Collaborative Search Engine Development [Case Study]
27 Jun 2022
Surfing the internet today for specific information by utilizing any of the popular search engines could throw up hundreds of results. Most of the time while we get answers to questions but what if we need to go back to them at a later stage or share the relevant list of web links with someone? With a traditional approach, either we need to go back to the search history to extract links or store them separately for future reference. How about a secure mechanism that could allow you to not just store search results but document them and even share them with the target group/community?
Learn how team Rishabh helped a US-based tech innovation company develop a collaborative search engine that does all the above and helps curate custom content.
Project Overview
The founder of the Dallas-based technology innovator had a novel idea. They wanted to build a one-of-its-kind collaborative search platform that would allow users to research, store, share, discuss (chat) and even curate content in a secure ecosystem. They were in pursuit of a competent IT partner to make this idea a reality across the web and mobile-based platforms by leveraging powerful search capabilities.
Challenges
- Limited/no understanding of search engine platforms
- No technical know-how about Data Engineering and Machine Learning
Solution
Our team started the collaborative search platform development with a mobile-first approach. They choose Next.js to develop the web application to work seamlessly across web and mobile platforms with Node.js utilized as the back-end framework.
We identified & mapped varied user personas as listed below;
- Collaborators/Curators: They can search the web, set up a room (a virtual space to collaborate & curate), set the rules for the room, invite members to the room, converse with members, build a knowledge block (a curated content), earn most votes on it & publish it on the platform. Also, activate a storefront to capitalize on it forever.
- Curated Content Seekers: They can search the web, get a preview of related knowledge blocks along with other search results, and buy the curated content that meets your requirements.
Listed are some of the essential developed components;
- Search Data Integration: Utilized data source of Bing search & YouTube. Applied inverted search algorithm to identify people searching for similar topics who can collaborate for productive discussions & curation
- Virtual Space Creation: Public & private rooms and storefront modules to allow groups to collaborate, collate & curate content, and capitalize on content
- Secure Sign-On: Utilized Amazon Cognito for user identity authentication, validation & user management across web & mobile app
Benefits
- ~60K users signed up within a year
- 43% increase in content availability
- 100% visibility on relevant content – web & curated
Customer Profile
A US-based purpose-built technology company
Technologies
Next.js, Node.js, MongoDB, AWS Cognito, SendGrid
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