How are Saudi PropTech companies using artificial intelligence in their daily work? In the Zero to One AI podcast episode on AI in real estate technology, Saleh Al-Ghamdi, Co-Founder and CEO of Ghanem, discusses practical examples alongside Razwa Al-Harthi, Co-Founder of Nuzul.
Published on 14 October 2026, the episode explores property analysis, customer support, marketing, and product development. Its central theme is that AI creates value when it helps teams deliver clearer information and better service while preserving the trust behind a property decision.
Watch the full podcast on YouTube. The conversation is in Arabic.
How Ghanem uses AI in real estate
Al-Ghamdi explains that Ghanem used AI tools from the early stages of building its product and later expanded their role in internal operations. As its fractional property ownership business developed, organizing information and accelerating reviews became increasingly useful for handling more opportunities and customer requests.
Examples discussed in the episode include:
- Property opportunity analysis: bringing together initial information about a property's location, income, and source, and helping staff spot missing or inconsistent data.
- Financial model preparation: using opportunity documents and tenancy agreements to support analysis through internal business models.
- Document processing: applying optical character recognition, or OCR, to extract information more quickly before review.
- Internal team support: using an internal AI agent to help sales, marketing, finance, and product teams find information and prepare reports.
Verification remains part of the process. Al-Ghamdi connects opportunity reviews with checking ownership information, contracts, and property valuations. These are operational examples described in the interview, rather than a claim that automation removes every possible error.
Fractional property ownership depends on trust
For someone exploring fractional property ownership in Saudi Arabia, a quick answer is only one part of the experience. They also need to understand what they are buying, access the relevant property information, and know who can answer their questions.
In the discussion of trust at 35:48, Al-Ghamdi emphasizes relationships with investors, developers, and brokers, alongside clear information and independent valuations. Human conversations, meetings, and product explanations therefore continue as the technology develops.
He also distinguishes Ghanem's internal agent, which supports employees, from a planned external agent for customers. At the time of recording, he said the external agent had not yet launched. This distinction matters: capabilities already used by the team and future product plans are different stages of the same journey.
Turning customer conversations into useful knowledge
Nuzul offers a complementary example in the WhatsApp discussion at 32:07. Al-Harthi describes analyzing previous customer conversations to identify recurring questions, information that helped customers, and situations that caused frustration or confusion.
That knowledge was organized into a shared reference supporting customer service and the website's search experience. The broader lesson for real estate technology companies is to build useful knowledge around questions customers actually ask, then keep it updated as products and information change.
A consistent source of information also helps teams avoid giving different answers across different channels. The conversation shows why the work behind an AI assistant matters as much as its interface.
Data quality comes before real estate automation
The episode also examines the limits of rapid AI adoption. Building an initial tool is only the beginning; maintaining it and defining the underlying process still require attention. Incomplete records and unclear procedures can carry existing problems into a faster system.
In the closing advice at 1:03:10, Al-Harthi recommends defining the intended outcome before choosing a tool. Al-Ghamdi describes Ghanem's direction in terms of enabling its team, improving customer service, and strengthening credibility through clearer, more accessible information.
A practical way to apply these ideas is to ask four questions before starting an automation project: Which task should improve? Where will the information come from? How will outputs be checked? When should a person take over? These questions connect AI adoption to a real operational need.
FAQs about AI and Saudi PropTech
Which real estate AI applications does the episode cover?
The discussion includes property data analysis, document processing, financial models and reports, customer segmentation, internal knowledge access, and customer support.
Can AI replace human relationships in property transactions?
Both experiences presented in the episode emphasize having an accessible team, particularly when customers need explanations and confidence before making a purchase. Automation helps that team respond more efficiently.
How does AI relate to fractional property ownership?
Internal tools can help organize the information teams use to assess and explain opportunities. Understanding ownership rights, costs, risks, and exit conditions still depends on each opportunity's specific terms and documents.
Watch the conversation and learn about Ghanem
For the examples in full, watch Zero to One AI. To learn about the platform, visit About Ghanem and browse the frequently asked questions.
Source: the Zero to One episode on AI in real estate technology, published on 14 October 2026. The cover is a still from the episode. This article is an editorial summary of the discussion.
