Case Study

Building a Conversational AI Platform with 98% Recommendation Accuracy to Automate Workspace Discovery for Qdesq

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Industry 

Real Estate

Expertise 

Amazon DynamoDB, Amazon EC2, Amazon S3, Amazon Bedrock, Amazon Bedrock AgentCore

Offerings/solutions 

Built a multi-agent AI platform automating workspace discovery, lead capture, and visit scheduling.

About the Client

Qdesq is a leading workspace solutions platform specializing in flexible workspace discovery and booking across India. The platform connects businesses and professionals with coworking spaces, dedicated desks, private cabins, and meeting rooms through an extensive network of premium workspace providers across major cities. With a focus on technology and user-centric design, Qdesq continuously innovates and leverages advanced technologies like Generative AI to enhance customer experience.

Highlights

~98%

Accuracy in Workspace Recommendation

Agents

Automated Requirement Extraction

Qdesq Specialist Agent

Improved Lead Capture

The Challenge

Qdesq struggled with manual lead generation bottlenecks, high drop-off rates from incomplete requirement capture, and the inability to process natural language queries or deliver context-aware venue recommendations, with complex multi-parameter searches requiring manual intervention across multiple touchpoints.

Solutions

• Deployed a multi-agent conversational AI using Amazon Bedrock AgentCore with Strands Agents and Anthropic Claude 3.5 Sonnet for natural language interactions, intent detection, and structured requirement extraction.
• FastAPI backend on Amazon EC2 manages WebSocket-based real-time communication, session handling, and orchestration between the UI, AI agents, and data services.
• Concierge Agent extracts structured requirements such as city, workspace type, budget, and capacity, validated before routing to the Qdesq Specialist Agent via A2A protocol.
• Specialist Agent queries DynamoDB-indexed venue data to identify and shortlist relevant venues, captures leads via Qdesq CRM API, and confirms visit bookings.
• Amazon DynamoDB stores session data, conversation history, and user context for seamless multi-turn interactions.
• Governance and security enforced via AWS IAM, Amazon CloudWatch, AWS CloudTrail, AWS Secrets Manager, Amazon ECR, and Amazon Bedrock Guardrails.

The Results

Delivered a multi-agent conversational AI platform with 98% recommendation accuracy, automating workspace discovery, requirement extraction, lead capture, and visit scheduling to improve conversion rates and operational efficiency.

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