According to a MarketsandMarkets report, the incident response services market is estimated to grow from $13.09 billion in 2016 to $30.29 billion by 2021, at a CAGR of 18.3%. smartQED is an intelligent, AI-driven workbench for expediting IT incident response and problem resolution. It enables collaborative investigations through visual tools for systematic root cause analysis, making it easy for teams to jointly resolve problems.
smartQED was founded in June 2015 by CEO Julie Basu and COO Rishi Mukhopadhyay. Julie has a PhD in Computer Science from Stanford University, with several years of hands-on industry experience at Oracle in Databases, J2EE and Enterprise Collaboration, and in building leading-edge enterprise solutions. Over her career, she has developed several mission-critical Enterprise products and has been involved in several “War Room” scenarios to resolve complex technical problems.
Adding more people often adds to the confusion and finger-pointing that is inherent in War Rooms and even in solving less urgent problems. It is difficult to collaborate efficiently, and reusing knowledge is hard. Most importantly, issue tracking systems lack the intelligence to learn from solving prior problems. She realized the need for better and modern tools to resolve them efficiently with Artificial Intelligence (AI) and Machine Learning (ML) techniques, and thus the idea for smartQED was born.
The smartQED recommendation engine uses machine learning on prior problems and community intelligence to automatically suggest possible root causes and solutions. Julie has provided the requirements of the system, refined them based on customer conversations, and helped with detailed feature design to build an intuitive and intelligent root cause analysis system that learns and grows more effective with time.
There are three layers to its market landscape. The first layer is the Collection and Detection layer that gathers both event logs and performance data. The market leaders in this space are log analytics players like Splunk and APM tools like AppDynamics and NewRelic.
The second layer is the Issue Tracking layer, which typically involves someone from the IT operations or DevOps team who will examine the events and metrics gathered from the first layer and log the problem into issue tracking tools like JIRA, ServiceNow, or BMC Remedy. The issue will then be assigned to relevant Subject Matter Experts (SMEs) to resolve. The second layer can also include tools like Moogsoft and BigPanda that help consolidate alerts to prevent overload, and PagerDuty that routes alerts to appropriate SMEs for analysis.
The third layer is the Incident Investigation and Response layer, which has the biggest pain points and gaps in existing products. This is where a group of SMEs collaborate to solve the problem. Some competitors from the second layer have started providing offerings in this area. Moogsoft, an alert consolidation tool, has an internal case base of prior problems to match with new alerts. Symphony Summit, a cheaper alternative to ServiceNow for smaller companies, has introduced a visual root cause analysis toolset that overlaps with some tools in smartQED in terms of design. Evanios, a mix of an alert consolidator and monitoring tool, uses machine learning from ServiceNow data to suggest remediation and root cause analytics.
The main value proposition of smartQED is that it brings intelligence to the problem resolution process to help solve future incidents and significantly lowers the time to resolve complex issues, reduces outages and revenue loss, and greatly increases the productivity of IT Support and Operations teams. Its primary areas of differentiation/IP are in:
1) Collaborative root cause analysis: Its framework for collaborative root cause analysis provides the richest toolset compared to any other product in the market. This not only provides users with a better experience, but also provides its system with superior data for future solution recommendations.
2) Machine learning on user data and actions: It not only learns from existing case data but also learns from IT/DevOps team members as they collaborate to analyze and solve problems within its system. This also adds to its data quality.
3) Crowdsourcing and community intelligence: It is the only root cause analysis tool that allows customers to leverage each other’s insights and consolidates community data found on the web using advanced NLP techniques. This allows smartQED to have the largest pool of solutions available in its case base compared to its competitors, and thus enables its customers to leverage all the available knowledge effectively.
Its top target segments are Cloud and IT Service Providers and Enterprises with a large IT operation team of more than 20 people.
Most of smartQED’s customers came through its advisors. Its beachhead was a paid pilot with a global telecom provider, with whom its advisor had a prior relationship.
SmartQED expects to close $50K-$100k ARR deals from each of its pilot cloud service provider clients. The pilot clients are also expected to become key channel partners generating about $2-$3 million annually by selling the smartQED solution to their 300+ customers. Total revenue for each channel partner could range in the $6-8 million range, with 30-50% of it coming to smartQED annually.
Its current go-to-market strategy is to replicate similar pilots by targeting other cloud and IT Service Providers as channel partners. After completing pilots with four to five such partners, the company plans to also build ServiceNow and Splunk apps to leverage their platforms and sell into their established customer bases.
smartQED has both cloud and on-premise pricing plans. It has four packages: Bronze for 1-50 users, Silver for 51-100 users, Gold for 101-200 users, and a customized Platinum package for over 201 users. The cloud Bronze plan costs $35/month/user, Silver costs $30/month/user, and Gold costs $25/month/user. The on-premise annual license excluding support costs $24,000 for Bronze, $42,000 for Silver, and $72,000 for Gold.
Using a bottom-up market analysis, Rishi estimates the TAM to be $600M. The company’s average deal size per year is $10,500, $27,000, and $45,000 for small, medium, and large companies, respectively. Based on census data, there are 39,329 small companies, 11,780 medium companies, and 8,758 large companies. For the TAM, 5% small, 75% medium, and 85% large companies were considered. This translates to a TAM of $21M, $239M, and $335M, respectively or $600M in total.
Currently the company is bootstrapped with $150K that Julie had raised through four convertible notes.
smartQED is in the midst of raising a $600K seed round and plans to close by Q4 2017. Its ideal investors are seed stage funds and angel investors with domain expertise and connections to its ideal enterprise customers. Its main area of focus is to target Cloud and IT Service providers as channels to sell to their customer bases with revenue sharing agreements.
The company plans to hire in both Silicon Valley and in India through an offshore development center. It would like to build its core AI and ML components in Silicon Valley because of the higher quality of talent in latest technologies. However, since Silicon Valley talent is very expensive, it plans to open an Indian subsidiary and hire full-time employees in India.
As for the exit strategy, Rishi says that to achieve its full potential, smartQED ultimately belongs in the hands of a larger company in the area, such as ServiceNow, Splunk or Cisco. They have, in fact, talked to a Director of Marketing at ServiceNow who has confirmed that this is a white space for them and that smartQED fits very nicely with their ITOM and ITSM offerings. Other possible acquirers include BMC, SumoLogic, CA Technologies, and HP.
My assessment is that if the company can close 4-5 sizable channel deals that create an $8-$10 million ARR pipeline in the next 18-24 months, this is a relatively swift acquisition for one of the above-mentioned players. This exit could be achieved with either the $600K seed round alone, or perhaps with another small round of capital infusion.
The company targets to hit $1M ARR by the beginning of Q2 2018 and remain capital efficient.
This segment is a part in the series : 1Mby1M Incubation Radar 2017