We implemented a cutting-edge AI-based search solution for a leading Indian retailer, leveraging Google Cloud’s Vertex AI. This intelligent search engine delivers highly relevant, context-aware results, improving product discovery, enhancing customer experience, and boosting conversion rates—turning every search into a seamless shopping journey.

Customers were overwhelmed by endless filter menus, sliders, and drop-downs just to find the product they wanted. The process felt slow, clunky, and often led to giving up before discovering the right item.

We replaced complex filters with a simple, natural way to search—just type or speak what you want in plain English. The AI-powered system understands everyday language and intent, instantly showing relevant products. Now, finding the perfect item feels effortless, intuitive, and even enjoyable.

We implemented Google Vertex Search & Gen AI for a startup in the US. The startup is a social network platform with a unique and niche value proposition. Gen AI helped their users generate accurate description for the listings created by the users and AI powered search returned more relevant and personalized search results.

Customers were listing the items they would like to rent but the information was incomplete, inaccurate and the website owners were not able to validate the information about the listing. This affected the user experience on the website and the conversion of browers to buyers.

We implemented Gen AI so that the users can just enter the product name and model number and rest of the information is filled by the Gen AI agent and if the user overwrites the information in the listing the AI would validate and alert the admin for the any discrepency. This ground breaking change resulted in growth of the users count in the website.

We implemented Google Document AI for a media house in India and helped them process the orders for advertisements from various agencies. The Document AI helped extract the relevant information from different formats without writing custom code for each format. This reduced the onboarding time of new agencies to a day and processing orders from few hours to few minutes.

Customer was not able to automate the extraction of the information from advertisement orders because each of their customers send the information in different formats. This made the end to end process of receiving orders from customers and scheduling them a tedious manual process.

The solution we implemented helped the clients onboard new customers within few hours and receive their orders and process then in few minutes and plan the broadcast of the advertisements on the same day in most cases.

Using ChatGPT we developed a tool for one of our clients which will take the process information as input and generate flowcharts. This helped them to validate the standard operating procedure used in their company by various departments. This helped them make quick decisions within the defined framework of SOP.

Customer has operations geographically distributed and was not able to make the output and throughput consistent across the locations. The main root cause for this problem is that the Standard Operating Procedure was on paper but not available in the time of practice.

The solution we implemented helped the clients from Paper to Practice: We turned SOPs into instant, AI-generated flowcharts, enabling teams across locations to work consistently, decide faster, and deliver better results.

Requirement documents are outdated. In the era of AI requirements must delve deep to show the understanding of the problem and help envision the solution. Requirements must be more relatable to the individual contributor rather than higher officials. Knowillence has started using Gen AI to elucidate the problem and refine the requirements.

Customers pounded us with two questions that took more time than doing the actual task. The questions are

By integrating Generative AI into our requirements process, we transformed vague, outdated documents into clear, context-rich narratives that deeply explore the problem and inspire the solution. Requirements became relatable for individual contributors, ensuring better alignment between vision and execution. This shift reduced misunderstandings, accelerated development, and improved solution quality by grounding every step in a shared, AI-enhanced understanding of the problem.