Gautam Kanumuru, CEO & Co-Founder of Yogi – Interview Series

You previously worked on NLP and Cortana at Microsoft, then helped lead Clarke.AI through an acquisition. What motivated you to start Yogi, and how did your background shape the company’s mission?

What drew me to start Yogi was really the potential of natural language processing. At Microsoft and Clarke.AI, I saw firsthand how a relatively small improvement in NLP—say, a 5–10% increase in performance—could unlock hundreds of downstream use cases. But I also noticed a gap between what looked impressive in a demo and what actually delivered real value to customers. With Yogi, we set out to close that gap. We wanted to build something that could make a tangible, visible impact, like changes to a product on a store shelf that you can trace back to insights generated by our platform.

In the early days of Yogi, what was the biggest hurdle in getting consumer brands to trust AI for something as nuanced as customer sentiment?

Skepticism came from two places: one was the technology itself, and the other was us being a small company talking to large enterprises. We learned quickly that it’s not enough to talk about what your product can do, you have to show it. That meant offering to analyze samples before being asked, answering real business questions on the spot, and always delivering value from day one. And we also made it clear that we’d work through any issues post-adoption. That kind of reliability mattered.

Yogi uses AI and NLP to extract sentiment from product reviews. Can you walk us through how your platform translates raw shopper feedback into granular, actionable insights?

We think of it in three stages: aggregate, organize, and analyze. First, we aggregate customer feedback from multiple channels: reviews, surveys, support tickets, and ensure it's tied accurately to the right product, SKU, and retailer. That’s harder than it sounds. For instance, the same product might have slightly different listings across multiple sites.

Then we organize the data. Here’s where our second layer of AI reads the feedback the way a human would. It identifies what topics are being discussed, how they’re being described, and with what sentiment without relying solely on keywords.

Finally, we analyze. This is where we present insights to our users through a highly interactive interface. Our latest tools even let users type complex questions like, “How have I been performing against my three competitors over the past year?” and get an answer within seconds.

What differentiates Yogi’s AI models from generic sentiment analysis tools? Are there specific techniques that help you capture nuance in consumer feedback?

Yogi is like the PhD level graduate of consumer feedback. Generic models, even advanced ones like ChatGPT, are like really smart undergrads: they know a bit about everything. We’ve fine-tuned our models specifically for this space, using our own dataset and extensive preprocessing. Because we add layers of structure, like sentiment, topics, and product mapping, we provide rich context around every piece of text the model evaluates.

Many AI platforms struggle with context or sarcasm in customer reviews. How does Yogi address the challenges of unstructured, emotionally complex data?

We tackle it through ongoing training and user input. Our model improves by continuously ingesting examples of sarcasm, ambiguity, or evolving slang. We also allow users to flag problematic interpretations, which we can then feed back into our training process. This fine-tuning doesn’t require millions of examples, just a handful of targeted ones can meaningfully improve performance.

How does Yogi help companies detect product-level issues, track sentiment changes, and react in real-time? Can you share a success story?

Absolutely. Broadly, we see a multitude of use cases but common three categories. First, product innovation: companies use us to explore new categories and identify unmet needs before launching a new product. We’ve had clients begin using Yogi two years ahead of a product release to shape everything from formulation to packaging.

Second, product quality: if a team changes a component—say, a part in a coffee machine—they can track post-release sentiment to see if complaints spike. This applies across sectors, including beauty, food, and electronics.

Third, strategic analysis: we’ve seen brands use Yogi to evaluate potential acquisitions by analyzing consumer feedback on target products. It's a layer of due diligence they didn’t have access to before.

Yogi is now being used to optimize PDPs, align marketing messaging, and even track shipment issues. How has the product evolved to support so many workflows?

It’s all customer-driven. We believe consumer feedback is relevant to every team in a company from product to sales to support. So when we see our users pulling insights for a new purpose, we ask: can Yogi adapt to support that use case natively? That’s how we’ve grown. We didn’t build for marketing or supply chain originally, but those teams saw value and asked for features. We listened.

How do you help companies monitor competitors and detect disruptors before they become threats?

We leverage public sources like reviews and ratings to track competing products in real-time. Our platform uses alerts and an “insights feed” to flag when something unusual happens, like a spike in positive sentiment for a competitor’s product. Our customers don’t need to monitor everything manually. Yogi is constantly scanning and will surface anything notable without being prompted.

Where do you see the future of AI-driven consumer insights heading in the next 3 to 5 years, and what role will Yogi play in that landscape?

Two key shifts are coming. First is automation. Tasks that once took weeks, like compiling a competitor comparison, are being reduced to hours or even minutes. Soon, a user might ask Yogi a question and get back a fully formatted report or slide deck.

Second is the emergence of new types of analysis. AI will enable quick, iterative investigations that were once too expensive or time-consuming, like ad-hoc focus group–style insights from public data. We believe Yogi is well-positioned to lead on both fronts: speeding up research and enabling entirely new workflows.

Tack för den fina intervjun, läsare som vill veta mer bör besöka Yogi.