Background
Amplify.ai is an AI-powered platform with expertise in increasing customer engagement via messengers on Facebook and Instagram. It deploys feature-rich bots to ensure actionable outcomes using true-to-life and persistent conversations.
Amplify.ai is the founding duo’s second startup. Previously, they collaborated with Talentica Software for their first startup, AdMarvel, to develop a mobile advertising platform. Its success inspired a fresh teaming up for building an MVP and then validating product-market-fit.
Challenge
Amplify.ai came to us with a vision of improving brand engagement with customers by leveraging social media channels. Turning their idea into an MVP quickly was not the only challenge.
Iterating to find the best product-market-fit (Build – Measure – Learn)
The initial product got attention from brands and they started demanding for different functionalities to make it useful. Hence iterating the initial product became crucial to find out the best product-market-fit.
Solution
Iteration 1 — BUILDING A MESSENGER BOT to increase brand engagement on social media channels
BUILD: Amplify.ai wanted to get its clients’ customers engaged to their brand content on Facebook and Instagram. So, together we built a messenger bot that allows page-owners to send updates to followers and engage more customers.
MEASURE: Brand owners realized that content sent via messenger bot increased their reach. But the user engagement rate was not encouraging and it was difficult to measure ROI.
LEARN: From end user feedback, they realized that the content had to be personalized based on received data patterns or triggers from individual users.
Iteration 2 — PERSONALIZING CONTENT on messenger bots using analytics-based notifications
BUILD: We built a solution using machine learning to identify the right triggers based on customer behavior and designed notifications around it. However, their brand presence on social media was getting affected by other obstacles.
MEASURE: Online retailers leveraged this new design to improve their customer influx and customer experience. But responding manually to each comment/feedback was not a feasible option.
LEARN: From the user feedback, they got to know that the user’s decision to buy was getting affected by reviews and cross-promotion.
Iteration 3 — MODERATING CONTENT on social media using Natural Language Processing (NLP)
BUILD: Amplify.ai found out that Natural Language Processing (NLP) could be used to moderate content. We implemented algorithms to back the idea & our UI/UX experts developed a practical, user-friendly interface for it.
MEASURE: The end product sieved social media contents efficiently to prevent unwanted interference for brands. Brand owners were happy with the accuracy rate that our automation introduced.
LEARN: Several brand owners reported that using this NLP, they could manage the brand sentiment and maintain brand equity. With these features, Amplify.ai got enough paying customers to achieve product-market-fit.
Result
Achieved Product-Market-Fit
Pivoting was essential to validate the idea across different markets. It helped to make the initial product suitable for a larger customer base and acquire enough paying customers.
Raised Funding in Seed Round
After the initial success in achieving product-market-fit, they raised a seed round of funding.
Exited Through Successful Acquisition
Triller acquired Amplify.ai as it saw potential in the product’s versatility. Its ability to adapt quickly and the synergy with their existing products acted as decisive factors as well.
With Talentica, we got an engineering team that could bring the agility and adaptability required for quick turnaround and the ability to work with a startup’s ambiguity. Their startup-centric processes and technology breadth across skills like AI & Machine Learning, UX, and DevOps helped us achieve product-market fit.
CO-FOUNDER & CTO, AMPLIFY.AI
Services Provided
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Artificial Intelligence
- Machine Learning
- Natural Language Processing
- Conversational Bots
- Product Engineering
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