How Agentic AI is reshaping business decisions in Real Estate and PropTech

Virtual staging. A chatbot answering rental questions.

That’s what most people picture when they hear ”AI in real estate.” But for PropTech operators, the actual impact is happening deeper in the stack – where the ”Big Iron” lives and systems built for a different era slow businesses down. In an industry defined by fragmented data and inefficient processes, staying agile means moving toward AI-native software engineering – using AI models throughout the entire development lifecycle, not just as a small add-on feature.

At Software Mind, we’ve been applying Agentic AI to do just that.  In this article, you’ll learn how, with the use of our AI Modernization Toolkit, we tackled two massive, critical problems: modernizing decades-old legacy systems and automating the chaos of public data collection.  Here is how these AI-native capabilities look in practice.

When mainframes become bottlenecks: AI-powered COBOL 2 Java migration

Why do ancient-looking mainframes and COBOL systems still run the world? Simple: they are fast, reliable, battle-tested and mission critical. Financial institutions, airlines and even PropTech platforms process millions of transactions daily on mainframes. But while these systems are incredibly reliable, they are also a ticking time bomb.

Here’s what most people don’t see – the real problem isn’t COBOL itself. It’s everything around it:

• a shrinking talent pool – experts who wrote the systems are retiring
• massive tech debt (40,000+ COBOL/JCL files in one system is not unusual)
• undocumented business logic written decades ago
• enormous maintenance cost
• difficulties in integrating legacy applications with new ones, as well as barriers in implementing new industry standards in general
• the risk of touching something you don’t fully understand

For many engineers, maintaining these mainframe systems may feel like digital archaeology – deciphering code older than they are, often with no documentation or authors to guide them.

A full rewrite can be even scarier. The cost of retraining everyone like developers, operators, auditors and business users typically outweighs the cost of keeping the mainframe running. That’s why for years companies have been taking the “frontend modernization” shortcut: wrapping the mainframe in a new UI rather than replacing it. But you can’t wrap your way out of technical debt forever.

So, how do you move millions of lines of code to Java without breaking the bank?

Modernizing decades-old legacy systems

To specifically handle the messy realities of mainframe migration, at Software Mind, we’ve built an AI-powered COBOL modernization toolkit. We operate with a process-first mindset, using AI to pinpoint which parts of your legacy logic remain vital and which must evolve to meet today’s standards. This ensures your modernization efforts actually serve business goals, rather than simply moving outdated problems to new servers.

This approach gives us maximum flexibility. If your business priorities demand immediate change our AI agents are capable of refactoring code and injecting new functionalities simultaneously with the migration process (rearchitecting during migration). Whether you need a safe 1:1 transition or a complete modernization during the transfer, we tailor the roadmap to your specific needs for stability and speed, using AI to automate the heavy lifting that previously dragged on for years because of risky “big bang” rewrites (the Lift-and-Shift approach). 

At Software Mind, we don’t believe in generic, one-size-fits-all tools. We believe in personalization – tailoring AI to fit the specific, messy, complex reality of your business. 

Our toolkit isn’t just a ”black box” solution; it’s a suite of specialized agents:

COBOL to Java migrator that offers seamless migration of COBOL functionality to a modern Java stack:

• Process-first mindset: we identify what must stay and what should evolve
• A new application architecture ready for rapid future development
• Automated discovery and analysis of mainframe logic using AI agents
• Minimal human rewrite effort for COBOL → Java 21+
• 67%+ workload reduction vs traditional rewrite

Test2code tool that automatically generates:

• Unit and integration tests
• Regression tests with golden data
• End-to-end and UAT tests
• Very high-test coverage (85%+) and full functional parity with the old system 
• 87% workload reduction vs manual test writing

UI 2 code tool  

• Conversion: instantly transforms UI screenshots (from web, Figma or terminal) into clean, maintainable React or Angular code using AI analysis.
• Validation: provides a live sandbox preview for immediate testing, ensuring the output matches the original layout and controls.
• Customization: allows developers to review, refine and style the code using industry standards (Material UI, Bootstrap) or custom design systems.
• Workload reduction: lowers up to 80% of workload, especially for UI-heavy systems.

Oracle2Postgres migrator

• AI-powered database transformation with up to 90% workload reduction.

The business impact: We are seeing a 60-90% reduction in workloads compared to manual rewriting. But more importantly, we utilize strategy where human experts verify every line of generated code. This allows companies to escape the mainframe trap and move to a cloud-ready architecture without the risk of a ”big bang” failure.

Transforming property data operations with AI-driven web scraping

In the US Real Estate and PropTech market, data is gold – specifically, property data. But anyone who’s ever worked with U.S. Tax Agency data, knows two things: it’s publicly available and it’s incredibly fragmented. Every county, every agency, every portal looks different. Yet, real estate companies rely on this data every single day.

Historically, operations teams have had to manually scour these sites and copy-paste data into spreadsheets. It’s slow, soul-crushing work with a high margin for expensive human error and almost zero potential for traditional automation.  

The intelligent automation solution 

To improve data freshness, accuracy and accessibility – all while reducing the operational effort – something had to change in how public Tax Agency data is collected across the U.S. We addressed this by developing AI agents that fully automate data harvesting from these sources.

We didn’t just build a scraper; we built an ecosystem of AI Agents that understand process over layout. Instead of treating them as hundreds of isolated problems, we categorized common processes and automated them in scalable groups – something standard third-party tools simply can’t do.

• One prompt, many sites: unlike standard bots that break when a website layout changes, our AI groups agencies by process similarity. We can support multiple agencies with a single natural language prompt. Business users can now manage these scrapers using natural language. You don’t need a dev team to adjust a parameter; you just tell the AI what you need.
• Self-healing bots: if a Tax Agency updates its interface, our QA Agent detects the break, fixes the script and retries automatically. Humans only step in for edge cases.

• Property data transformation: historical property codes (e.g., NTT codes) are often stored in thousands of Excel files scattered across teams. Manually analyzing and reconciling this data would take months, if not years. Instead, we used an AI-generated script that analyzed the entire dataset in seconds – mapping, cleaning, and standardizing it into a centralized system. This automation scales across multiple agencies – bringing everything into a single, unified platform.

 The result is a shift from fragmented, outdated datasets to a unified, live stream of intelligence – significantly increasing operations autocomplete (processes fully automated) and reducing human error to essentially zero.

Building real estate for the next decade

Both examples – mainframe modernization and property data automation point to the same reality: AI isn’t just enabling new capabilities – it’s finally fixing the long-standing constraints that have been holding companies back for years.

Whether it is migrating from COBOL to Java or harvesting property data, it all comes down to one thing: efficiency. AI takes on the ”heavy lifting” – analyzing legacy code, navigating messy data sources, generating tests – so that your teams can focus on what really matters – making strategic business decisions based on reliable, real-time data.

Real estate organizations that adopt agentic AI now will be able to unlock automation levels impossible before, as well as reduce operational cost at scale. Moreover, AI will also allow to eliminate human-dependent bottlenecks and modernize tech stacks without unacceptable risk. But most of all, AI will let them build systems ready for the next decade, not the last.

Don’t let legacy systems slow your growth. Contact us and let’s discuss a roadmap for your PropTech transformation.

FAQ

How does Agentic AI reduce the risks associated with system modernization?

AI minimizes risk by automating code analysis, generating high test coverage and ensuring functional parity between old and new systems. Combined with human expert validation, this approach enables safe, incremental transformation rather than disruptive, large-scale rewrites.

Why is automating property data collection such a challenge in the U.S. market?
Property data is highly fragmented across thousands of local tax agencies, each with different formats, portals and processes. Traditional automation like RPA struggles with this variability. Agentic AI overcomes this by understanding process patterns rather than fixed layouts, enabling scalable and resilient data extraction.

What is the business impact of adopting Agentic AI in PropTech operations?
The impact is both operational and strategic. Companies can achieve 60–80% workload reduction, near-zero human error in repetitive tasks and real-time access to unified data. This allows teams to shift focus from manual processes to higher-value activities like strategy, innovation and decision-making.

Author: Krzysztof Szczecki, Przemysław Frąckowiak-Szymański