{"id":19640,"date":"2024-03-13T04:08:35","date_gmt":"2024-03-13T04:08:35","guid":{"rendered":"https:\/\/dev.matsio.com\/matsio\/saasboomi\/?p=19640"},"modified":"2024-07-03T06:27:36","modified_gmt":"2024-07-03T06:27:36","slug":"steering-product-through-gen-ai-disruption","status":"publish","type":"post","link":"https:\/\/dev.matsio.com\/matsio\/saasboomi\/saas\/growth\/steering-product-through-gen-ai-disruption\/","title":{"rendered":"Steering a product through GenAI disruption"},"content":{"rendered":"\n

\u201cWe will be ready to sacrifice our product if required, because we have to consciously call out that we will be an innovative company,\u201d says Amit Srivastava, CTO and co-founder of Constems-AI.<\/p>\n\n\n\n

With 20+ years of experience in charting product roadmaps across embedded and AI systems, Srivastava has become habituated to scanning the horizon for new technologies. He\u2019s constantly asking himself, \u201cIs there a better and faster way to do things?\u201d But that question took on a new dimension over the past year.<\/p>\n\n\n\n

It\u2019s a dilemma every product development head faces. On the one hand, you\u2019re trying to innovate and adopt the latest and greatest technologies. On the other hand, you have to focus on delivering a robust tried-and-tested product to customers.<\/p>\n\n\n\n

But for Constems-AI, the challenge doubled with the GenAI wave. As an early mover in using computer vision for retail analytics, the seven-year-old NCR-based startup had developed deep expertise in convolutional neural network models. Now the rapid advances in the transformer architecture of generative AI offers a path to higher levels of performance.<\/p>\n\n\n\n

\u201cWith the convolutional neural network, you will hit a ceiling. The transformer model can go to the next level,\u201d says Srivastava.<\/p>\n\n\n\n

Parallel teams<\/h2>\n\n\n\n

It seems a no-brainer then to jump to the new architecture. The dilemma is that the incumbent models\u2019 accuracy has been refined over years of training. To be able to do that with the transformer model will take time, and customers just want a product that\u2019s reliable – most of them don\u2019t care what\u2019s under the hood. On the other hand, knowing that the transformer model\u2019s performance will inevitably outdo standard deep learning models, the startup has to reinvent its product at some point.<\/p>\n\n\n\n

\n

\u201cToday you are at a point where the accuracy for both is comparable. But tomorrow there is a potential for the transformer to actually give you much more accuracy with many more images.\u201d<\/p>\n<\/blockquote>\n\n\n\n

The workaround Srivastava came up with is parallel teams. One of them is migrating the product to the transformer models, while the other one is continuing on the product development road<\/a> charted earlier with the deep neural network models. It\u2019s not an ideal scenario because it requires more manpower, higher expenditure, and divided attention in the short term. But for Constems-AI, it was a necessary band-aid in a disruptive environment.<\/p>\n\n\n\n

\u201cI\u2019ve been experimenting with the transformer model closely. Today I see that the deep neural network and transformer are neck-to-neck,\u201d says Srivastava. \u201cThe question is when to make the switch. That is why we\u2019re building things in parallel, which is taking some of our bandwidth.\u201d<\/p>\n\n\n\n

At the same time, it\u2019s an exciting inflection point. \u201cGoing forward, we will be able to generate more value to the customer in a shorter time. That’s how I see the coming new wave,\u201d says Srivastava.<\/p>\n\n\n\n

Computer vision in retail<\/h2>\n\n\n\n

Before becoming an entrepreneur, Srivastava had worked at the confluence of software and hardware at the multinational semiconductor tech company STMicroelectronics for 16 years. It was at an IIM Lucknow incubation programme that he met his co-founder, Amit Singh, who had deep experience in the retail business.<\/p>\n\n\n\n

They decided to apply the emerging tech of computer vision to retail analytics, beginning with a motorcycle maker\u2019s display at a pavilion. It expanded to helping FMCG brands derive quick insights on their presence on retail store shelves.<\/p>\n\n\n\n

The startup ran into multiple problems in training its AI models for accuracy. Relying on smartphone images, for example, it had to find ways to standardize the quality of images for the AI. Some agents took pictures of the whole store instead of specific shelf spaces, and some inserted their selfies as proof of presence. But with training of humans alongside AI, the system soon became much faster and more accurate than earlier manual methods of collecting and processing visual data from stores.<\/p>\n\n\n\n

Eventually, its product became robust enough to be used by leading brands in India and Southeast Asia. The startup\u2019s revenue grew four-fold to cross $1M ARR last year, says Srivastava. It is also foraying into new domains like Industry 4.0, with computer vision and AI analytics applied to quality control inspections.<\/p>\n\n\n\n

Widening sales funnel<\/h2>\n\n\n\n

\u201cBecause of this new wave, our sales funnel is growing. It is widening because more people are aware of AI,\u201d says Srivastava.<\/p>\n\n\n\n

Inquiries range from asking for an API (application programming interface) to adapting AI analytics platforms to different domains, such as real estate or insurance. \u201cCollaboration is the name of the game today.\u201d<\/p>\n\n\n\n

There\u2019s extra effort involved in customizing the platform for each new scenario that Constems-AI\u00a0is exploring with partners. And this is where GenAI is enabling a faster progression already<\/a>. \u201cAs we go forward with the transformer model, the effort we put into any new scenario would start reducing.\u201d<\/p>\n\n\n\n

One of the ongoing debates in most deep tech startups is around the tradeoff between focusing on the core vertical and extending it to other verticals for a broader base. There\u2019s always a risk of wasted time and money in these forays. Now that is getting mitigated by the transformer model, as in the case of Constems-AI.<\/p>\n\n\n\n

It already has a parallel transformer model whose performance is close to that of its product in retail. \u201cWe still use the deep neural network for retail, but for all other verticals where we are providing APIs, we are using the transformer model to get to market faster,\u201d explains Srivastava.<\/p>\n\n\n\n

The startup saves the time and expense required to train\u00a0AI\u00a0models in each new vertical<\/a>. The dual benefit is that its transformer model is getting tested and refined in the other verticals. So when the time comes to make the switch in its core retail vertical where it has a large customer base, it can tap into the learnings from the other verticals and make the change smoother.<\/p>\n\n\n\n

\u201cProduct is what gives you money. Innovation is what keeps you relevant,\u201d points out Srivastava. The product vs innovation challenge is something that magnified quickly for Constems-AI with the GenAI wave. It had to come up with a strategy to balance the two.<\/p>\n","protected":false},"excerpt":{"rendered":"

\u201cWe will be ready to sacrifice our product if required, because we have to consciously call out that we will be an innovative company.\u201d – Amit Srivastava, CTO and co-founder, Constems-AI.<\/p>\n","protected":false},"author":12,"featured_media":19697,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[26,35],"tags":[7,9,105,138],"chapters":[],"class_list":["post-19640","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-growth","category-product","tag-growth","tag-build","tag-ai","tag-gen-ai"],"acf":[],"yoast_head":"\nSteering a product through GenAI disruption<\/title>\n<meta name=\"description\" content=\"\u201cWe will be ready to sacrifice our product if required, because we have to consciously call out that we will be an innovative company.\u201d\" \/>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, 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