Facilgo is a comprehensive platform that streamlines the home inspection process, helping property managers, real estate agents, and inspectors automate their workflows. The platform covers everything from inspection scheduling to report generation and compliance management. With growing demand, Facilgo sought to enhance its service offerings by incorporating AI-driven automation to improve inspection efficiency and ensure high-quality reporting.
Facilgo faced several key challenges as its platform scaled
Facilgo required a robust AI-driven solution that would automate these manual processes, reduce human error, and enhance the overall efficiency of the inspection process.
Inspectors were spending too much time manually inputting data and creating detailed reports, leading to delays and inconsistencies in the final output.
With thousands of inspection reports generated daily, extracting meaningful insights from large volumes of data was a time-consuming task. Facilgo needed an automated system to quickly analyze inspection data and flag potential issues.
Communication between inspectors, property managers, and other stakeholders was often fragmented, leading to misunderstandings and delays in addressing critical issues.
Amplework Software implemented an LLM (Large Language Model) specifically designed to enhance and automate the inspection process on Facilgo’s platform. The solution included
Using LLM, we automated the process of converting raw inspection data into detailed, standardized reports. The AI could interpret input from inspectors (images, notes, and measurements) and generate coherent, professional-grade reports with minimal human intervention.
The LLM was trained to extract key insights from large datasets. For example, the model could identify recurring issues across properties, such as water damage or structural weaknesses, and flag these for the inspectors. This allowed Facilgo to offer predictive analytics to property managers, highlighting potential future risks based on historical data.
The LLM was equipped with advanced natural language processing (NLP) capabilities to understand the context of inspection notes and automatically categorize issues (e.g., electrical, plumbing, structural). This helped inspectors focus on critical issues by prioritizing them in the generated reports.
Based on the inspection report, the AI automatically assigned tasks to the appropriate teams (e.g., plumbing contractors or electricians) based on the identified issues. This streamlined the process of addressing problems quickly and efficiently, ensuring no communication gaps between inspectors and property managers.
The AI provided actionable recommendations to property managers based on the inspection findings. For instance, if water damage was detected in a property, the LLM could suggest preventive measures or repairs, including cost estimations, based on past data.
Amplework Software integrated a support chatbot powered by LLM to assist inspectors in real-time. The chatbot answered common queries about compliance regulations, inspection standards, and best practices, enabling inspectors to perform their tasks more effectively.
To create an intelligent AI model for the home inspection industry, we gathered vast amounts of historical inspection data, including reports, photos, notes, and stakeholder communication. The LLM was trained to understand the structure and content of inspection data, enabling it to automatically generate accurate and standardized reports.
We trained the LLM to understand the specific language and terminology used in home inspections. For example, phrases like “cracked foundation” or “leaky pipe” were mapped to specific problem categories, allowing the AI to detect and categorize issues more efficiently. This made it easier for inspectors to create comprehensive reports with minimal input.
We developed an AI-based task assignment engine that connected directly with the LLM. When an issue was detected in an inspection report, the AI would automatically assign the task to the appropriate team and track its resolution. This eliminated manual coordination and improved the speed at which issues were addressed.
A conversational AI assistant was integrated into the inspector’s dashboard. Using NLP, this assistant could interpret complex queries from inspectors, provide relevant answers, and guide them through specific compliance requirements or technical standards.
After deployment, the LLM was continuously optimized based on user feedback and real-world performance. The model improved its accuracy in report generation and task assignment over time, thanks to the ongoing learning process embedded in the system.
The AI and LLM integration revolutionized Facilgo’s home inspection processes, delivering significant benefits
Reduction in Report Generation Time:
The AI-powered inspection report generation system drastically reduced the time it took to create detailed reports, freeing up inspectors to focus on more critical tasks.
The LLM’s ability to understand and categorize issues based on inspection notes and photos led to highly accurate reporting, reducing the need for manual intervention.
Accuracy in Contextual Issue Detection:
By automating task assignments and communication, the AI system significantly improved the efficiency of addressing and resolving inspection-related issues.
Increase in Task Completion Speed:
With real-time assistance and automated reporting, inspectors were able to complete more inspections in less time, resulting in increased operational efficiency.
Increase in Inspector Efficiency:
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