Emotion Ai Is an Exponentially Growing Tech” Say Cofounders of Lightbulb.ai

Be it in advertising, marketing, or the more remote gaming industry, being able to predict user sentiment has been a prime factor in winning the deal. If businesses can track user engagement in real-time implies it knows where the business-client relationship is going and make amends thereby. Lightbulb, an emotion detection tool helps businesses leverage their proprietary ML models developed using cutting-edge technologies like computer vision, speech transcription, audio data mining, etc. Analytics Insight has engaged in an exclusive interview with the Co-Founders of Lightbulb.ai

Lightbulb is an emotion ai platform. We help remote teams understand what users are feeling & whether they are engaged in real-time, to make remote interactions effective & productive.

Computer vision, speech transcription & audio-data mining using proprietary ML models

Pre-test recorded video content with external testers to understand emotion and engagement responses.
Test presenter videos to understand potential to engage and emotions depicted.
Or test user understanding & engagement with online user experiences and touchpoints.

Spike acquisition, improve discovery, increase retention…it's all possible with Lightbulb.

1.With what mission and objectives, the company was set up? In short, tell us about your journey since the inception of the company.

The insight for Lightbulb came in late 2020 during the peak of the Covid pandemic when the founding team was relentlessly exposed to the dismal realities of online learning & work-from-home culture. It was the realization that the future of interactions has fundamentally changed which led to the creation of Lightbulb's emotion ai platform.

With a team of 7 members, including 3 founding team members, Lightbulb launched the MVP of its CVAAS-enabled emotion ai platform in Sep-21 with over 1.2 Mn faces scanned & 75% + accuracy and immediately took it to market in a series of pilots with remote learning companies to validate its various product hypotheses.

With strong positive feedback and early pilot conversions, Lightbulb started building out the product, making efforts to bring in speech transcription & sentiment analysis to add depth and nuance to insights across multiple verticals.

2.Kindly mention some of the major challenges the company has faced till now.

One of the key challenges faced by most AI / ML companies is the sourcing of comprehensive and well-balanced data sets that can support and enable high-accuracy algorithms for face, emotion, and engagement recognition, in a manner that gives due credence to data privacy concerns. One of Lightbulb's key challenges was to procure balanced data sets that span ethnicities and geographies to give highly accurate results.

3.What is your biggest USP that differentiates the company from competitors?

Lightbulb is geared to help businesses that offer remote user experiences, solve for higher engagement by mapping user emotion & engagement in real-time! From analyzing user engagement during live meetings/calls to researching the emotional impact of pre-recorded content and asynchronous user experiences of consumers, Lightbulb's key USP is offering customized & nuanced solutions that are relevant to industries such as online learning, sales enablement, and consumer research amongst others.

4.Please brief us about the products/services/solutions you provide to your customers and how they get value out of it.

Emotion Ai is a nascent yet exponentially growing technology with a wide variety of use cases across multiple industries, from remote learning, sales enablement, user-experience testing, and market & consumer research to mental health counseling.

It is important to remember that the fuel that powers emotion ai is emotion & engagement data, across geographies, ethnicities, and age groups with adequate representation from all minorities/edge cases.

5.What are the key trends driving the growth in Big Data analytics/AI/Machine Learning?

According to a recent report by NASSCOM, integrated adoption of artificial intelligence (AI) and data utilization strategy can add $500 billion to India's GDP by 2025. This is already evident as we have seen the advancement of these technologies in our existing work and learning environments. The extent of proliferation is at an exploratory stage today, as we uncover the true value of these technologies intersecting with our work and lives.

The report suggests that there are four key sectors spearheading the AI adoption – banking and financial services, consumer products and retail, healthcare, industrial and automotive, and it is said that these sectors could contribute about 60% of AI's potential value-addition of $450-500 billion to the country's GDP by 2025. However, I do believe over the next 5 years we will see industry-wide applications for these technologies across almost every sector.

We are already seeing rapid adoption of these technologies today in the eCommerce, healthcare, and automotive sectors in India, and that adoption is only likely to grow. The same report has predicted a 3-fold jump in adoption of these technologies over the last 3 years and I still think that is only the tip of the iceberg. It will be exciting to see how businesses will gear themselves to enhance processes, productivity, and customer engagement in the years to come with deep tech becoming more mainstream.

6.What are the concerns that organizations have before using Analytics?

One of the key concerns that we have seen businesses display is with reference to data privacy, especially in international markets where data privacy and user privacy are deeply regulated at the government level. We are pleased to share that we are cognizant of GDPR guidelines and in compliance with the Data Privacy act to ensure that the data of customers is protected in a highly secure manner with explicit opt-ins and user consent taken at every step.

7.Which industry verticals are you currently focusing on? And what is your go-to-market strategy for the same?

The ability of the product to be relevant across multiple (very-large) market verticals in the long-term was an indicator of how valuable our company can potentially be in the future, so our focus has been to serve different markets and not be sector specific. And the effort at our end is to continuously funnel rich streams of real-world authentic data obtained with explicit consent into the platform to create highly accurate prediction models that can lend insight in a variety of situations.

We are however focused on the initial segments of Online Learning, Consumer Research, and Sales Enablement.

8.Would you like to highlight a few use cases where analytics has benefitted the organization tremendously?

With reference to the Online Learning segment, one of the key use cases we have seen is monitoring and assessing learner engagement in live learning scenarios to ensure better learning outcomes as well as high quality of teaching.

Whereas if you look at the Consumer Research segment, where Emotion Ai is an established technology, assessing user emotion and engagement as they respond to ad and media creatives or interact with UI-UX on digital assets is a critical and high-value use case.

9.What are your growth plans for the next 12 months?

We see strong opportunities for Lightbulb in the following verticals, as part of a phased long-term expansion strategy:

• Remote learning
• Sales Enablement
• Consumer research (including UX testing)
• Mental health

The immediate imperative is to build strong value-adding offerings for the US market in 1 or 2 key verticals based on a stable emotion-ai platform and scale up to 90% accuracy levels across ethnicities and age-groups.