OpenAI-Based Agent for Realty Complaint Resolution

Based in the US, the client is a leading developer and operator of military family housing, managing over 60k homes across major defense installations. Beyond its defense portfolio, the firm manages a diverse, national real estate footprint spanning master-planned residential communities, commercial properties, and infrastructure investments.

• Manual Work Order Creation: Despite using a property management system, there was no automated mechanism to generate work orders, leading to delays in issue resolution.
• Unstructured Complaint Handling: There was no intelligent system in place to receive, understand, and assess complaints, limiting the ability to prioritize urgent issues effectively.
• No Resident Communication Loop: Residents received no status updates on their maintenance tickets, impacting transparency and eroding trust in the service process.

We developed a comprehensive AI-powered agent leveraging NLP to process resident complaints, identify root causes, and automate resolution.

• LLMs for Information Extraction: Used NLP models to extract relevant information from resident inputs; open source and cloud-based LLMs to identify key entities, workflows, and attributes from SOPs.
• Agentic RAG for Document-Based Responses: Deployed agentic RAG (Retrieval-Augmented Generation) to generate responses using categorized documents and images.
• Structured Questioning System: Designed a rule-based system to ask targeted questions, identify root cause, and assess complaint severity.
• Auto-Generation of Bot Logic: Automatically generated bot logic from root cause flow diagrams using internal organizational knowledge ensuring a precise approach to issue resolution.
• Utilization of Troubleshooting Document Corpus: Fine-tuned LLAMA3 models on a structured corpus of troubleshooting guides for smarter issue resolution.
• Integration with Ticketing System: Automated ticket creation and routing through seamless integration with the client's support platform.
• Continuous Learning and Improvement: Applied reinforcement learning and feedback loops to enhance agent accuracy and adaptability over time.

• Used LLAMA3  to understand user intent and extract information
• Implemented RAG workflow to answer questions directly from categorized SOPs and visual references
• Implemented RASA for rule-based conversation logic to augment LLMs
• Built a document extraction module using OpenAI to autogenerate predefined conversation flow
• Used a multi-agent architecture to aggregate several agent logic into a single point of entry
• Built on AWS and orchestrated with LangChain for scalable performance and seamless workflow integration
• Integrated with Yardi for complaint management

• Efficient Complaint Handling: Automating complaint reception and ticket generation reduced processing time by 40%, enhancing operational efficiency.
• Cost Savings: Reduced manual intervention translated into leaner operations and lower support costs.
• Improved Satisfaction: Timely and accurate issue resolution led to a 25% increase in positive feedback and a noticeable boost in resident trust and retention.
• Data-Driven Insights: Generated valuable data for informed decision-making and process improvements.
• Scalable & Standardized Operations: The modular solution ensured consistent and fair complaint resolution across high-volume property portfolios.