Causely Investment Memo

Provided here is our redacted investment memorandum, detailing our rationale for investing in and leading Causely’s Seed in 2023.

COMPANY OVERVIEW

The company was founded in 2021 when Peter Bell, Managing Partner and Founder of Amity Ventures, introduced Ellen and Shmuel who had a similar vision for a next generation Service Reliability Engineering (SRE) platform. The long-term vision of the company that was shared in the earliest meetings between 645 and Causely was to build next generation Service Reliability Engineering (SRE) solutions. The ultimate goal is for users/humans (i.e. engineers) not to look at screens when solving IT infrastructure issues. Causely described a vision to build systems that actually solve the problem vs. providing another data point for humans to analyze the problem, which is done via traditional data observability platforms like Datadog, New Relic, Grafana, and Prometheus. New generation solutions use AI/ML Ops solutions; however, we haven’t come across solutions that use Casual AI.

Causal AI is different than Generative AI given that it behaves as a smart computer system that can explain why things happen. The current AI technology wave achieves accurate results by predicting the next word or pixel instead of understanding the root cause. Causal AI helps companies understand why they make certain decisions and what leads to those choices. Unlike other computer programs that can predict outcomes based on past data, Causal AI goes a step further. It figures out the real reasons behind events or behaviors, which might not be obvious from just looking at the historical data. If machines could understand causes like humans do, they could become much smarter and begin to take reliable actions autonomously.

While there are few production use cases to-date, Causal AI is being used by IBM -- where Shmuel and several members of the Causely team worked before spinning out to form Causely -- and Microsoft. IBM has been working on causal AI technologies for applications in healthcare, finance, and other industries. They have been researching methods to infer causal relationships from data and use them to make predictions and decisions. Microsoft Research has been involved in causal inference research, exploring how causal models can improve decision-making in areas like healthcare, economics, and public policy.

Causely is one of the first companies to explore the role that causal AI will play in data observability and automatic remediation of IT defects.

KEY HIGHLIGHTS

• Founder, Shmuel Kilger, is a Super Founder, a founder who has built a company that has gotten to at least $10 million in revenues or excited for over $50 million. SMARTs where Shmuel was the CTO exited for $300 million while Turbonomics exceeded $100 million in revenues and exited for $2 billion.
• Founder, Ellen Rubin, has also built and scaled companies to exit including CloudSwitch (acquired by Verizon) and ClearSky (acquired by AWS). Ellen has a resilient work ethic, values driven leadership, and is strong at building a product from 0 to 1.
• The team has already demonstrated a unique ability to recruit top talent. Several team members from Turbonomics have already followed Shmuel to Causely.
• The company is building on new technology, Casual AI, and going after a new market, automated IT defect remediation.

INDUSTRY OVERVIEW

In 2023, IT professionals like reliability engineers are more underwater than ever. As companies’ technical stacks grow more complex and IT observability tools proliferate, the amount of captured metrics and logs across environments and applications has grown exponentially.

The engineers who are responsible for monitoring these alerts, identifying patterns, and testing their solutions struggle to troubleshoot complex issues without a clear understanding of their environment. Furthermore, the increase in incidents and alerts are disrupting application delivery and can cost companies millions of dollars.

IT problems are tricky. They’re not normalized, but can often hit all at once and for great expense. Traditional APMs have fallen short here and their approaches to AI (mostly centered around correlation analysis) struggles to solve this gap on its own because it doesn’t have the ability to take root causality into account.

These problems are demonstrated by a few key data points:

• 71% of ITOps teams say monitoring data is not actionable
• 55% of IT issues are escalated, taking over 10 hours to resolve on average
• $855K is the average cost of an unplanned outage or downtime

THE NEW AGE

Causely believes in a world where remediation can be accelerated and in rare cases, even automated. The team wanted to take data from the observability tools in order to determine causality & deliver actionable defect remediation strategies via its integration with container orchestration systems like Kubernetes (K8s).

Understanding cause and effect relationships is the core value proposition of Causely. For example, a team using Causely could automatically observe when a defect and failure occurs, run root cause analysis (either manually and automatically), and seek to fix the problem at the source, allowing applications to scale without an exponential increase in observability data. A deeper explanation of how Causely achieves this can be found below in the Product Section of this memo.

COMPETITIVE LANDSCAPE

The enterprise IT observability space can be segmented into two main categories. The first category is Detection, which can be further broken down into two sub-categories: (i) monitoring products like LogicMonitor and Grafana, and (ii) observability solutions like Datadog and Honeycomb. The second enterprise category is Inference, which can be broken down into (i) analytics software like Splunk and Big Panda, and (ii) causal AI, which we’ll revisit later.

Monitoring is the oldest group. They were the first iteration of broader observability, designed for understanding the state of systems, and are best at gathering data. It is the simplest category, but necessary for the next generation of solutions.

The next step is Observability. These companies are designed to raise alerts for unusual activity, but are limited in their remediation capability. These typically provide a suite of debugging tools that allow the user to handle the alerts they deem most important.

Analytics software is correlation-driven. It provides insights into the significance of unusual activity, but doesn’t attribute causality or define actionable steps towards remediation.

Causal AI is the most advanced and fastest moving market segment. It takes a step beyond correlation and identifies defects’ root causes - a necessary qualification before automating remediation. This eliminates the need for troubleshooting and other guesswork involved with the other segments. Causely was the first of the category, but legacy players like Dynatrace have begun rebranding as causal AI platforms.

MARKET SIZING

A large number of enterprise organizations are adopting the technologies that are enabling the rise of Causely. These include enterprise observability technologies like NewRelic (9K customers), Datadog (+10K customers) , Splunk (+15K customers), New Relic (+15K customers) and others, as well as open container orchestration technologies such as Kubernetes (“K8s”).

TEAM OVERVIEW

• Ellen Rubin (Founder) -- Repeat founder, focused on IT infrastructure for the Cloud. First company she was a part of was at Natesa -- data warehousing company. Second company was CloudSwitch - connecting enterprises to the cloud and was acquired by Verizon. Third company was Clearsky data - hybrid cloud storage space and was acquired by AWS. Harvard undergrad.
• Shmuel Kilger (Founder) -- Former CTO and Co-founder of Turbonomics, acquired by IBM for $2 billion. He started the company in 2009 -- raised during the downturn. The company was focused on application resource management. Former CTO and co-founder of SMARTS, which was acquired for $300 million. PhD in math and worked for IBM Watson after getting his PhD. He started his career in 1974 working on mainframes. He has been in this space for 50 years.
• Endre Sara (Founding Engineer) -- Has a background in Electrical Engineering, holding a PhD from Stevens Institute of Technology. His career extends over nine years at Goldman Sachs, where he served as VP/Technology Specialist in Enterprise Systems Management. Endre orchestrated the creation of a global practice encompassing service management, process optimization, and operational efficiency. He oversaw the development and implementation of an integrated systems and application management platform, which spanned over 8000 servers, seamlessly covering mission-critical front-office trading applications.

Endre's worked with Shmuel at Turbonomic for 13 years as VP of Advanced Engineering. At Turbonomics, he guided the Advanced Engineering Team, he spearheaded the evolution of the Turbonomic platform. He helped lead Turbonomic's expansion into managing converged infrastructure, public cloud, and cloud-native environments. From the company's earliest days, Endre played a role in forging partnerships with industry giants like Cisco, RedHat, and IBM, resulting in the fruition of OEM products.

• Enlin Xu (Founding Engineer) -- Earned a master's degree in Computer Science from Columbia University. He started his career at Turbonomic as a Software Engineer following his masters degree at Columbia. Enlin spent nearly 11 years at Turbonomics where he became the Director of Advanced Engineering. Enlin's directorial responsibilities included overseeing Turbonomic's Advanced Engineering Research initiatives, covering critical areas such as Cloud Native technologies and Network Control. His contributions extended to a notable achievement of 12 granted patents, underscoring his commitment to innovation and technological advancement.
• Christine Miller (Founding Engineer) -- Received a mechanical engineering degree from MIT, coupled with a master's degree in computer science from Columbia University. Her career began at SMARTS, where she joined as an engineer where she worked alongside Shmuel. She stayed with Turbonomics for 13-years through the company's acquisition by Dell for $300 million. Christine remained at Dell until Shmuel started his next venture, Turbonomic. Christine again joined Shmuel, serving as the Director of Engineering. She remained at Turbonomic for 13-years through the company's acquisition by IBM for an impressive $2 billion. She has joined Shmuel again at Causely as a Founding Engineer.
• Steffen Geissinger (Founding Engineer) -- Earned a Master's Degree in applied computer science from Fernuniversität Hagen. He joined SAP, where he honed his skills and expertise over a 12-year tenure as a Software Architect. Steffen then joined Blue Yonder, where he worked on Blue Yonder's collaboration with Causely as a design partner. Having witnessed the potential to create a new category, Steffen's understanding of the industry's dynamics and experience implementing the software made him a great addition to the Causely team.