How User-Facing and Server-Side AI Tools Work Together in CRE Software

How User-Facing and Server-Side AI Tools Work Together in CRE Software

2 May 2024

AI isn’t all chatbots and content creation, it’s also powering business operations.

When thinking of Generative AI (GenAI) in business, we often picture its ability to support and improve chatbots, content production, and product recommendations. Frontend solutions are the customer’s first point of contact with a business. Unsurprisingly, these are the groundbreaking uses of GenAI that we see in the news. But, what about GenAI integrations that the user doesn’t see?

Customer-facing software consists of two parts. The frontend is the part of the website that the user interacts with. The backend, sometimes referred to as server-side, is the “invisible” structure developers create to help software function properly.

This means that customers of software utilising AI, typically interact with the frontend application. But, understanding the recent developments in AI, with the arrival of Generative AI, and advancements in Large Language Models (LLM), and Natural Language Processing (NLP), means understanding what’s behind the comprehensive answers it puts out. This is the only way to develop a holistic view of AI’s potential.

GenAI has emerged as a defining force in the technology landscape in the last year, with the McKinsey Global Survey, ‘The State of AI in 2023’ finding that one-third of organisations have integrated GenAI tools into their operations. GenAI is no longer a distant trend or futuristic dream, but an essential tool for real estate asset managers to leverage in 2024.

GenAI will not only change the way we develop SaaS software solutions, but also how we host it. Backend software is the backbone of applications and systems. If you have ever booked a reservation online, sent back a product, or relied on automated support, you have interacted with backend integrations.

For software across commercial real estate (CRE) to deliver useful and comprehensive solutions, AI tools used in the background to clean, compile, and make sense of data must be integrated with a tool that makes this information accessible to customers. Having high-level data analytics becomes exponentially more useful when there is an intuitive interface to host them.

What do server-side AI integrations look like in commercial real estate technology?

The concept of AI, as we use and understand it today, is quite a bit different than it was a couple of decades ago. This is because of the advanced machine learning algorithms and cloud based infrastructure that make up the technology today. 

AI integration has completely changed the way software is conceptualised, developed, and executed. AI is increasingly able to process significant amounts of data, and through that, make intelligent decisions. 

AI that is server-side refers to tools that have no direct interaction with the user. When it comes to CRE, these tools are used for data processing and cleaning, predictive analytics, property valuations, market analysis, and various other use cases.

For real estate data, NLP techniques can be used to extract the relevant information from unstructured text data, such as property descriptions and market reports. Machine learning models are able to be deployed server-side to perform predictive analytics, such as estimating property values, forecasting rental demand and predicting future trends.

Users do not interact with AI that runs in the back-end of software. This invisibility belies its profound impact on system performance and functionality. Through sophisticated algorithms and machine learning models, AI sifts through immense volumes of data generated by software interactions, transactions, and operations. It discerns patterns, predicts trends, and identifies anomalies with a level of accuracy and speed beyond human capabilities.

AI that is not user facing in commercial real estate software can enable automated, data-driven decision making. It deepens asset managers’ understanding of their portfolio, as it relies on advanced analytics, ML and NLP, across large-scale, complicated, real estate data. But, accessing and making sense of this valuable data is difficult.

How does AI in the back-end of software become accessible to asset managers?

AI integrations that speak to user-facing services help make complicated data accessible for the average platform user. 

Natural Language Processing (NLP) distinguishes AI-powered chatbots from their traditional chatbot predecessors. Chatbots are now able to hold human-like conversations with users. These contemporary uses of chatbots allow a connection between the complicated data processes in the background and the customer. 

When the front-end (what users see and interact with) and the back-end (the server where the data lake is stored) act together, users are able to reap the full benefits of complex AI systems. So, AI that is able to sort, comprehend, and predict based on data, can also create outputs that the average user understands and utilises. 

In CRE software solutions, background uses of AI can collaborate with user-facing chatbots to enhance functionality and user experiences. AI used in the back-end can process vast datasets generated by property listings, market trends, and user interactions, analysing them for valuable insights. AI systems employed on the user-facing side, like recommendation engines and chatbots, utilise these insights to personalise user experiences, provide targeted recommendations, and facilitate natural language interactions. 

AI strengthens security measures by detecting anomalies and ensuring user authentication, protecting sensitive data within the CRE software ecosystem. This enables customer-facing AI to speak with datasets, providing CRE software platforms the ability to adapt in real-time, learn from user feedback, and continuously improve both the efficiency of property management operations and user satisfaction with the platform.

At Stonal, we have developed StonalGPT, the first conversational AI for real estate professionals looking to pull detailed data from their portfolio. The back-end is where the data is stored, compiled and organised in a data lake. The digital assistant, sits on the front-end, and creates a conversational and accessible way for Natural Language Processing (NLP) distinguishes AI-powered chatbots from their traditional chatbot predecessors. s to ask questions, and receive verifiable and accessible information.

The power of verifiable, detailed, and significant data, is bolstered by accessibility. AI on the front-end and the back-end, means real estate asset managers are able to ask specific questions about their portfolio data, and receive instantaneous, and correct information, allowing them to increase time and accuracy in the decision making process.

1. McKinsey, The State of AI in 2023.

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