Amygda, Faizan Patankar: We are transforming the traditional way of doing predictive maintenance in the industry

Faizan Patankar, CEO and co-founder of Amygda, for South-East European Industrial Market

Amygda is a young company but has already gained valuable market experience. Would you please tell us more about your journey so far - the business idea, the team and the goals?

Our journey so far has been rewarding and exciting. The business idea of Amygda came about when the company´s co-founder and I were still working at Rolls-Royce. We could see the proliferation of data in transport industries like aerospace and rail. We realized that it was a field that was going to grow and we felt that there were changes that could be done to the industry. Thus we took action and initiated those changes ourselves.

At Amygda we utilise machine data and try to decipher why a machine is acting a certain way and how it is most likely to respond in different situations, thus helping in decision making for the maintenance teams behind these machines. We are good at making sense of maintenance data. Since we started we have come a long way with new customers and investors, external validation and also new team members.

We have people in the company that are experts, but also members who don’t carry the baggage of experience. Not knowing the risk of failing and experimenting is where we will find the next breakthrough. We live by simple principles in our team. Speed is our friend and we are biased toward action.

What are the features, advantages and key potential applications of your predictive analytics platform? How are digitalization and state-of-the-art innovations like AI and machine learning transforming traditional industries?

Current state-of-the-art technology includes time-based maintenance according to the number of miles covered by the asset, irrespective of the asset condition. Some data is used to build rule-based thresholds that notify once damage has propagated. Via our high-frequency analytics platform, Amygda processes existing sensor data from assets (temperature, notch, fuel, oil, vibration, etc.) using unsupervised learning to create data-driven patterns of breakdowns. New data is classified in real time against the extracted patterns, for different maintenance actions. Maintenance teams receive an alert and suggest actions as an output, enabling faster root cause analysis. A proof-of-concept that Amygda conducted with a rolling stock company found savings of GBP 350 000 per annum on a fleet of just 200 vehicles.

Amygda is transforming the traditional way of doing predictive maintenance in the industry. Our AI technology can identify known and unknown faults ten times faster. We have an explainable AI system from day 0 and our models are autonomously learning customer behaviour, so the maintenance team needs minimum input into the process.

Which are the highlights of the platform´s first practical implementations and what feedback do you get from your customers? How do you plan to further expand your customers and services portfolio?

We have been working with Boeing, Leonardo and some other companies. So far the feedback has been amazing. Through our customers we know that there is a need for our product in the market, we can help provide different levels of data-driven business models to improve customer experience and add value for the users, and also keep expanding our market to different countries.

Our sales model includes working with established go-to-market partners who have a need for our solution and can shorten the sales cycle. The industrial AI sector is still maturing and most of the time customers rely on trusted networks and people, like us, to help them narrow the solutions. Go-to-market partners are key to our plans to get the product out on customer sites.

What challenges did you have to overcome so far? What place does Amygda´s technology hub in Sofia have in your further development strategy?

Challenges are inevitable, and we’ve had our share of them. We started Amygda in the middle of a pandemic and we entered an industry where decisions take months, not days. We knew our clients would take time, and the pandemic lengthened that time, but the goal was to help the customers, not just ourselves. We are focusing on the change that we have the potential to bring to the industry. If it was easy, anyone could do it.

When we established our company, travel restrictions were at their highest because of the pandemic. It’s now starting to look better and we had our first team offsite in Sofia earlier this year, in July. As a remote-first company, we are still virtual. Our plans to co-locate together in a physical office are for 2023, most likely in Sofia and Derby in the UK, the birthplace of Rolls-Royce.

Our readers would find interesting the concept of servitization and the way it applies to industry and manufacturing in particular. What solutions in this segment do you intend to develop in future?

The concept of servitization is deeply incorporated in our work. At Amygda we see servitization as a business model to transform products into intelligent and connected solutions, providing new services that enhance customer value and satisfaction. To elaborate on it, servitization means focusing on digital transformation by leveraging the data generated from intelligent assets, and that is the heart of what Amygda actually does. We enable companies to smoothly integrate servitization as part of their offering to the customer through data-driven solutions, be it in the aerospace, rail or any other industry.

Servitization is also a great way to add sustainability to the operations. When you don’t focus on constantly producing new units, but rather extending the life of existing assets, you are really reducing the embedded carbon footprint. Today this challenge is more accepted and transformations like servitization have a huge part to play.
Amygda enables servitization for engineering companies who manufacture or lease products. For transport and asset-intensive industries we are always happy to talk about reducing maintenance costs, optimising operations, and reducing carbon footprint of assets.