TCV has been named a Top 25 Growth Equity Firm of 2022 by GrowthCap Advisory. Read more about TCV and see the full list of winners here.
Disclaimer: This award is not indicative of any future TCV fund performance. TCV paid a one-time fee of $7,450 to participate in the publication of the results of the GrowthCap Top 25 Growth Equity Firms of 2022 List, which was issued on 3/2/23. Other third parties or investors may disagree with this award. An award may not be representative of a particular investor’s experience.
Financial services is one of the largest industries on the planet, making up several trillions of dollars of annual revenues. Despite its outsized importance in the global economy, the industry is dominated by incumbents that struggle to innovate, weighed down by legacy tech, heavily manual processes, and costly physical branch networks. As a result, customer experience is consistently poor, cumbersome and frustrating. Tech-enabled products and services have made other facets of consumers’ lives easier and frictionless and they’re hungry for financial products that do the same. This has opened the door for tech-forward, customer-first challengers across the financial services industry.
Over the last five-plus years, we’ve doubled down on this opportunity with two specific focus areas: Digital Banking and SME Financial Operations Tools, and have been fortunate to partner with the likes of Revolut, Nubank, Brex, Qonto, Toast, Xero, and Razorpay. While each of these companies is disrupting different segments of financial services in its own specific way, each has benefitted from tailwinds associated with these two macro themes. The worlds of technology and finance continue to merge, with SaaS companies extending into payments and lending like Toast and Xero, and with banks being built from scratch underpinned by a modern, unified technological core and an obsessive approach to product experience like Revolut, Nubank, Qonto – and now Allica.
The underserved middle
We believe Allica is the only digitally-native UK bank that is building out a full-suite offering – inclusive of commercial mortgages, asset financing, savings accounts, current accounts and more – for the underserved middle of the “established SME” segment. Established SMEs are businesses that have 10-250 employees, a segment that we estimate to represent roughly one-third of the UK economy.1 These established SMEs sit awkwardly between mass market businesses – retail and micro-SMEs – who can get by with relatively simple, automated banking self-services, and large corporate entities that banks service manually given their high revenue potential.
In contrast, lending to established SMEs combines high complexity – they have a wide range of legal entity structures, shareholders, security types, loan features, etc. – with high volume and lower ticket sizes. Credit is mission-critical to these businesses, yet incumbents fail to serve the segment adequately because of slow, manual and legacy technology and processes. Other fintechs haven’t entered the segment because they lack domain expertise. The result is many credit-worthy SMEs are unable to secure loans in a timely fashion, or at all. There is a gaping need for a bank that can deliver mass-customisation combined with great customer experience at low marginal cost.
Enter Allica and its proprietary, cloud-native banking platform that is specifically suited to handle the complexity associated with established SMEs. Allica’s platform digitizes and automates many components of the end-to-end lending journey, which drives significant operational efficiency and enables best-in-class decision turnaround times, a deeply valuable proposition to SMEs and the growing UK broker community. We are excited to support Allica’s ambition to build a full suite of modern financial products over the coming years. The company’s recent launches of a market-leading business current and savings accounts are the latest steps on this journey.
Allica has shown strong momentum in the three years since being granted a banking license. The bank is profitable and growing quickly: it has surpassed £1 billion in lending to SMEs and its Q3 2022 revenues were up more than 700 percent year-on-year compared to 2021. We believe the opportunity for Allica to be sizable: the total lending flow to UK SMEs is approximately £60 billion a year and in our view Allica has already shown it has better economics than its incumbent competitors we have reviewed, with more room for improvement as the team further digitizes the lending journey. These achievements are a testament to the strength of the product, value proposition to customers, and highly impressive execution.
Our partnership with Allica
We are thrilled to announce that we have led the £100M Series C round of Allica alongside existing investors Warwick Capital Partners and Atalaya Capital Management. We have known the Allica team for many years and couldn’t be more excited to support them and their vision to become the seminal UK SME fintech. We have also worked closely with Allica’s CEO Richard Davies over the last three years at our portfolio companies Revolut and Zepz and are delighted to back him in this partnership.
We believe Richard has built a talented and dynamic team that shares a collective passion for SMEs, deep expertise in credit, and a track record for building tech products customers love. We believe the team’s collective experience and drive uniquely position them to execute on their ambitious vision for UK SME banking. We can’t wait to roll up our sleeves to support them in turning that vision into reality.
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1 Source: ONS, UK Finance, Finance and Leasing Association, British Bankers’ Association.
The views and opinions expressed are those of the author and do not necessarily reflect those of TCMI, Inc. or its affiliates (“TCV”). TCV has not verified the accuracy of any of the data or statements by the author and disclaims any responsibility therefor. This blog post is not an offer to sell or the solicitation of an offer to purchase an interest in any private fund managed or sponsored by TCV or any of the securities of any company discussed. The TCV portfolio companies identified above are not necessarily representative of all TCV investments, and no assumption should be made that the investments identified were or will be profitable. For a complete list of TCV investments, please visit www.tcv.com/all-companies/. For additional important disclaimers regarding this interview and blog post, please see “Informational Purposes Only” in the Terms of Use for TCV’s website, available at http://www.tcv.com/terms-of-use/
We’re still in the early innings of AI adoption, yet we’ve already seen it transform industries. Companies like Netflix, Spotify, and Uber have scaled internal teams from a handful of data scientists and machine learning (ML) engineers to hundreds. These teams, and the models they built to inform business and product decision-making, have shaped how consumers watch television, listen to music, and hail rides.
As AI use cases abound, ML teams face growing challenges around how to build, deploy, and maintain their models. Practitioners demand the flexibility to optimize each component of a model and, like software development, use specific tools to address the various phases of the model development lifecycle. In an attempt to bridge the development gap in ML, a new framework called MLOps has emerged. MLOps is derived from core DevOps principles and represents one of the fastest growing markets in technology today.
To date, MLOps has largely centered on data preparation, model training, and model deployment; however, building and deploying models is only the beginning of the journey. What happens once a model is running in production? The reality is most companies still lack the necessary tools to scalably monitor and understand their live models. Moreover, as these models become more complex, troubleshooting issues gets harder and both upstream and downstream problems compound.
In order for AI to achieve long-term sustainability, companies must improve model transparency, understanding, and performance. Enter Arize, a ML observability platform built by practitioners to help unpack the proverbial AI black box and optimize the performance of models in production.
Shaping the future of AI infrastructure
As the various components of the model lifecycle standardize, many companies have started to orient away from expensive in-house builds and less flexible integrated platforms. Instead, practitioners are opting for best-of-breed MLOps solutions from focused vendors like Arize to gain additional control over their modeling workflow.
Arize sets itself apart as one of the few platforms that sits at the center of production ML. Within MLOps, there has been a flood of investment in tools addressing data preparation all the way through to the model deployment stage, but few tackle the reality that all live models face over time: degradation. Without real-time monitoring and observability, ML teams spend countless hours pouring over anomalies and trying to understand problems in the data, software, and / or model itself. Arize’s observability platform seamlessly plugs into any MLOps stack and provides a scalable solution to monitor the performance of models, explain what the models are trying to do, and diagnose data and drift issues without going back to square one.
In speaking to Arize’s customers, which include many of the world’s most sophisticated ML teams, it’s clear observability is seen as a core pillar of AI infrastructure and represents a natural progression in how they think about model lifecycle management. There’s a reason adjacent observability solutions like Datadog and Monte Carlo exist in other areas of IT, and we believe ML will be no different.
Built by practitioners
Arize’s founders, Jason Lopatecki and Aparna Dhinakaran, first met at TubeMogul, where Jason was a founder who helped build out the company’s ML team and Aparna was a data scientist. Jason would eventually guide TubeMogul through a successful IPO and sale to Adobe while Aparna went to work for Uber as part of its famed Michelangelo team.
Jason and Aparna stand out in a MLOps space where many founders hail from academia. Both draw from deep practitioner roots and have experienced firsthand the heartache of spending months building and training models, deploying them to production, and having no insight into how the models actually performed once deployed. Independently, they came to the conclusion that something was fundamentally missing in the MLOps toolchain. Together, they are now focused on bringing transparency, understanding, and performance to production ML through Arize’s dedicated observability platform.
Our partnership with Arize
We’re thrilled to announce that TCV has led Arize’s $38M Series B alongside our friends at Battery Ventures, Foundation Capital, and Swift Ventures. At TCV, we gravitate towards founders that are culture and product obsessed. Jason and Aparna blend multi-stage company-building experience with firsthand knowledge of a real customer pain point. We’re incredibly excited to partner with the Arize team on their mission to make AI work and work for the people. If you are interested in joining Arize for the journey ahead, please visit their website to learn more about current career opportunities.
This award, focused on women leaders, provides a closer look at those individuals who have excelled through hard work, determination and outstanding performance.
For investors deeply passionate about healthcare, there is no dearth of excitement and inspiration in contemporary times. Specifically, we are now seeing modern technology platforms being developed on the back of transformative progress in computer vision, computational technologies and methods, and the ability for systems to interconnect, in addition to sophisticated data modeling capabilities, among others. This innovation has served as the foundation for a new generation of healthcare software companies to take on massive industry challenges, including high-impact areas such as democratizing patient access, improving the quality of care, reducing drug discovery and development timelines, enabling frictionless payment and collections, and driving efficiencies in business processes and operational workflows. Aidoc is one such healthcare technology company with a vision to be the intelligence layer for medical imaging diagnostics and care coordination.
Aidoc was founded in 2016 by CEO Elad Walach, CTO Michael Braginsky, VP of R&D Guy Reiner and CMO Gal Yaniv who met while serving in the Israeli Defense Force’s Talpiot program, an elite technology program focused on Artificial Intelligence (AI) and Machine Learning (ML) research. The company’s platform consists of 20 medical applications, including 15 FDA-cleared algorithms, designed to drive speed, efficiency, and accuracy of diagnosis in medical imaging and multidisciplinary coordination of care in the context of more complex episodes – stroke and pulmonary emboli, specifically.
Medical imaging represents a massive component of overall healthcare spend – the U.S. spends approximately $118 billion on imaging services annually, and expenditures are projected to grow approximately 7% per year for the foreseeable future. For complex patient cases, clinical personnel across multiple medical specialties must be engaged in order to determine the appropriate treatment. Historically, this coordination of care across stakeholders has proven challenging, lacked systemization, and been performed largely via offline methods. As healthcare providers have introduced more multidisciplinary programs and increasingly look to standardize the provision of care across them, technologies that enable care coordination and provider collaboration across specialties have become increasingly important.
Aidoc’s mission is to leverage AI and workflow software to drive multidisciplinary care coordination and deliver theright diagnosis, at the right time, to the right physician. To that end, Aidoc has developed a technology platform that applies the company’s 15 FDA-cleared algorithms – with many more on the way – to a radiologist’s queue in order to prioritize and triage patient cases. Further, the company’s growing repository of millions of annotated medical images is used to continually improve its algorithms over time. Importantly, Aidoc’s AI is “always on,” and the platform applies the company’s algorithms to every case simultaneously, allowing cases in more urgent need of intervention to be elevated for review regardless of where they fall in the queue. Following triage and diagnosis, Aidoc’s software also enables clinical personnel to coordinate the downstream provision of care by facilitating information sharing and communication across multiple stakeholders. Finally, and perhaps most impressively, the company’s platform integrates seamlessly within the existing operational workflows and clinical protocols of its customers.
Aidoc’s customers include a number of large health systems in the U.S., including HCA, Northwell Health, The Mayo Clinic, and Cedars-Sinai. The company also has a meaningful presence internationally, with customers that include Antwerp University Hospital, Netherlands Cancer Institute, Sheba Medical Center, and Alfred Health, among others. Finally, Aidoc also has partner relationships with leading radiology service providers, including Radiology Partners and Everlight Radiology.
The company’s customers derive value from Aidoc’s platform in terms of: a) increased diagnostic efficiency, b) improved prioritization and triage of the most complex or urgent cases, c) higher-quality diagnoses and reduced diagnostic error-rates, and d) more systematized, streamlined coordination of patient care. The company’s compelling value proposition, coupled with its reputation for high-velocity, relentless innovation and above-and-beyond customer delivery and support, has engendered customer delight. As evidence, Aidoc boasts an average net promoter score of between 80 and 90.
TCV’s healthcare team has spent the last several quarters prioritizing companies that provide AI technology across various applications for the healthcare industry. Most recently, this focus led to our investments in BenchSci, a provider of AI software for driving productivity in preclinical research for the life sciences industry, and Syllable, a provider of AI technology for provider business process automation (and soon payor and other healthcare sub-sectors).
Our Series D investment in Aidoc, completed in partnership with our friends at Alpha Intelligence Capital, General Catalyst, Square Peg Capital, and Emerge Ventures, represents another illustration of our thesis, this time for diagnostic and care coordination use cases. The Series D funding is intended to help Aidoc expand the company’s AI and ML software platform into additional applications, rapidly scale headcount, and forward invest in future growth initiatives. We are particularly excited about Elad’s vision for both the business and future of patient care, in addition to the company’s recent momentum that has established Aidoc as an emerging leader in the category. Elad has also lined-up an impressive team of advisors and experts to advise the company, including TCV Venture Partner Anita Pramoda.
“Of the various AI and ML use cases in healthcare, Aidoc’s is the one that I’m particularly excited about – their technology immediately improves patient outcomes and drives efficiencies for clinical personnel,” says Anita Pramoda, TCV Venture Partner. “Exponential patient and provider value will continue to be realized as Elad and the team roll out new algorithms, software applications, and integrations. In the future, instant diagnosis of conditions will transform patient care as we know it, and most importantly, save lives.”
Value proposition and momentum notwithstanding, what also impressed us is the humble, high-learning, and customer-centric culture Elad and his team have developed that permeates throughout the organization. Starting with Elad, it was clear during our diligence that the Aidoc team is mission-driven and firmly committed to using their team members’ talents to develop technology that improves patient outcomes through AI-driven care coordination. The results speak for themselves – Aidoc is a top-ranked employer on Glassdoor.
“With healthcare institutions facing labor shortages and navigating difficult economic situations, the future is predicated on value-based care that is enabled by automation technologies like AI and ML,” says Aidoc CEO Elad Walach. “But AI and ML are not enough in singular use cases – they must be applied across the entirety of a healthcare enterprise in order to deliver value-based care to the extent that would make a deep impact – an intelligence layer covering the entire patient lifecycle. That’s where we believe our AI Care Platform will transform healthcare. Partnering with TCV – a team that truly understands the importance of value-based care – gives us the support we need to manifest our vision.”
We are off to the races in our partnership with Elad and the Aidoc team, and are incredibly excited to help build a category-defining, generational software company that helps improve patient outcomes through AI-driven care coordination.
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The views and opinions expressed are those of the speakers and do not necessarily reflect those of TCMI, Inc. or its affiliates (“TCV”). TCV has not verified the accuracy of any statements by the speakers and disclaims any responsibility therefor. This blog post is not an offer to sell or the solicitation of an offer to purchase an interest in any private fund managed or sponsored by TCV or any of the securities of any company discussed. The TCV portfolio companies identified, if any, are not necessarily representative of all TCV investments, and no assumption should be made that the investments identified were or will be profitable. For a complete list of TCV investments, please visit www.tcv.com/all-companies/. For additional important disclaimers regarding this interview and blog post, please see “Informational Purposes Only” in the Terms of Use for TCV’s website, available at http://www.tcv.com/terms-of-use/
Digitization of healthcare provider business processes and workflows is one of our healthcare team’s key investment themes. Our focus on this theme is a derivative of several industry dynamics affecting hospitals and physician practices, including reimbursement headwinds that are pressuring already slim operating profit margins, workforce shortages and high employee turnover, and a reliance on manual, labor-intensive, and / or paper-based methods to facilitate key business processes, among others. Put simply, across front, middle, and back office applications, we believe healthcare providers will increasingly adopt automation software to drive operational and financial efficiencies, increase workforce productivity, improve employee retention, reduce burnout of clinical personnel, and, perhaps most importantly, improve the patient experience.
In baseball terms, we believe the healthcare industry is in the “early innings” with regard to software adoption. While provider organizations have spent the last 10-15 years adopting electronic medical records (EMRs), they continue to lag other verticals in terms of software adoption for managing and / or automating various business processes. As one example, healthcare providers have only recently begun implementing customer relationship management (CRM) software platforms, while most other industries have had CRM systems for decades. To this juncture, automation software (often termed “robotic process automation” or “RPA”) adoption in healthcare has largely focused on back office applications – revenue cycle, specifically. However, we believe automation of front-office workflows through technology – healthcare’s “digital front door” – will have the most profound impact on the patient experience, while also driving significant operational and financial value for providers.
It is against this backdrop that we are delighted to lead Syllable’s $40M Series C financing in partnership with our friends at Oak HC/FT, Section 32, and Verily Life Sciences. Our investment in Syllable is intended to help Syllable expand its artificial intelligence and machine-learning (AI/ML) enabled software platform into adjacent applications and use cases, penetrate new healthcare customer segments (e.g., payor, pharmacy, etc.), rapidly scale headcount across all functions, and forward invest in future growth initiatives.
There has been a lot of “buzz” recently regarding the healthcare industry’s “digital front door,” which we define as simply the technology-enabled mediums via which patients engage with the healthcare system. This is largely a function of the growing consumerization of healthcare and corresponding emphasis on improving the patient experience, and numerous technologies have emerged to help facilitate digital interactions between patients and providers. Having said that, our research – and data from Syllable’s customers – indicates that the dominant modality for patient engagement with providers remains a telephone call; in our view, this is a derivative of the uniqueness of an individual patient’s circumstances and needs, in addition to the complexity of the healthcare system and its historically limited or non-existant digital channels. As a result, we believe the technology platforms best-positioned to automate front office workflows must offer solutions that address both voice and digital mediums.
Enter Syllable and its CEO, Kobus Jooste. Kobus founded Syllable in 2017 with an initial vision to build a software platform capable of removing high-friction barriers between healthcare providers and their patients. Prior to founding Syllable, Kobus spent several years in engineering leadership roles at Google, the most relevant of which was his leadership of the engineering team that developed and launched Google Assistant. Given Kobus’ background in conversational AI and natural language understanding (NLU), we believe that he is well-positioned to build a platform capable of automating both voice and digital patient-provider interactions. More specifically, Syllable’s technology platform leverages AI and NLU to automate inbound patient interactions with providers via phone, web, chatbot, and SMS text. As 95%+ of all patient interactions with providers presently take place via phone call, healthcare providers invest heavily in call center operations – anecdotally, some of Syllable’s customers field 15M+ patient phone calls per year, and invest hundreds of millions of dollars in their own call center personnel and operations. At the same time, providers are also investing significantly in digital applications to facilitate more efficient patient engagement.
Despite this level of investment, the healthcare industry continues to lag others in terms of consumer (i.e., patient) experience. As one datapoint, healthcare’s average net promoter score (NPS) is approximately 27; an NPS of 50 to 70 is generally considered “good.” As another, healthcare providers have call abandonment rates close to approximately 30% (per data from Syllable’s customers) – one of many reasons underpinning a poor patient experience. For providers, a poor patient experience negatively impacts their revenue from multiple vantages; for example, calls abandoned may represent lost revenue opportunities, and a poor experience with a provider reduces the probability of a repeat visit. Properly and efficiently engaging patients is incredibly challenging, as any digital mechanism for managing interactions needs to be highly approachable for patients, must incorporate healthcare-specific contextualization in terms of the intent of the interaction, requires immense scalability (e.g., large systems have 15M+ inbound phone calls annually), and, to reiterate, must facilitate engagement across both digital and voice channels.
That’s precisely the problem that Syllable is out to solve. Syllable’s technology platform leverages conversational AI and NLU technology in tandem with the company’s purpose-built digital applications to automate inbound patient interactions with healthcare provider organizations. Across the company’s current customer base and use cases, Syllable’s platform is capable of automating the majority of inbound patient inquiries, and those inquiries it cannot drive to a conclusion without human intervention are routed to the customer’s call center personnel for further triage. Common use cases include routing calls to the correct endpoint, appointment scheduling, and referral and medication management, among several others. What’s more – Syllable’s ML technology trains its AI on those interactions it cannot automate such that the company’s automation percentage improves with incremental volume and customer utilization. Syllable’s data suggests that its platform drives hard ROI for customers across multiple dimensions, including a significant reduction in call abandonment rates (to zero with Syllable’s platform) and wait times, a 2x+ or greater increase in first call resolution rates, and higher appointment scheduling conversion rates, among others – all of which result in an improved experience for patients.
Syllable’s compelling value proposition, coupled with its reputation for relentless innovation and top-tier customer service, contributed to engendering customer delight, and the company boasts an NPS of 80+. Its customers include numerous healthcare provider organizations, including Parkview Healthcare, Shannon Healthcare, New York Presbyterian, Weill Cornell Ambulatory Care Network, Houston Methodist, and Mass General-Brigham, among others – a particularly impressive roster given the company only began marketing its platform in October 2021. In 2021, Syllable interacted with 39.7M Americans in text and voice about primary care, specialty referrals, vaccinations, and general practice information. Based on Syllable’s sales momentum, the company will more than double its revenue in 2022, particularly as Syllable scales product and technology resources and continues to add to its go-to-market organization.
CEO Kobus has assembled an impressive team of advisors and experts to advise Syllable on the healthcare industry’s needs and complexities, in addition to AI and ML technology. With this in mind, we are excited to have TCV Venture Partner Anita Pramoda support Syllable as the company takes-on new challenges in the healthcare industry.
“Syllables compresses the delay between needing and accessing care,” says Anita. “With Syllable, healthcare providers can now bring reliability, repeatability, and ubiquity to access – foundational tenets of good health. I’m honored to partner with Kobus and the entire Syllable team as they scale their platform and offer better care for all patients.”
Growth metrics and accolades aside, what perhaps impresses us most about the Syllable team is its unwavering commitment to approaching the healthcare industry with humility and respect. For a brief anecdote on this front, please refer to this segment with Joe and Syllable’s CMO, Adam Silverman, on a recent episode of the company’s podcast.
“Syllable is at a crucial point in its growth trajectory. As one of the most transformative platforms for health systems, our vision is clear. We want to help as many patients navigate hospital and primary care, while lowering the cost of access to care and the burden on front office staff and clinical staff,” says Kobus.
Syllable was also recognized as a most promising startup in healthcare for 2022 by CB Insights, picked as a leader in the private market from a pool of 7,000 companies – chosen based on R&D activity, proprietary Mosaic scores, market potential, business relationships, investor profile, competitive landscape, team strength, and technology novelty.
We are off to the races in our partnership with the Syllable team – including newly-appointed COO Catherline Krna, who joins Syllable from the Chief Administrative Office of Ambulatory Care and Service Lines at Stanford Health Care. We are incredibly excited to help build what we believe is a category-defining, generational software company that engenders patient delight while driving operational and financial efficiencies for healthcare providers.
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The views and opinions expressed are those of the author and do not necessarily reflect those of TCMI, Inc. or its affiliates (“TCV”). TCV has not verified the accuracy of any of the data or statements by the author and disclaims any responsibility therefor. This blog post is not an offer to sell or the solicitation of an offer to purchase an interest in any private fund managed or sponsored by TCV or any of the securities of any company discussed. The TCV portfolio companies identified above are not necessarily representative of all TCV investments, and no assumption should be made that the investments identified were or will be profitable. For a complete list of TCV investments, please visit www.tcv.com/all-companies/. For additional important disclaimers regarding this interview and blog post, please see “Informational Purposes Only” in the Terms of Use for TCV’s website, available at http://www.tcv.com/terms-of-use/.
Contracts are at the heart of business, enshrining a company’s rights and obligations across areas ranging from sales transactions and supplier relationships to employment agreements and beyond. Resulting from this centrality, rising contract volumes and legal complexity have made contract management unmanageable without leveraging technology.
Evisort delivers end-to-end contract intelligence software that turns contracts into data. Customers use a simple, intuitive interface to extract critical context from contracts, integrate that data into other enterprise systems, and automate a wide range of legal and operational workflows – themselves codified in contract data. Evisort’s platform is powered by award-winning AI that is purpose-built for contracts and trained on over 10 million documents, thereby driving a differentiated customer experience and rapid, tangible ROI.
We are thrilled to announce TCV’s Series C investment in Evisort. We believe that contracts have been both an under-managed source of risk and under-explored source of value for companies, and that Evisort’s AI-powered Contract Intelligence Platform solves increasingly important pain points for businesses of all sizes, ranging from the Fortune 500 to mid-sized companies alike.
Evisort was founded in 2016 by lawyers and technologists who saw the need for automation in contract management. The platform started as an intelligent analytics engine that extracts clauses and metadata to index contracts and their contents, making them easily searchable and manageable without manual data entry. Evisort’s AI further contextualizes the contract, indicating what type of contract it is, identifying counter-parties, flagging auto-renewal dates, and more.
More recently, Evisort has been adding workflow capabilities – relevant for coordinating contracting processes and operational workflows across the business. Evisort’s end-to-end approach ensures that all contract data is located in one repository, minimizing security risks, reducing the number of required integrations, and allowing the system to apply learnings from previous contracts to new ones.
More than any other contract management software business we’ve evaluated, Evisort’s AI platform supports a wider range of teams, industries, and use cases. Sales teams use Evisort to drive sales and renewals by reducing contracting friction and speeding time to agreement and revenue recognition. Legal departments use Evisort to drive compliance, quickly find and report on critical information, and act as a single source of truth. Procurement and sourcing organizations rely on Evisort to accelerate purchases, negotiate stronger agreements, and manage supplier risks more effectively. In all cases, Evisort drives efficiency by reducing reliance on manual legal review – a major bottleneck in many contracting processes.
Transforming the future of contract management
Evisort’s Contract Intelligence Platform has three main capabilities:
AI-Powered Contract Analytics and Insights: Evisort extracts data from contracts, produces critical insights, and reports on those insights in an easy-to-use dashboard, so that users can focus on higher value tasks. This contract intelligence is then used to generate workflows across the organization. Evisort is focused on delivering the intelligence layer between core operating systems such as customer relationship management and enterprise resource planning platforms.
Intelligent Contract Lifecycle Management: Evisort provides contract request intake, contract drafting, approvals, version control, and repository (storage, search, reporting) features. Evisort’s platform creates a source of truth so teams can centralize knowledge, collaborate easily, and simplify contract administration.
Central Contract Repository and Integrations: Evisort’s no-code platform lets legal, sales, and procurement teams self-serve, taking the burden off of IT teams and providing immediate configurability. Evisort easily integrates into existing systems to minimize the need for data migration and accelerates deployment because employees can work from the systems they already use.
Why now: A big market waiting for the right end-to-end product
At TCV, we have invested extensively behind the digitization of the legal industry – having backed innovative legal technology industry leaders such as Clio, LegalZoom, and Avvo. As part of our work in this space, we have been closely following the evolution of the CLM market for nearly a decade. In that time, customers consistently indicated a desire to manage both new and existing contracts in the same place – in other words, a true end-to-end platform. Over the last several years, our conversations in the space increasingly indicated that Evisort’s founders Jerry, Jake, and Amine had built exactly that and Evisort’s platform was seeing accelerating adoption in a largely greenfield market.
Evisort customers – which include our portfolio companies such as Netflix – typically start with analytics use cases to understand existing contracts, and then add pre-signature workflow to more efficiently generate new contracts. From there, thanks in part to Evisort’s ease of use, usage often quickly expands to additional teams and stakeholders within their organization. For customers, the results are industry-leading time-to-value, implementation speeds, self-service analytics, and flexibility to apply contract-based insights to a wide range of business functions. For Evisort, a cohesive and forward-thinking strategy appears to have translated into an innovative and fast growing company in an exciting market.
Looking Forward
As we look to the future, we are incredibly excited about the tailwinds strengthening Evisort’s value proposition for its customers. Businesses of all sizes have more contracts and a greater need to manage them than ever before. The compliance and regulatory environment also continues to evolve, requiring businesses to maintain constant visibility into their contract corpus. And companies are increasingly leveraging the data embedded in contracts to drive business processes across sales, procurement, operations, and finance.
Given that robust backdrop, we are incredibly excited to work with Jerry, Jake, Amine and the rest of the Evisort team to maximize the opportunity for AI applications in contract management.
We at TCV believe that our greatest asset is the collective group of world-class professionals with whom we have had the pleasure and good fortune to work with over our 27 year history as a firm.
This is a dynamic and constantly growing group of individuals, which includes the founders and management teams of our current and former portfolio companies, current and former employees and operating executives, and a broad set of top experts across multiple areas.
We are pleased to share that Patrick Morrison is joining TCV as Head of Portfolio Talent. In his role, Patrick will have two primary objectives: nurture and expand TCV’s global talent network; and partner with our portfolio companies to reach their strategic hiring, networking, and organizational goals. He will provide the “heat, light, and attention” necessary to build and sustain a deep and highly accessible community of world-class talent and resources.
Patrick previously worked at Khosla Ventures, where as Vice President of Talent he worked with a portfolio of 300 companies across enterprise, consumer, digital health, sustainability, and frontier. Prior to Khosla, Patrick led executive search for Adobe’s $7 billion Creative Cloud business. He began his career in Talent at preeminent search firms Korn Ferry and Bespoke Partners, where he led CxO searches for public, private equity, and venture capital backed technology companies.
“Fostering and nurturing connections – and access to top-tier talent, specifically – has never been more important,” says Ric Fenton, General Partner and Chief of TCV’s Investment Operations. “With a community of portfolio companies and executive connections across the globe, we’re thrilled to have someone as talented as Patrick aboard to be a thoughtful and strategic ‘connector’ for our network.”
TCV’s healthcare team has long been pursuing a thesis around the utilization of healthcare data, particularly for applications in the life sciences industry. Specifically, we believe that companies with proprietary technology that enables them to aggregate, curate, and contextualize healthcare data have a tremendous opportunity to layer on software applications and help address a myriad of downstream use cases for their customers. Our Series C investment in BenchSci, completed in partnership with our friends at iNovia Capital and F-Prime Capital, provides an illustration of this thesis in our portfolio – one of many, we hope, over the next few years. The Series C funding is intended to help BenchSci expand the company’s artificial intelligence and machine learning-enabled software platform into additional applications, rapidly scale headcount, and forward invest in future growth initiatives.
BenchSci was founded in 2015 by CEO Liran Belenzon, Chief Science Officer Tom Leung, Chief Data Officer Elvis Wianda, and co-founder David Chen who met one another through the University of Toronto’s Creative Destruction Lab. The company’s technology platform endeavors to increase productivity and efficiency in the preclinical research process for pharmaceutical and biopharmaceutical organizations. The life sciences industry spends an extraordinary amount on preclinical research as these efforts help develop a pharmaceutical or biopharmaceutical company’s core intellectual property. We estimate global preclinical expenditures at ~$80B annually, or ~40% of total research and development investment for life sciences firms, and scientists at pharmaceutical and biopharmaceutical companies perform tens of thousands of preclinical experiments per year.
Despite this level of investment, preclinical research has long been plagued by inefficiencies. BenchSci’s customers estimate that approximately 80% or more of preclinical experiments performed yield no value to their overall research efforts; relatedly, it is extremely challenging to identify potentially wasteful or redundant experiments a priori. This process continues to be one of trial and error – more “art than science” – and limited technology tools exist to help scientists become more productive. Moreover, the data captured in the context of these efforts exist in disparate, siloed systems, thereby inhibiting information sharing and collaboration even within a life sciences company. Even when successful, a preclinical research process takes between six to seven years on average, thereby delaying time-to-market for life-saving medicines.
The problem described above is the one CEO Liran and his team are determined to solve via technology. The company’s mission is to deliver technology that helps scientists bring novel medicines to market 50% faster by 2025. To do so, BenchSci has built a comprehensive preclinical experiment-focused knowledge graph, encompassing data on over 40 million experiments. Consistent with our framework outlined above, the company has built software and computer vision technology that automates the stitching together and curation of experimental, bioinformatic, and other data from numerous, disparate sources, including its customers’ own internal data. Further, the company’s team of PhD scientists works alongside BenchSci’s product and technology teams to contextualize BenchSci’s 100+ machine learning models and algorithms such that its knowledge graph and results make “scientific sense” to scientist end-users as they leverage the company’s technology platform.
BenchSci’s flagship application was launched in 2017, and leverages artificial intelligence to help scientists select the optimal antibodies and/or reagents to use in their experiments based on experiments previously performed that are relevant to the study in question. This saves scientists significant time and resources – customers indicated to us that they have saved tens of millions in hard costs alone by eliminating redundant/wasteful reagent purchases, not to mention the time savings (several weeks to months per project) and other efficiencies they’ve realized. The company is not stopping there, and we are particularly excited about what BenchSci is going to do next with its breakthrough technology that will shape the future of preclinical research, although we will leave it to Liran and his team to share more in the coming months.
BenchSci’s compelling value proposition, coupled with its reputation for relentless innovation and superb customer service and support, has engendered customer delight, and the company boasts a net promoter score of 80+. Its customers include 16 of the top 20 pharmaceutical companies (by revenue), in addition to over 4,500 leading research centers globally, and its platform is being used regularly by 50,000+ scientists. CEO Liran has scaled the organization to meet latent demand – BenchSci has grown its employee base more than 8x in the past three years, and expects to reach 400+ employees by the end of 2022. The company has been recognized as a Deloitte Tech Fast 50 company and a CIX Top 10 Growth company.
CEO Liran has also lined-up an impressive team of advisors and experts to advise BenchSci on life sciences research and development, organizational culture, and artificial intelligence and machine learning technology, including TCV Venture Partner Jessica Neal (former Chief Talent Officer at TCV portfolio company Netflix).
“BenchSci plays an important role in curating and contextualizing healthcare data to increase productivity in the preclinical research process,” says Jessica, “and I look forward to supporting Liran and his team as they continue to scale.”
Growth metrics and accolades aside, what also impressed us about BenchSci is Liran’s unwavering focus on fostering BenchSci’s culture. Liran believes the company’s distinguished culture is instrumental to its success, and he endeavors to build an inspiring, inclusive, and equitable work environment where employees are set up to thrive and have a meaningful career. Starting with Liran, it was clear during our diligence that BenchSci’s employees pursue continuous improvement and a high-degree of transparency and candor. The results speak for themselves – BenchSci has been named a certified Great Place to Work® and is a top-ranked organization on Glassdoor. We are excited to add Jessica Neal to BenchSci’s advisory board to help Liran continue to develop and grow the company’s culture as BenchSci scales through its next major inflection points.
“Our recent Series C raise enables us to build and deliver a next generation AI solution for global pharmaceutical companies that will enable scientists to exponentially improve their preclinical R&D work,” says BenchSci CEO Liran Belenzon. “We are a mission-driven organization intent on achieving success beyond success, and I’m excited that TCV recognizes our market-leading potential and has chosen to back our meteoric hypergrowth.”
We are off to the races in our partnership with Liran and the BenchSci team, and are incredibly excited to help build a category-defining, generational software company that drives productivity and efficiency in the preclinical research process, thereby bringing novel, life-saving medicines to patients faster.
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The views and opinions expressed are those of the speakers and do not necessarily reflect those of TCMI, Inc. or its affiliates (“TCV”). TCV has not verified the accuracy of any statements by the speakers and disclaims any responsibility therefor. This blog post is not an offer to sell or the solicitation of an offer to purchase an interest in any private fund managed or sponsored by TCV or any of the securities of any company discussed. The TCV portfolio companies identified, if any, are not necessarily representative of all TCV investments, and no assumption should be made that the investments identified were or will be profitable. For a complete list of TCV investments, please visit www.tcv.com/all-companies/. For additional important disclaimers regarding this interview and blog post, please see “Informational Purposes Only” in the Terms of Use for TCV’s website, available at http://www.tcv.com/terms-of-use/
