms such as McKinsey show that most organizations can’t even use between 80–90% of their data. That is why Eigen was founded — to help solve this problem and make the world’s data useful.
Can you share the most interesting story that happened to you since you began your career?
I think the most interesting story that comes to mind was a defining decision early in Eigen’s history, one that for me was really a no-brainer, but that was actually a very controversial decision at the time. When I first started Eigen– when we were only a 10–15-person company — we had a prospect, Goldman Sachs, issue a request for proposal (RFP). The RFP was essentially looking for a technology to help Goldman Sachs comply with Dodd Frank, which at the time was one of the new banking regulations brought in following the global financial crisis of 2008.
For me, it was simple. We had to go all in on this opportunity to work with one of the top 5 investment banks, even if there were a lot of questions being raised internally about what this meant for the company. We were only about a year old at the time, and I was in the middle of my honeymoon. But I instantly knew this was our once-in-a-lifetime big break to really put Eigen on the map. So, I ended up working through my honeymoon, day in and day out, constantly dialing into New York for the business pitches.
In the end, we won the RFP, and it was a defining moment for us as a business. I think the reason why we did was because Goldman Sachs was really looking for best-in-class, cutting-edge technology — not just a big-name tech or consulting company that made a lot of promises about what they could deliver. When they initially sent out the RFP, they said they didn’t care about the size of the company, but that they needed innovative tech that could get the job done. They were true to their word, and we proved that Eigen was the best choice in their vendor selection process. But after we won the deal, which was honestly unexpected, some of the team asked, “Lewis, are we actually going to do this project?”
To me, the answer was obvious — “of course, we’re going to do this, because this type of big opportunity is how great companies are made”. So, over the next six months, we had to scale the business from 10–15 people to around 40 people. We got to work completing the rest of our solution (at the time), which had our innovative machine-learning AI components, but still lacked a few key features that we needed to deliver the Goldman project. It was, by far, the craziest go-all-in moment of my entire life. In the end, it all paid off. We were happy with the product we built, and the Goldman team were very happy with the results. So happy in fact that they and even invested in Eigen’s Series A, which really did put us on the map.
It was one of those moment-in-time decisions in my career, where I instantly knew “I have to go all in”, and we’re either going to come out as a major player on the map or we’re going to fail. And that’s okay because that’s what being a startup founder is all about.
Can you tell us about the cutting-edge technological breakthroughs that you are working on? How do you think that will help people?
When we think about innovation in the world of DeepTech, or specifically in terms of AI, there are two types of innovation. One is on the data science side, and being able to exploit more data, leverage better algorithms or better features. This side is really focused on machine learning. The second type of innovation — is designing a product within a workflow that enables machine learning to work in production, in organizations and add value. There’s tons of effort and research that supports both components.
Considering the first type of innovation: We are really proud of all the data we’ve collected and the way we’ve processed it, the algorithms that we’ve architected, and how we’ve broken down human language to analyze documents in a way that no one else has ever done.
But then thinking about the second piece of the innovation puzzle, you are looking at how you design a product. How do you design a workflow that enables an AI solution to be trusted by a large enterprise? There are so many claims in the news today about these large tech giants being able to do complex tasks, like diagnosing cancer better than human doctors. But these are all in very controlled environments — not real-world situations. The challenge with AI, beyond just the algorithms or the data, is how do you design a system, a product that enables tangible results in real situations? Or in Eigen’s case, how do you design a system or a product that allows a business user, like a lawyer, a banker or a business analyst, to take the documents they want to analyze and then teach the machine to do that for them in a really easy-to-use and comprehensible way?
The whole process of design thinking is actually about building a product that takes a really complex set of algorithms and data structures and makes it easily accessible and usable with just a few clicks for one of these end users. That’s the innovation that we’re focused on and what we’re really excited about at Eigen.
Thinking another step further though, we need those users to trust the machine’s output, and that’s where AI ethics comes into play. That’s where we need to make sure we’re constantly teaching and re-teaching the machine to make sure it’s not biased in certain ways and to cut down on other potential risks or errors. We’ve also designed the AI to ensure our platform can achieve the right level of accuracy rates and the level of precision needed in production. Even with those checks, when there are areas in which the machine is uncertain, it will kick the data or document out to have a human review it. All these things are really complicated to get right in a workflow. That said, I think that’s why we have been able to achieve such large-scale transformations in organizations, because it’s not just the fact that we have clever mathematics and have made great use of our data, but we’ve also been innovative on how we’ve built a workflow that enables users to get AI into production via an easy-to-use piece of software.
How do you think this might change the world?
Let’s look at the global financial crisis as an example. It’s no secret this was a massive problem that ended up having huge implications on the way the world’s financial system operates today. From 2008 to 2010, in the U.S. alone, there was more than $4 trillion of negative economic impact that occurred as a result of the financial crisis.
In the movie, The Big Short, you see a very clear picture of why extracting and analyzing data from large complex documents has such a huge impact and is so important. There’s a scene where Dr. Michael Burry, who’s played by Christian Bale, is lying on the ground, exhausted, having read all these massive documents that no one else had taken the time to read, and he realizes that the global financial system was going to come crashing down. Unfortunately, the fact that these documents are too expensive, too complex, and too cumbersome to read meant that investors (from large firms, like Lehman Brothers, to mom-and-pop investors) had no idea what they were buying or selling. A problem that ultimately led to the financial crisis, a problem that directly impacted almost all of us in one way or another.
Of course, hindsight is 2020. As a result of events like this, what we’re doing today is helping banks to not only meet their regulatory obligations, but also to make the financial system safer. For many of our bank customers, Eigen’s technology solution processes all of their financial contracts to ensure when they report on these documents to regulators, like the Federal Reserve and FDIC, we can avoid another global financial crisis. Because today, even if one part of a top 10 global bank goes bankrupt, the Federal Reserve will be better prepared to make a quick, better-informed decisions based on a complete analysis of all the industry data that has been reported to it over the years.
Keeping “Black Mirror” in mind, can you see any potential drawbacks about this technology that people should think more deeply about?
Let’s think about the destructive power of common human intelligence, or lack of human intelligence, and what adding badly trained AI into the mix might really mean. Big tech companies often come under fire for insufficiently filtering information or misinformation, essentially for using ‘bad data’. AI, or the type of technology we work with, has the ability to do both good and bad, depending on the data it is trained on and how it is managed. Whether it’s fake news or conspiracy videos, I see a big potential threat if people use bad data to train algorithms that can ultimately manipulate AI systems.
There is ever increasing amounts of data being put into machine learning systems, so to avoid the threats and drawbacks of this technology, it’s important to have checks and balances in place. Just optimizing for things such as clicks or views can end in scary results, like dramatic changes in public opinion based on misinformation. As we are increasingly seeing, this is a threat to democracy itself. We have already seen with some tech companies, like the social media titans, the consequences of too many unchecked and uncontrolled algorithms that amplify problems. Without careful consideration, badly trained AI could also create issues.
What are you doing to ensure your technology doesn’t cause these issues?
As the founder of an AI company, I think a lot about this problem — and how to build the right checks into the system to avoid contributing to these problems. That’s also why we really focus on small data AI.
When someone teaches the machine on a set of tasks, every single person who taught the machine is accounted and is auditable. Our product records when, where and how they interacted with it and if they made any changes to the machine learning models. That way, if the machine makes some dubious decisions, you can go back and check to find out who interacted with it last and correct or update it to perform better in the future. That’s the kind of accountability needed in this new world of AI because AI has the power to change the world for the better. However, it can only do that if you have a high level of accountability.
What do you need to lead this technology to widespread adoption?
Thinking about this from a product perspective, if you throw a financial document, medical document or quality assurance document into our machine, it can be processed easily — extracting and analyzing the relevant information that our clients need to make better businesses decisions. But the bigger question is how do we get our product into the hands of everyone?
As we head into 2022, one of our three strategic pillars, is product partnership. We recognize that for some, Eigen is the whole solution for the problem they are looking to solve. For others, Eigen might be part of the solution. For these customers, they might want (or need) to connect Eigen to systems upstream they have such as a document management system, a contract lifecycle management system or a manufacturing database. They may also need to connect our product to a downstream system like SAP, Oracle, an ERP system or an analytics platform. Because of this, we’re investing in making Eigen more easily accessible to other platforms like these, so our customers can more efficiently use it in their ecosystem.
For us, one of the things we’re focusing on is making Eigen ubiquitous and a component in many of these other software solutions. For example, the goal is that if you’re in an ERP system, managing vendor contracts, Eigen will also be natively in that system to help pull all the data out of your vendor contracts. That’s how we’re going to get better and drive much more widespread adoption.
What have you been doing to publicize this idea? Have you been using any innovative marketing strategies?
Recently, we’ve been doing a lot of co-branding, through announcements, webinars, case studies and more, with other technology platforms — to promote the win-win factor of joint adoption. In fact, we just announced Eigen’s partnership with NetDocuments, a leading cloud content and document management platform. A few months ago, we ran a joint marketing campaign with both Unqork, a leading no-code enterprise software platforms, and one of our joint clients, Goldman Sachs. As part of this campaign, leaders from Eigen, NetDocuments and Goldman Sachs participated in a webinar, explaining how to unlock your document data using NLP and no-code systems.
This marketing strategy may not be a new concept, but it’s critical in promoting our focus on partnerships — because it displays that when you put these tools together, you get a much greater impact than the two tools do on their own and our clients get an even better return on their investments.
Further, Eigen recently commissioned Forrester to conduct a Total Economic Impact™ (TEI) study, which examines the potential return on investment (ROI) enterprises may realize by deploying Eigen’s no-code AI platform. I believe that ultimately the reason executives buy software is because there’s something in their company’s current processes that could be significantly improved by that software, and that you can quantify that value. At Eigen, we’ve been meticulous in terms of documenting value, so we can demonstrate it to prospective customers.
Lastly, we see so many siloes within enterprise organizations, particularly cases where one division has Eigen, but the broader company may not even know they actually have our software already and could (and should) leverage it across the business. To tackle this we’ve started putting more marketing effort into driving greater adoption in existing accounts. Now, once we win an account we immediately work across the rest of the organization, to help break down these silos.
None of us are able to achieve success without some help along the way. Is there a particular person who you are grateful towards who helped get you to where you are? Can you share a story about that?
I’ve already shared the story of getting our first big break RFP during my honeymoon, but the real hero of that story and the person who I have to thank the most for my successes is my wife, Andrea. She’s the one who sacrificed our honeymoon when I told her I had this once-in-a-lifetime opportunity. We were literally on our way to Sri Lanka when I found out about the RFP, and she’s the one who said, “Lewis, you have to do this.” It doesn’t get much more supportive than that — and she has remained unstintingly supportive of me throughout the whole of the Eigen journey. She’s always been one step ahead of me when it comes to being insightful about people or situations, and I think that in many ways, Eigen is as much her company as it is mine. I just happen to be the face of it, but I think she deserves as much credit as I do for the success of Eigen thus far.
How have you used your success to bring goodness to the world?
At Eigen, we’re working to make the world’s data useful, so our clients can solve big problems. For example, we’ve all filed healthcare claims that often take a long time to process forward before you can get the healthcare services you need. While some people can pay out of pocket and wait on insurance reimbursements, others don’t have that option or opportunity. The reason it takes so long for insurers to make these decisions is due to the collection of data from disparate sources, which is a very time intensive and manual process. These documents (lab reports, doctor’s notes, etc.) all come in various formats and generally require a human to read and process them before insurers can then make a decision — and patients can get access to much-needed care. In one of our newest use cases, we’re helping process the documents to speed up the overall process, so people, like you and me, can get a decision back in minutes instead of days — leading to better patient experience, and, more importantly, better health outcomes.
What are your “5 Things I Wish Someone Told Me Before I Started” and why. (Please share a story or example for each.)
Raise capital earlier — Scaling a business quickly, especially an AI business, requires significant investment; you’ll need this to keep your momentum. One of the things I wished I did was to raise our Series A earlier.
Invest in marketing — It is often easy to dismiss marketing as ‘fluff’, and we underinvested in marketing for the first few years of the business. But generating top-of-the-funnel awareness takes a lot longer than it seems, especially in the B2B space. While we’re all caught up now, we could have had a much smoother path if we’d invested earlier in our company journey.
‘When people show you who they are, believe it’ — This is a favorite line, from my wife. In building a company, it’s fundamentally about building a team to build something, reach an end goal, etc. Therefore, finding the right people and building a team is by far the most important job of a CEO. That sometimes means you have to evaluate your team as objectively and as fact-based as possible. This also means you’ll need the ability to have hard conversations before it’s too late.
Execution is more important than strategy — There are so many ideas out there, but if you wait to execute until your entire strategy is laid out, it might just be too late. You will probably run out of time and money before you run out of great ideas.
Context is king — So many times we think we have communicated the context to employees, but so many times it gets muddled and doesn’t make its way through to the entire team. Having context for the team generally increases effectiveness by 10–100X.
You are a person of great influence. If you could inspire a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger. 🙂
I think, as a human race, we often forget that we’re all human. There are always two (or three or four) sides to every story, but ultimately, there’s one common side and one common story — which is that we are all human. We all deserve to be loved and respected and heard. If there’s one movement I could inspire, it would be to remind people that we need to work together and remembering that we’re all just human. Because that’s the only way we’ll get countries, like America and China, to work together on worldwide problems such as climate change. If we were more united, then our challenges wouldn’t be nearly as bad. So, if I were to have much more power, and much more influence, to encourage some kind of movement it would be to inspire people to recognize we’re all human, in a deeply fundamental way.
Can you please give us your favorite “Life Lesson Quote”? Can you share how that was relevant to you in your life?
‘Imagination is more important than knowledge’ — While this is a very cliched Einstein quote, I’ve really tried to abide by this idea. It’s, of course, important to gain knowledge and skills, but being a pioneer is all about imagining the impossible. As an AI entrepreneur, I think that having an art degree at Harvard has often been more helpful than my physics degree.
Some very well-known VCs read this column. If you had 60 seconds to make a pitch to a VC, what would you say? He or she might just see this if we tag them 🙂
80–90% of the world’s data is useless — meaning this data is unstructured and unusable by organizations and digital processes. Eigen is a no-code AI company that enables anyone in an organization to make their data useful by transforming unstructured documents or images into structured usable data. We’re unique in that we have pioneered ‘Small Data AI’, which means that instead of needing thousands of examples to teach our AI how to extract data or answer a question, you don’t need any. You can ask a question, any question, as domain specific as you want, and Eigen can answer that question. This process can be repeated across millions of documents and billions of data points per organization. Our revenue has more than doubled year-over-year since our Series A three years ago, and we now serve over 40% of the world’s largest banks, manufacturing companies, insurance and healthcare providers, and professional services firms, including the likes of Goldman Sachs, Aviva and Deloitte.
How can our readers follow you on social media?
You can find me on LinkedIn or on Twitter, @LewisZLiu. You can also follow Eigen Technologies on LinkedIn, Twitter and Facebook to learn how to make your data useful.
Thank you so much for joining us. This was very inspirational.
The Future Is Now: Dr Lewis Z Liu Of Eigen Technologies On How Their Technological Innovation Will… was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
