Beginning of the year, we were all set to start a new phase of Eugenie by setting up its global operation in California. Just when we were on the verge of bagging a few strategic contracts, the pandemic broke out. Few of our previous plans around our products and operations took back-seat. During Covid, other than its devastating impact on human lives and livelihoods, we have experienced a change in focus across the industry for digitization that enables remote monitoring and resolution/execution of critical operations. More digitization means more data, leading to more complexity in analyzing that data for making insights actionable. Seeing this, we decided to build a new product line in Eugenie that focuses on handing real-time data to extract actionable operational insights for the asset-heavy industry. In 2020, we have built Papillion for AI-driven operational intelligence with IIOT data. Fortunately, we got opportunities to work with many major Indian and international industry players in Oil and Gas, Manufacturing, etc. These experiences not only helped us to understand the product-market fit of our product, but it also helped us to learn new market need states and opportunities.
In this article, we would like to share a few such lessons with our readers.
Getting the right data is still a challenge
For any AI-based system data is the most important and critical input for the efficacy of the insights – “garbage in, garbage out“. Despite the stress on industrial digital transformation, data consolidation, data cleansing, and data preparation are still the bottleneck in any modern IT system. In most cases, data is unstructured, duplicated, and fragmented. In Eugenie, we have developed an auto-data consolidation module that can detect gaps in the data and consolidate them using some strategies. This feature helped us to drastically reduce the timeline for the data preparation phase from weeks to hours.
User-centric enterprise SaaS product is the real need of the hour
The low adoption rate of enterprise AI platforms motivated us to understand better the user pain points. Before building the product, we wanted to understand the unmet needs of end-users. We conducted a user-research experiment by speaking to various people involved in day-to-day operations of manufacturing/asset-intensive industries. We tried to understand their inherent motivation or challenge to use technology in their daily operations. In this process, we have realized that for an AI product to truly make a difference data, algorithms, and usability aspects need to have equal priorities. We spent a considerable amount of time understanding implicit and explicit triggers and benefits for the users to use a tool like Eugenie. This approach helped us in realizing how important it is to bring transparency, traceability to the AI algorithms that we have today in Eugenie. Based on these discoveries we have tailor-made our product roadmap to make it more aligned towards addressing user-needs rather than a dump of functionalities.
Eugenie’s user research product roadmap
Explainable AI for bridging the gap between users and technology
For creating a product that users can truly own, we have taken the following steps to ensure a top-notch quality:
• Easy onboarding of users and assets/processes using intuitive visual workflows.
• Augmenting IIoT sensor data with acoustic and video sensory data.
• Better scope of knowledge management by extracting knowledge from the past incident, storing them to be used as a reference point for the future.
• Framework (Spot, explore, exploit) of explainable AI for traceability and transparency.
• Helping users to build the remediation solution and help them in the process of building the solution.
• Making the consumption of the insights easy and actionable.
Our people – our main asset
To make Eugenie a global-scale company, with products that solve real-life-complex problems for industries, we needed to build a team with the right mixture of skill and passion. We have hired the best talent in the industry who are experts in technology stack such as deep learning, anomaly detection. We also have people with experience in the process industry who can bring the right domain knowledge into the product. At Eugenie, we welcome people with diverse backgrounds and passion to join our team. The biggest motivation for us to see the difference our product is making for our customers in terms of improving operational reliability with the right automation and knowledge.
Planet size impact – Sustainability is everyone’s concern
Carbon emission by the process industry is an accepted fact. The impact of which we see in our daily lives in the form of climate change, natural calamities, global warming, etc.
Sustainability is not just a political issue – it is everyone’s concern. It is time that we take a few deterministic steps towards addressing this issue. As technology experts, our vision at Eugenie involves moving towards a sustainable future by making industrial operations more efficient and reliable.
For us, operational reliability is a small but important step towards making systems and processes more eco-friendly. More efficient machines are equivalent to less carbon emission and less wastage. In Eugenie, we are committed to making the industry aware of its carbon footprint and showing them ways to reduce/optimize it without sacrificing their production throughput.
Welcoming 2021 – “Qui vivra verra”
We are all excited to welcome the year 2021. We will continue to build a great product that would create tangible business impacts for our clients and society. We are all committed to making that happen. We are looking forward to working with a few global companies to make more than billion-dollar impacts for each of them by improving their operational reliability. We will start our California operation soon. We will continue to hire the best talents, who are passionate to build products that users would love to use and adopt. If you are interested to be a part of this team, feel free to reach out to me over email at soudip[dot]chowdhury[at]eugenie[dot]ai.