Over $600 billion is spent each year on recruiting activities worldwide, and yet many companies still feel their talent needs are not being met. U.S. companies spend $8,000 - $15,000 and 22-40 hours per new entry level hire, but, because they often target a small pipeline of schools, this investment of time and money does not yield the desired return. Further, companies are looking to increase their return on recruiting investment while addressing shortcomings in diversity and inclusion goals.
Many students and early career applicants want to take advantage of this high demand for their talent, but are unsure how to do so. This lack of clarity is especially acute for students from marginalized or non-traditional backgrounds. They need resources on topics like resume preparation, pitch practice, and networking tips, but don't know where to start and don't identify with generic templates currently on the market.
Upkey has created a solution to empower these students and benefit employers at the same time. Through partnerships with universities, Upkey provides high school seniors and college students with fun, interesting, and engaging learning tracks to help them cultivate a marketable professional image, show off their grit and tenacity, and emphasize their potential. For enterprises, Upkey provides a low-cost tool to reach a wider variety of applicants and remove traditional hiring biases. Each of these solutions are powered by AI engines that provide content recommendations and actionable feedback for both students and recruiters.
We are pleased to announce Tensility has invested in Upkey as a participant in their $2 million seed round led by S3 Ventures. Upkey presents a number of unique advantages over current solutions on the market: First, it creates a data-rich recruiting channel for companies to identify high-quality, diverse talent that may otherwise fall outside traditional recruiting strategies. In addition, these products provide students an alternative to the dry and one-size-fits-all approach to career development. The result is a network of engaged applicants and employers: universities use Upkey to ensure their students are prepared for recruiting while enterprises use Upkey to expand their recruiting footprint cost-effectively.
Upkey was established in Chicago, Illinois in 2014 by Amir Badr, who was previously the diversity lead at Excelon. He was inspired to start Upkey as a way of solving some of the pain he felt as an immigrant in the United States. Upkey has a vision to build an inclusive talent development service that helps students of all backgrounds become more confident and capable of achieving professional success, and we are excited to contribute to that mission.
Today, businesses are generating a massive amount of data every second across hundreds of disparate data sources. As organizations attempt to capitalize on new ML/AI capabilities, each new system or tool disaggregates the journey from data to business value even further, creating costly, slow, and labor-intensive data preparation. The situation creates challenges for the data engineering teams to keep up with business and technical demands.
In machine learning, a "feature" is an input variable, similar to the explanatory ("x") variable in simple linear regression. A machine learning project might use hundreds or even millions of features, and each feature must be paired with labels (similar to the dependent "y" variable) to train a model. A robust and scalable ML/AI development program requires improving the feature extraction process, an early step in ML that prepares data for analysis by abstracting complex schemas and their data into basic objects and attributes.
Relying on reference architectures for feature re-use can help, but this introduces issues of latency, complexity, and another data silo to be managed. To simplify and speed up feature extraction and re-use, a new technology has arisen, called feature stores, which assists with the demanding data preparation required for effective ML. No longer unique to the capabilities of large firms like Uber and Airbnb, feature stores can transform raw data into feature values, store those features, and serve those features for training and analysis in the future.
Importantly, feature stores do not remove the need for Snowflake or similar cloud solutions. Feature stores easily overlay with existing data infrastructures, enabling almost instantaneous updates, reducing risk, and scaling quickly with an enterprise. The graphic below compares feature stores to current approaches.
In short, all of an organization’s data can be converted to reusable features and analyzed with full fidelity, regardless of format or source location, for immediate analytics. Not only does this speed up a data scientist's development timeline, but it brings economies of scale to ML organizations by enabling collaboration. When a feature is registered in a feature store, it becomes available for immediate reuse by other models across the organization.
One example of a leading provider of feature store capabilities is Molecula, who recently completed a $17.6 million Series A round with Tensility as a participant. Molecula leaves data at its source and continuously extracts and updates only features into a centralized feature store. This process eliminates the need to copy, move, or pre-aggregate data, reduces the data footprint by 60-90 percent, and provides a secure data format for sharing.
Whether through Molecula or another organization, feature stores pair with current systems to enable prescriptive analytics while reducing complexity, costs, and risk. We are excited at the capability they provide to data engineers and data scientists to improve the data pipeline and leverage all of a company's data for better business outcomes.
Tensility is proud to announce the acquisition of Stabilitas, a Tensility Fund II investment, by OnSolve. This is an exciting exit for us, and we are thrilled to see a team led by two veterans, Greg Adams and Chris Hurst, navigate the entrepreneurial cycle to build Stabilitas from zero to a successful exit.
Now more than ever, the ability to understand a situation and its impacts in real-time, rather than after the fact, is a key skill for businesses around the world. However, doing so in today’s data-rich and resource-constrained world requires a force multiplier, such as artificial intelligence, to enable humans to quickly analyze and act on insights from the countless data points generated every moment of every day. The value of such a capability is clearly recognized by OnSolve, a leader in multi-modal critical event management, as evidenced by its acquisition of Stabilitas Situational Awareness platform. Stabilitas created a product that combs over 17,000 data sources to correlate critical events with key enterprise assets and will bolster OnSolve’s Critical Event Intelligence capabilities.
With just under a year since our initial engagement, this is one of Tensility’s shorter investments, however, we have thoroughly enjoyed the opportunity to work with Greg, Chris, and the rest of the Stabilitas team. It has been a pleasure to see the team grow its offerings and partnerships over the course of our relationship. From deepening Stabilitas' relationship with G4S – the largest global, integrated security company – to understanding the societal impacts of COVID-19 and sheltering at home, it has certainly been a productive year for Stabilitas and we wish them all the best as they become part of the OnSolve team.
To learn more, read the press release from OnSolve, check out our previous announcement of our investment in Stabilitas, or visit the Stabilitas website.
The top 50 Quick Service Restaurant (QSR) brands in the U.S. generate over $200 billion in sales annually, with $150 billion coming from their 14 billion drive-through and phone-in orders per year. However, the industry has been facing several challenges for continued revenue growth, such as rising wages for service agents, increased wait time for drive-throughs, inaccurate orders due to human errors, and missed upsell opportunities. In 2019, McDonald’s acquired Dynamic Yield and Apprente to make ordering faster and more accurate by using conversational AI, a speech-based digital assistant that automates communication and delivers personalized customer experience at scale. These acquisitions put additional pressure and urgency on other QSR brands to improve their operation and service experience.
We are excited to announce Tensility’s investment in ConverseNow’s $3 million seed round. ConverseNow develops conversational AI software to automate drive-through and phone ordering for QSRs. By deploying ConverseNow’s automatic speech recognition and natural language processing technologies, restaurants can now simplify its operations and increase its revenue through data-driven, personalized upselling. We see several key advantages in ConverseNow’s approach to the market compared to its competitors: First, the company is highly focused on serving QSRs and improving their workflows, as opposed to offering a one-size-fit-all solution. Second, ConverseNow closely partners with key vendors in the QSR ecosystem, such as providers for point of sale systems and self-service kiosks and is therefore able to effectively reach customers. Lastly, ConverseNow follows a low risk, step-change rollout - by allowing customers to deploy an AI-assist solution before implementing a fully-autonomous AI - which helps accelerate adoption.
The company was founded in Austin, Texas in 2018. The founders, Vinay Shukla and Rahul Aggarwal, both bring more than 15 years of IT consulting experience. They have also built a strong technical team with deep expertise in both machine learning and natural language processing. To date, the company has successfully completed several pilots and its solution is now live across multiple franchises and restaurants. We have been impressed with the speed and execution capabilities of the ConverseNow team, and we look forward to partnering with them to enhance the restaurant ordering experience!
At Tensility, we’re constantly improving on our pattern recognition. Our ability to spot revolutionary technology innovations, market opportunities, and high-performing founding teams is shaped largely by the previous successes and failures that we’ve had with our portfolio companies. The long time horizon of the venture business makes it difficult to spot the correlations and causations in the data, which often is fuzzy and incomplete. Outcomes are influenced by so many factors that searching for statistically significant relationships is difficult.
Let’s talk about founders. Our experience over 20+ years of venture investing in technology startups has taught us what to look for in successful entrepreneurs:
The energy required of founders to both have all these skills as well as grind through building a company from scratch has historically implied at least one characteristic: youth. Anecdotal evidence appears to support this trend: Mark Zuckerberg founded Facebook when he was 19. Michael Dell started Dell Technologies at the age of 21. Paul Graham once said that “the cutoff in investors’ heads is 32… After 32, they start to be a little skeptical.”
Recent research from Ben Jones, professor of strategy at the Kellogg School of Management, however, offers surprising results:
The researchers were chiefly interested in high-growth new ventures—the kinds that can transform the economy—and understanding whether the Silicon Valley mythology was true. So they limited their dataset to include only technology companies, and further winnowed that down to the fastest-growing 0.1 percent—in other words, the one company out of every 1,000 that saw its sales or number of employees increase the most in its first five years.
45! What a disconnect from the prevailing narrative of young founders being successful. We see this narrative at play everywhere. The average age of YCombinator Winter 2018 Cohort was 29.9. The average age of TechCrunch awards recipients between 2008-2016 was 31. Etc.
Several factors may contribute to this phenomenon. Technology startups depend heavily on software developers- and the average age of software developers appears to be 28.7. The media also tends to overemphasize young entrepreneurs.
How does this new understanding of who are likely to be successful founders affect our investing?
We’ve never explicitly targeted younger founders. But, prevailing narratives can have subtle and unintentional effects on our investing decisions. Moving forward, we should all be aware of potential biases and make sure we don’t fall prey to them.
Businesses today face risk from a myriad of factors- from political upheaval that disrupts supply chains, to natural disasters which threaten people and facilities, and even to local crime which puts employees in danger. Companies can attempt to plan for these risks through enterprise risk management, but collecting actionable intelligence in time to mitigate risk continues to be a difficult task.
Stabilitas, based in Seattle, is the leading provider of Critical Event Intelligence that combs the world’s data to correlate critical events with key enterprise assets. The Stabilitas platform integrates the industry’s widest array of global data sources, including unstructured data like text-based news sources as well as structured data sources like earthquake feeds from the USGS. Stabilitas’ key innovation is using multiple machine-learning techniques to synthesize this data from disparate structured and unstructured sources into discrete critical events. Stabilitas works with customers to develop a full picture of the customer’s assets, however they may be distributed around the globe, and surfaces only that critical intelligence which is relevant to the customer, filtering out the noise and amplifying the intelligence signal. Unique in the industry, Stabilitas also offers API connectivity that allows seamless integration with a customer’s existing security operations system.
Tensility is pleased to announce an investment in Stabilitas, which will enable Stabilitas to scale their customer base and expand into new markets. We’re excited to work with the founders, Greg Adams and Chris Hurst. Greg and Chris are both experienced military veterans and graduates of Harvard Business School, and they each bring unique and relevant experience in enterprise risk to Stabilitas. As an Army Green Beret, Greg saw firsthand the value of actionable critical intelligence in keeping his troops safe. Chris managed enterprise risk both in the Army and as while working at CH2M Hill on government contracts.
Stabilitas currently partners with large enterprises like Amazon and Procter & Gamble to provide timely and actionable critical event intelligence. Stabilitas also has a valuable partnership with G4S, the world’s largest security company. We’re excited to see the Stabilitas team move forward in addressing the risks inherent in doing business in today’s unpredictable world, and are confident that critical event intelligence provided by Stabilitas will be a invaluable resource for for all enterprises.
Many enterprises are deploying artificial intelligence (AI) and business intelligence (BI) solutions and nearly all of them continue to struggle with accessing their large, disparate data stores. For each AI or BI application, the enterprise must create and manage a set of policies and procedures to ensure data privacy and security is in place because most applications copy, replicate and propagate the data. This creates an increasing data governance challenge as a business becomes more digital and intelligent through the use of analytical applications.
Molecula addresses both the performance challenges of accessing large, disparate datasets and simplifies data governance. We’re excited to announce our investment in Molecula’s $6M seed round. Molecula is a Data Virtualization platform that helps enterprises make their data ready for AI. Molecula offers a Virtual Data Source (VDS) that gives users a containerized view of their data, and builds upon their open-source Pilosa distributed bitmap index technology. Molecula enables blazingly fast querying over traditional methods and simplified data security and governance because the data is not replicated as is common in today’s BI and AI applications. With Molecula, companies can drive faster decision cycles for business users as well as simplify data governance compliance. Molecula’s VDS Management System (VDSMS) facilitates the creation and administration of multiple VDS instances, reducing the complexity of managing data infrastructure to just a few lines of code and allowing users to clone, move, manage access to, and apply plugins to VDS instances.
Molecula was founded by H.O. Maycotte in Austin, Texas. The company has a strong leadership team and is already partnering with major firms such as Oracle. We’re looking forward to working with H.O. and the team at Molecula to help make enterprises more ready for artificial intelligence!
In today’s data-driven economy, we increasingly need to discover new methods to deal with substantial data and information. One company that will transform how information overload is managed is New York-based Agolo, a leading summarization platform for enterprise. Agolo’s AI engines can analyze thousands of documents daily and generate human-readable summaries in real-time. As mentioned in the “Agolo attracts Microsoft and Google funding with AI-powered summarization tools” article by TechCrunch, Agolo helps automate the process of ‘summarization’, pulling in The Associated Press, a pioneering and huge news organization, as a flagship client. Agolo is able to summarize quickly and accurately, producing an AI-powered summary that is broadcast and enterprise-quality.
Tensility is elated to announce that we are leading a $3 MM investment in Agolo, the leader in enterprise scale summarization of textual data. We are enthusiastic to work with Agolo, and our co-investors Microsoft and Google, to enable users to consume large volumes of data and information more efficiently to spend more time on higher-value business activities. Agolo fights information overload through AI-powered summarizations. Agolo has assembled the largest dataset of human-written summaries in existence to power its neural network training. Using Natural Language Processing (NLP), Agolo’s technology analyzes content, identifies different subjects, and draws connection between them.
Media companies use the product to deliver personalized summaries of topics interested to users. Voice assistant platforms can generate voice content to deploy hyper localized, listenable summaries for their customers. Similarly, financial advisors deliver news summaries customized to the stocks in clients’ portfolios. Moreover, enterprises can use Agolo to generate search-based summarization across a variety of documents in their data lake.
Agolo was founded by Sage Wohns and Mohamed Al Tantawy. We are confident their team of NLP and software engineers has the business and academic experience to tackle this complex information overload problem. Tensility is impressed by their level of expertise and we are excited to work with Agolo to address information overload.
Tensility is thrilled to announce our investment in Aegis Systems’ seed round of $2MM. We at Tensility are passionate about cyber security and making any public space a safer place. We are concerned about gun threats to innocent people. According to an FBI study of Active Shooter Incidents in the United States Between 2000 and 2013, around 60% of the shootings end before the police arrive. Unfortunately, law enforcement often receives delayed and inaccurate information. The Aegis technology can detect a gun before it is fired, unlike other technologies that detect the sound of a gunshot after being fired. We believe the Aegis software save precious minutes and has the potential to reduce the causalities by detecting firearms, providing early warnings, and improving law enforcement response.
Aegis Systems builds computer vision software using powerful neural network techniques to turn any security camera into a gun-detecting smart camera, enabling real-time response to gun violence. This system can be described as a threat detection technology. The AI software scans thousands of video feeds simultaneously and alerts building security upon a successful detection of a gun. The technology detects adjacent threats, such as intruders, left objects, and vehicles, enabling end-to-end management security without requiring additional hardware or security personnel.
Aegis was founded by Sonny Tai and Ben Ziomek. Sonny was a US Marine Corps officer, a strategy consultant, and has a deep passion to address gun violence. Ben worked at Microsoft leading engineering and data science teams to leverage Artificial Intelligence and will bring the needed product expertise. Tensility is excited to partner with Aegis Systems to drive the company forward and reduce gun violence.
Tensility is thrilled to announce that one of our Tensility Fund II portfolio companies, Health Data Link, is being acquired by Datavant, a company on a mission to connect the world’s healthcare data. This acquisition will further Health Data Link’s mission of building a reliable data sharing ecosystem to support medical research that ultimately improves patient outcomes.
Privacy laws and fragmented data make sharing of customer healthcare data between enterprises very difficult. Health Data Link (HDL) developed a unique solution to this problem, using an AI hashing function to anonymize the data, allowing it to be linkable to other sources. This makes customer data completely anonymous and gives enterprises the ability to connect data from disparate databases. It significantly cuts down on the time needed to share data, since the data is anonymized and does not have to go through time-consuming compliance processes. Through this process, researchers can perform segmentation and analyses on these datasets and drive critical breakthroughs.
HDL’s customers validated the usefulness of the product for population health studies and health economics studies. Over 40 institutions used HDL’s solution, connecting data from over 10 million patients. A leading research network, REACHnet, based in New Orleans used HDL to link data across health care institutions and payers. PRACnet, a patient-centered health plan research network collaborated with REACHnet for an antibiotic study and HDL’s technology “enabled a more accurate understanding of the effects of antibiotic utilization while helping organizations protect patient privacy.”
We were also impressed with the expertise of the cofounders – Satyender, Abel and Jasmin - in the fields of healthcare analytics and research. It has been an absolute pleasure working with the founders and the CEO, Jacob Plummer, for the past year. We are confident in their ability to help Datavant accomplish their mission of connecting the world’s health data to improve patient outcomes and we are excited to watch their progress in solving this important problem. We continue to be big believers in healthcare data and are excited to make future investments in the space.
More information about the acquisition can be found in Datavant’s Press Release here.