Our primary focus in the near term includes emphasising on GenAI: Kaushik Mukherjee, Head of Raptorise

Kaushik Mukherjee

In a recent exclusive interview with CRN India, Kaushik Mukherjee, Head of Raptorise, sheds light on the journey of Raptorise thus far and its mission to revolutionise the digital landscape with pragmatic tools. Mukherjee delves into the distinct features of Raptorise’s flagship products, Percept Insight and Pixel, highlighting their unique capabilities and market traction. He also elucidates on Raptorise’s commitment to enhancing efficiency, productivity, and quality across diverse industries, catering to the evolving needs of organisations at different stages of growth.

What can you tell us about Raptorise’s journey so far?

We launched Percept Insight in September last year. Since then, we have gone ahead and also launched a new product called Pixel, which is an end-to-end image solution and CDN. We put it on the market, and it became the product of the day, and we’ve seen a significant amount of traction on Pixel as well. Now, Percept Insight is a very, very different product compared to Pixel. Percept Insight is a top-down product, focusing on data—where is my data going, how am I making sense of the data, how am I driving actionables from the data. Pixel, on the other hand, is a bottom-up product; it’s very simple for people to onboard and helps even small agencies. 

The go-to-market (GTM) strategies for these products have been very different. Since its launch, Pixel has been blazing, with close to 550 accounts in just four months. Percept Insight, on the other hand, churns close to a billion events, with large organisations already on board. These two products cater to a spectrum of different needs, and it’s been very exciting.

Regarding our broader vision and strategy of empowering enterprises, democratising data is the core tenet of Percept Insight. Usually, within organisations, different parts work with different tools and approaches—some rely on intuition, some use advanced analytics tools, while others stick to spreadsheets. This creates a heterogeneous metric language where people might talk about the same problem but in different terms, leading to fragmented or even conflicting understandings. 

Percept Insight aims to democratise the perception across the organisation, acting as a control tower where everyone has a similar view of the organisation’s growth, enabling them to make the right decisions. Traditionally, the SaaS industry has been fragmented, with tools solving specific problems well, but in isolation. For example, in e-commerce, an issue like a flood in Chennai causing warehouse shutdowns can create a ripple effect impacting order volumes elsewhere. The problem isn’t isolated; it’s interconnected.

Percept Insight preserves the context and provides an end-to-end view of data, offering significant cost benefits by reducing the need for multiple tools and creating substantial business impact through timely actions. For example, current tools may analyse customer behaviour and then use separate engagement tools to act on those insights, but delays can make the actions ineffective. Percept Insight retains context and offers a seamless, integrated approach.

When we present this to e-commerce and fintech companies, it resonates because they see the value in maintaining context and having a holistic view of their operations, enabling more effective and timely actions.

What is the USP of the Percept Insight, and how does it enhance efficiency and productivity across different industries?

So, the USP really is the fact that it actually preserves context and it’s a full-stack data solution. So, just imagine that you have a data layer sitting underneath. On top of that, you generate various kinds of insights. Then, you have constructs that enable you to engage with customers or your team. For example, you might communicate internally that a particular product is likely to go out of stock soon, or notify about an error encountered when trying to get the product out of stock.

These communications, whether internal or external, sit atop the data layer and insights. You can build models on top of this, such as identifying clusters that might be fraudulent or those with a high propensity to buy. Essentially, you’ve created a data layer, then built insights on top, followed by actionables, and finally, various intelligent models. These include data models and machine learning, though not necessarily AI, which provide the capability to make informed decisions.

So, the USP really is that it preserves context end-to-end, and you can accomplish most tasks with a single team.

How does Raptorise ensure that its pragmatic tools contribute to increasing efficiency, productivity, and quality in the digital ecosystem, considering the varying needs of organisations at different stages of scale, from startups to larger enterprises?

The mission of Raptorise revolves around making a meaningful contribution to the digital landscape by crafting pragmatic tools. The emphasis on pragmatism is crucial, as these tools aim to enhance efficiency, productivity, and the overall quality of the digital ecosystem. This concept is rooted in real-life challenges encountered by organisations across different stages of their growth. Startups, for instance, often grapple with efficiency-related hurdles, while larger organisations prioritise maintaining quality and productivity amidst increasing complexity and scale.

As companies expand, ensuring sustained productivity becomes very important, considering the added complexity that comes with growth. This involves managing not only increased traffic but also upholding consistent end-user experiences. Whether it’s maintaining performance amid surges in traffic or ensuring a seamless experience despite growing complexity, these are the core challenges that Raptorise seeks to address.

Our mission, therefore, is to develop practical solutions tailored to these needs. This mission underscores our commitment to delivering products that are not only effective but also aligned with the diverse requirements of organisations. Whether it’s Percept Insight or Pixel, our products bear the hallmark of this pragmatic approach, ensuring they resonate with our overarching mission.

Can you share any success stories or case studies that highlight the tangible benefits clients have experienced using Percept Insight?

The tangible benefits that have been planned since the concept of referral cards are quite noteworthy. To elaborate, this company ran a campaign where if you referred a friend, both of you would receive 100 rupees. However, there was a catch—you would only get those 100 rupees if you came and scratched the card. They wouldn’t automatically credit your wallet. This was the premise.

What the company noticed was that, while the acknowledgement of the 100 rupees reward had already happened because I referred you, for example, you did not know that you had to scratch the card. As a result, you didn’t see anything in your wallet and maybe you didn’t make a purchase. So, what the company did with this insight was to implement hooks to track whether someone who had received a referral actually scratched the card. If they noticed that someone hadn’t scratched the card within five minutes, they would send a WhatsApp message. This increased the scratch ratio by almost 75%. Consequently, 75% more users now had 100 rupees in their wallets, which in turn improved conversion rates by close to 30% – 35% because people could use that 100 rupees to make purchases.

Another example is a company using us for inventory management. They had fast-moving products, and we provided an anomaly detector, an ML model integrated into the platform. This detector helps identify when an item is selling at an unusual rate. Typically, a product might sell 15 or 20 times a day, but if it’s trending higher, it could mean the product is about to go out of stock, leading to potential revenue loss.

There’s also a fintech company that provides loans and serves customers beyond tier-one cities, including tier-two cities. Many customers complained that the app was slow or didn’t work properly. Initially, their engineers looked at API response times and page load times, which seemed fine on average. They assumed customers were complaining without cause. However, by introducing network jitter analysis, they discovered that while average response times looked good, issues were prevalent in specific tier-two cities due to local network problems. This insight allowed them to pinpoint and address the slow network areas.

Do you have any upcoming partnerships or collaborations?

To enhance the capabilities of our business degrees, we’re currently in discussions with several potential partners, primarily integrators and brands, to explore possible synergies. We’ve received numerous inquiries regarding our communication and engagement modules, indicating a strong interest in collaboration. While we have integrations with service providers like Gupshup and Yellow Messenger, we haven’t formalised any explicit partnerships yet. This is mainly because our platform is full-stack, and we would only seek partnerships if there is a clear distribution need. We are open to exploring such opportunities and are evaluating potential partners from a distribution perspective.

In terms of expansion and growth, Pixel enjoys a global audience with significant traction in the U.S., Germany, France, and other European markets. Conversely, Percept Insight has predominantly focused on the Indian market, which is vast and still holds tremendous potential. However, we are now exploring opportunities in the U.S. and Southeast Asian markets. Percept Insight has established a presence in Singapore and is actively targeting Southeast Asia while also beginning to mine the U.S. market.

Regarding the scale of companies using our products, it ranges widely. We collaborate with early-stage startups and have partnerships with various venture funds that support these startups. On the other end of the spectrum, we work with large organisations that have daily active users exceeding a million and monthly active users ranging from 15 to 20 million, scaling up to 200 million. This diverse clientele includes companies of various sizes and stages, reflecting the broad applicability and appeal of our products.

What is the traction of GenAI in this industry?

Yeah, that’s a great question. So, when it comes to Percept Insight, we’re currently exploring conversational analytics as a potential avenue. However, our primary focus right now is on preserving context and delivering value to organisations. We did initially start with conversational analytics, but we soon realised that our immediate priority lies in addressing the challenges associated with context preservation and value delivery.

Moving forward, our roadmap includes delving into unstructured data analysis. We aim to leverage conversational analytics to decipher information from diverse sources such as PDF files and images. This represents a crucial area of focus for us.

On the pixel side of things, we’ve already made strides with features like auto caption and tag generation for images. These capabilities are particularly beneficial for digital asset management companies dealing with vast catalogues of images. They streamline the process of image discovery and search, enhancing productivity significantly.

Additionally, we’re exploring the use of generative AI to create various backgrounds, providing contextual relevance, especially for e-commerce companies. These efforts are geared toward increasing productivity across various domains.

We’re also exploring interventions aimed at enhancing engagement and boosting conversions through our Percept Insight Engagement module, leveraging generative AI techniques. This represents another key area of focus for us as we continue to evolve and innovate in the realm of analytics and engagement.

Do you have a specific focus or idea for the next six to 12 months, particularly concerning GenAI?

Yeah, absolutely. So, there’s a significant emphasis on the image aspect, especially concerning generative AI. Additionally, we’re directing our attention towards actionable insights derived from unstructured data. These areas are our primary focus in the near term.


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