In June 2016 we sat down with the CEO and Founder of App Orchid, a Silicon Valley-based software developer specialising in the science of Articifical Intelligence and Cognitive Computing.
Hi Krishna, thanks for taking the time to talk to us. You're the CEO and Founder of App Orchid: can you tell us a bit about your journey and background?
KK: I started my career over 20 years ago as a SAP consultant at enterprises like Siemens, SAP and Hitachi. Then I founded my first startup, Space-Time Insight, and in 10 years I built the product, acquired customers and hired and coached the management team, including the CEO, resulting in a 200-person company with over £50 million in venture capital. We pioneered situational intelligence, with applications in the control rooms of some of the largest energy companies in North America. I started App Orchid to be an industry leader in Internet of Things (IoT) and Artificial Intelligence (AI). In three years, we have acquired several customers, have a presence in three continents, and triple-digit quarter-over-quarter growth. We are currently serving three markets - Energy, Insurance, and Healthcare.
What's the biggest lesson you've learned since founding App Orchid?
KK: As a startup, you deal with challenges ranging from optimising usage or team resources, to managing financials while acquiring customers and priming the pipeline. You need a first, well-known customer who will pilot your product ideally for a price - ours was a large national grid. We had a visionary solution so we could piggyback existing budgets and systems to promote our analytical offerings. We couldn't have done that with just a product. I had five very smart consultants who were with me - I would go there twice a week to get things going and then would use the other consultants to implement what had been sold. That way, I could build revenue while building the platform.
What challenges are your clients facing? What are their business needs?
KK: Issues like aging workforces and aging assets have forced organisations to examine integrating employees' tribal knowledge with other structured and unstructured data. Blending and querying all this disparate data should be no more complicated than asking a question in Google, making the identification of patterns, risks, and opportunities much easier than it has been using traditional analytical tools. Additionally, utilities want to break the barriers between data silos such as SCADA EMS, DMS CIS etc., and integrate structured data with unstructured data - from the internet, social media, news and weather; as well as reports, emails, evaluations, presentations, and observations.
How do Machine Learning and Artificial Intelligence address those drivers?
KK: Typically, structured data comes in the form of vast quantities of digital multi-dimensional sources. Converting structured data into actionable information requires the use of in-memory technology. A majority of the information in unstructured data is converted into "tribal knowledge" which is easily lost when people leave the workforce. Converting unstructured data into actionable information requires the use of cognitive computing. Integrating the information embedded in both structured and unstructured data sources has not been possible until now. The failure to integrate information from both unstructured data sources that typically reside across the Internet of Things can lead to safety issues, compliance penalties, reduced profits and customer dissatisfaction. At best, the failure the integrate results in inefficiencies, at worst, catastrophic failures.
What benefits and opportunities to these techniques unlock?
KK: We're harnessing advances across In-Memory Processing, Machine Learning, Artifical Intelligence and Natural Language Processing to blend millions of data points - from tribal knowledge, operational systems, and the Internet of Things - into a new generation of multi-device smart grid apps across the enterprise value chain. Analysts can now develop powerful business apps and solutions with minimum IT oversight and governance. By combining unstructured data like emails, maintenance logs, memos and compliance regulations with IoT data from smart meters and EMS along with asset ledgers, utilities can now have a 360-degree view of all data points that influence the state of their assets, provide insight into risks and profit leaks that were previously thought impossible. According to a Gartner Analyst who nominated us for 2016 Cool Vendor, "App Orchid is cool because the solution combines multiple cutting-edge technologies to produce accurate, non-obvious replies and recommendations to natural language queries. And these results can be informed by "dark data" such as source files that have not yet been normalised into EAM or other enterprise systems.
What other new emerging technologies do you see on the horizon?
- Cloud Computing enables us to easily use software as well as processing platforms and computing infrastructure (that are not equipped on our computers and smartphones) from any location through Internet services).
- Big Data provides us with new intelligence from massive data sets, which can help in situation/condition/status analysis and decision making.
- The field of Artificial Intelligence is advancing rapidly along a range of fronts. Recent years have seen dramatic improvements in AI applications like image and speed recognition, autonomous robotics, and game playing; these applications have been driven in turn by advances in areas such as neural networks.