1. transforming business with data & digital products
RAPIDOPS – AI POWER SEARCH – REFLECTED INTELLIGENCE 5 NOV 2022
THIS MATERIAL IS CONFIDENTIAL ・ © 2022 RAPIDOPS

2. About Me (Kartik Dhokai Off course )!
Speaker and Panelist at
• TEDx PortBlair
• IIM Rohtak
• IIM Visakhapatnam
• Agile Network India
• Leanpitch
• STeP-IN forum.
© 2022 Rapidops Inc.
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What I like to do
• Helping people to grow Agile Mindset
• Help customers to deliver faster results
• Defining Go-To Market strategy for the products
• Keeping myself fit
Sr Project Manager @
Rapidops Solutions Pvt Ltd
“Give good to the Universe and Get back the best from it”

3. Agenda
• Brief about Traditional search
• Challenges with Traditional search
• Brief about AI Power Search and WHY?
• How search can be optimized using AI Power Search?
• Statistics after implementing AI Power Search
• Implementation challenges
• Key Benefits of implementing AI Power Search
• Challenges in AI Power Search
• Future of AI in Retail industry
• Takeaways
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4. Traditional Search
• Traditional search engines provide users with a doorway into the information they are seeking but typically offer a very broad set of results that require
the user to continue to search and refine.
• These search engines are constantly crawling and indexing the entire internet, then ranking those results so when a user enters search terms, the
engine is able to provide a set of links, (including paid links), that match the terms the user is interested in.
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Ref - https://blog.nativeadvertisinginstitute.com/3-key-differences-traditional-native-search

5. Challenges with Traditional search
• If users are not well versed with exact search keyword, it will take a huge time of users to get the exact results or it’s either impossible to get the
results.
• Search spam
• The comparison process is fragmented when using traditional search, forcing the user to continue navigating back and forth from the search engine
when comparing products and services.
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6. AI Power search and WHY?
1. AI Power search allows us to bring in the multiple dimensions of the users and the data available to produce the most relevant results.
2. AI Power search engines evolved through not only in text but also in tags, descriptions, category markers, business priorities, geolocation of the
users, past behavior of the users, searchable metadata and other contextual factors in determining specific content relevance for everyone.
AI Power search will help end customers to reduce the search time and help them to find the exact search results, avoid spam, and help customers with
non-fragmented comparison between similar products and services.
In a nut-shell, AI Power search has a capabilities of Domain aware, Contextual and Personalized, Conversational, Multi-modal, Intelligent, and Assistive.
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7. How AI Powered Search works?
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8. ML based method for Relevance Optimization
AI-powered search engines rely on natural language processing (NLP) to read, understand, interpret, and analyse queries. ML based method covers
millions of use cases and edge cases, which run from vague to precise.
There are few techniques which helped in relevance optimization such as
1. Semantic Annotations
2. Text Analysis
3. Named Entity Recognition
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9. Statistics!
With this transformation, we helped our customer by integrating Customer Intelligence and Product Intelligence to create unique profiles for every
customer. Customer Intelligence combined data gathered from user clicks, products selected, and shopping intent, to create a 360-degree view of the
shopper. Product Intelligence identified the attributes of the desired products to provide recommendations with increased relevance.
The implementation of the suite uplifted the shopping experience, including repeat visitors, revenue per visitor, and visitor-to-order conversion rate.
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Conversion Rate - 9.7% Overall Revenue - 6.3 %

10. Implementation challenges
• Initially we received raw data from our client but unfortunately not having enough knowledge of products we have annotate the data such as Product
type, Diamond type, Carat weight, and Manufacturer name based on our best understanding and with the help of our customers but unfortunately, we
haven’t received the desired results.
• Interacted with the customers and connected with the diamond manufacturer with the help of client to understand and get some parameters about
diamonds more in detail. We annotated some more parameters as Cut, Polish, Symmetry, Price, and Gridle Thickness.
• In third iteration we have added Lab details who certify the diamonds.
• Few other parameters which we have annotated are Purity which is same as Carat weight, only Width and Height, and Gross weight.
© 2020 Rapidops Inc.
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11. Generalized Search
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Tech stack – PyTorch library, Solr, Spark, Content based model, Kubernetes / GCP

12. Popularized Search
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Tech stack – PyTorch library, Solr, Spark, Content based model, Kubernetes / GCP

13. Personalized Search
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Tech stack – PyTorch library, Solr, Spark, Collaborative-filtering based model,
Kubernetes / GCP

14. AI Power Search – Key Benefits
Customer Retention
Help existing customers to come back on our platforms
and serve them better.
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Sales Increase
Helping customer with the exact product will help us
to increase the sales.
More customer conversion
System can quickly authorize employees, detect multiple
employees in a frame, detect employees in low light and
detect photos (in progress)
Length of site visit
Showing relevant results will help customers to stay on
web app for longer period.
Increasing Personalization
Unlike current systems, all identification data and
access management is centralized which simplifies
operations and avoids data loss

15. Challenges in improving AI Power Search Relevance
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Omnichannel context is where user browser different products using different platform, which confuse the model to search relevant products for the
users.

16. Continue…
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Customer behavior is another big challenge as in an image we are seeing that customer is walking on left aisle and looking at a products on
right aisle. So, there are very high chance that user would be picking up a products from right aisle. Same way, it’s hard to predict user
behaviors digitally as user would browse different products at different time and may be every other day a different products which makes it
difficult for a model to help customers with a relevance search.

17. Continue…
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As we are seeing in an image that user is picking up a products from the store and care taking team of store can notify to the manager about product
getting out of stock. With this, what happens digitally when products go out of stock, it will still be listed in search results which is not at all a good user
experience. There are teams out in an organization who are trying to solve this use case with the help of AI Power Search.

18. Continue…
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Addressing Knowledge graph use case using AI Power search is bit challenging as most of the organization use content-based labeling where user finds it
difficult to get the correct product in search in first attempt.

19. Future of AI in retail sector
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Source - https://usmsystems.com/top-use-cases-of-artificial-intelligence-in-retail-industry/
According to the research reports, the market size of Artificial Intelligence in retail is expected to reach USD 31.2 billion by 2025 from USD 4.8 billion
in 2021.

20. Key Takeaways!
• Implementing AI Power Search (Generalized search, Popularized search and Personalized search) will help organization to understand their product
domain more in detail which eventually will help organization to market their products to right audience. This will increase the length of site visit of the
customers on our application which would eventually help us to grow in our revenue.
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21. Thank you!
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https://www.linkedin.com/in/kartik-dhokaai-8a305b32/
https://www.youtube.com/watch?v=-z9LAoXGbmQ&t=86s