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AI powered Semantic Search
Tired of Sifting Through Irrelevant Results? Switch to Semantic Search!
AI-powered semantic search is beneficial over normal keyword search in the following ways:
Accuracy: Semantic search provides more accurate results compared to keyword search as it understands the context of the search query and provides results that are relevant to the user's intent.
Multilingual support: Semantic search provides support for multiple languages, allowing users to search in their preferred language.
User experience: Semantic search provides a better user experience as it eliminates irrelevant results and provides results that are more tailored to the user's needs.
Taking Your Search Experience to the Next Level: Semantic Search in Action
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Enterprise search: Semantic search can be used by organizations to search through their internal databases and find relevant information.
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E-commerce search: Semantic search can be used by e-commerce websites to provide more accurate search results to their customers.
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Healthcare search: Semantic search can be used in healthcare to search through medical literature and find relevant information.
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Customer support: Semantic search can be used by customer support teams to quickly find answers to customer queries.
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Legal search: Semantic search can be used in the legal industry to search through legal documents and find relevant information.
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Education Search: Seamlessly integrate semantic search with existing video library, enabling precise query results within individual videos. Whether you’re a student revisiting critical concepts or an educator facilitating activity-based learning, our solution empowers you with efficient, visual, and multilingual search capabilities.
Under the Hood
Multilingual Large Language Model (LLM) embeddings are used to convert the text data into numerical vectors that can be analyzed by computers.
The numerical vectors of the text data are indexed into a vector database.
When a user submits a search query, the LLM embeds the query into a numerical vector.
The vector database is then queried to find similar vectors that match the user's query.
The results are then presented to the user in order of relevance.
Case Study
AI-Powered Video Search
Challenge:
Our client, the Parliament of India, wanted to create a video search application that enabled users to search for specific content in their video library using natural language queries. For this the Parliament came out with an RFP and conducted a rigorous Technical Evaluation of the vendors by asking them to demonstrate the above mentioned capabilities live on a video provided at the time of evaluation.
Solution:
We developed an AI-powered video search application using semantic search algorithms to retrieve results based on natural language queries.
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We started by synchronising the Official Debate Texts with audio using multilingual speech-to-text to generate subtitles.
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We then indexed the transcript/subtitles for searching.
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The UI of the application had two input options - one for AI-semantic search and the other for keyword search. Users could combine both options to find contextual content with specific keywords.
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The search results were displayed along with the relevant timestamps of the video. Users could click on the timestamps to start playing the video from that specific point.
Result:
Our solution secured the highest marks in the Technical Evaluation, ensuring that we won the bid and were awarded the project. The AI-powered video search application enabled our client to enhance their search capabilities and improve their users’ search experience. The semantic search algorithm provided more accurate results compared to traditional keyword search. The application also allowed users to easily navigate to specific points in the video content, saving them time and effort.
