While the world is debating whether artificial intelligence will take away everyone’s jobs, Pune-based E42.ai is busy building enterprise-ready multi-functional AI coworkers. These AI cognitive agents are designed to work alongside human colleagues and enhance productivity in marketing, sales, customer success, human resources, IT operations, and finance.
E42.ai was founded by Animesh Samuel (CEO) and Sanjeev Menon (head of tech and products) in 2012, when the term AI, as Sanjeev jovially explains, represented Air India and not artificial intelligence and NLP meant neurolinguistic programming and not natural language processing.
Cut to 2023, E42.ai stands out for its pioneering work in building foundational AI models in India with human-level cognitive capabilities, while other companies look to the West to purchase open-sourced AI algorithms.
In a conversation with team ProdWrks, Sanjeev elucidated his perspective on what it takes to build a deep-tech AI startup in India and his vision for creating a marketplace for hiring pre-trained AI coworkers.
Inspiration from IBM Watson
Sanjeev says that they envisioned E42.ai as an open-ended R&D company from the get-go. Their problem statement was to reduce the gap between machines and humans by adding human-level cognition to systems and unlocking machine evolution.
However, in 2012, when Sanjeev and Animesh started E42, teaching machines to learn human language and cognitive capabilities had two major roadblocks – The first was the requirement of raw data to train such a model, and the second was computing power.
To solve the data challenge, the E42 team drew inspiration from IBM Watson’s appearance in Jeopardy! (TV show). The E42 team created an experimental model called Pooch – an SMS-based answering engine, kind of like a rudimentary version of ChatGPT.
The Pooch experiment, launched in 2013, went viral and attracted considerable attention, amassing 300,000 users. The E42 team handled around 100,000 queries daily, and the platform generated 30 million Q&A pairs, providing valuable data for developing E42.ai’s algorithms.
While Sanjeev had now had enough training data, he still needed raw computing power to process all the data generated and train their AI model. So, they took help from Microsoft and other cloud service providers, who gave the E42 team the resources for half a million dollars worth of computing power for research and development.
Betting on the Future
With both data challenge and processing power sorted, Sanjeev and his team developed and trained their AI models with cognitive abilities. Looking back at this crucial phase, Sanjeev says that when building an R&D-focused company in deep tech, you will not see immediate results or use cases for your research models.
Sanjeev urges founders researching and building deep tech to be patient and look for areas where technology convergence may create a user need in the future. He asks founders to look at history for inspiration, where the need for new products and solutions arose due to technology convergence.
He cited the example of the advent of Voice over Internet Protocol (VoIP) and how the integration of different media streams (audio, video, and text) revolutionized communication and gave rise to the need for various communication platforms.
Moreover, in 2012, powerful hardware resources such as advanced GPUs for computing and training AI models were also prohibitively expensive. There was a risk that it would make the solutions they were building for the future expensive, too. But Sanjeev had a contrarian view.
The Pressure to Productize
Sanjeev narrated several instances where they were pressured to productize their cognitive AI models, but they chose not to and doubled down on R&D to make their algorithms better.
One such pivotal moment was during the embryonic stages of the Pooch experiment when enterprises were ready to pay and also contribute human resources to turn the Pooch answering engine into an expert online community to answer queries from their own users and customers.
In 2018, when the market signalled a shift towards verticalized B2B solutions, E42 was again pressured to use its algorithms to offer verticalized chatbots, which were in vogue. Investors and industry experts advocated specializing in domain-specific chatbots, predicting immense demand for conversational interfaces in the future.
We have to admit, using E42’s cognitive AI models to build verticalized chatbots would have created a wealth of similar data, making their systems more intelligent within a domain like marketing or sales or finance.
But Sanjeev had other ideas that defied common wisdom and decided to flip the switch on this thought process. He argued that artificial intelligence, like human intelligence, needs to be better in language and cognitive understanding first in order to learn any domain better.
Results of Resistance
Sanjeev’s decision to resist the productization of their algorithms in 2018 and not build verticalized chatbots became even more profound with the emergence of generative AI in 2022. Open-sourced algorithms from companies like OpenAI have made it easy for anyone to create a chatbot and would have made any similar solution that E42 had built redundant.
Sanjeev and his team initially adopted an IT services model, building customized solutions for businesses needing cognitive abilities. By doing so, they perfected their algorithms over time.
With a solid foundation in cognitive AI models developed over time and with a clear focus on what they did not want to do, E42.ai finally began the challenging yet crucial phase of productization in the latter half of 2018, six years after they started their R&D journey.
The transition to a market-ready product arose from a challenge from Tata Communications, which recognized the potential of E42 cognitive AI systems and tasked them to create an AI marketing assistant for their internal use cases.
The rest, as they say, is history. Within two days, the E42 team, which had only 15 members at this point, was able to build a POC and nabbed Tata Communications as their first customer. What’s more impressive is that they beat the IBM Watson team, who practically had unlimited resources at their disposal, to clinch the deal.
Cut to 2023, E42.ai has a team of 90+ people and 60+ clients, including major names like Accenture, Mahindra, Tata Communication, Piramal Group, and Aditya Birla. Their anticipated license-only revenues for the year is $3 million.
Sanjeev is confident of the robust technology layer and foundational algorithms they have built, which he believes is on par with the big tech, and it is one of the reasons enterprises love E42’s products today.
For instance, Mahindra and Mahindra use an E42’s AI coworker created on the platform called Mahindra Genie to assist their HR teams. Genie is a single point of contact for all Mahindra employees to get answers to queries on their policies, procedures, and leave transactions. It also has a performance improvement mechanism built on top. Even queries related to IT systems or IT incidents are handled by this single AI coworker from E42.
E42's Next Big Bets
As E42.ai continues its march forward, Sanjeev discusses the operational expansion into the US and Europe. He unveils two significant upcoming features: a marketplace for hiring AI workers and exploring social value alignment among AI workers.
Sanjeev says there is a long way to go to realize this model and invites ecosystem partners to identify unique problem statements to train these coworkers, place them for hire in their platform, and get a licencing fee in return.
He also believes that AI coworkers will evolve to include social value alignment, and the E42 team is already involved in deep research on the subject.