Property Finder’s commercial sales team beats target by 25% with WisdomAI

Overview

Property Finder is the leading real estate platform in the Middle East and North Africa, connecting property seekers with agents and developers across the United Arab Emirates, Saudi Arabia, Egypt, Qatar, Turkey, and Bahrain. Headquartered in Dubai, the company originally launched as a printed real estate magazine in 2005 and has since evolved into a full-scale property search, CRM, and marketing infrastructure tool used by agents and developers across the region. In January 2026, Property Finder announced a $170 million fundraise, bringing its total funding to nearly $1 billion in equity and debt financing.

Property Finder’s data team supports commercial, product, and marketing functions internally with the WisdomAI Agentic Analytics platform. In 2026, the company embedded WisdomAI in its product, giving the real estate agencies that list on its platform on-demand access to insights.

Challenge: Data democratization stuck in the pilot stage

Three or four years ago, Property Finder began exploring AI-driven data analytics. Volkan Oktem, vice president of analytics, began looking for a way to make self-service analytics available across the organization. The overarching goal was simple: give everyone in the company, regardless of technical background, the ability to ask questions and get answers as easily as asking a colleague. 

The problem was that every tool the team tried fell short of expectations. While point solutions could answer a narrow set of questions, none delivered the self-sufficiency Property Finder needed to roll self-serve analytics out beyond a handful of technical users. Every attempt hit a wall before it had a chance to scale.

The team also considered building something in-house. With a general-purpose LLM available, many of Property Finder’s engineers thought that a custom tool built specifically for the business would outperform any third-party platform. 

Oktem saw things differently: he knew the eventual solution would require a centralized, updated context layer, and that leveraging an LLM model on its own wasn’t the hard part. Building a solution that required ongoing engineering support to maintain context wasn’t sustainable for his team.

“LLMs are a commodity,” Oktem says. “The real value add is how you build, maintain, and update the context, and how you call that context when needed.” 

Solution: Betting on the product, not the model

Property Finder began researching the market to find tools that could deliver true self-service analytics without pulling engineering into every context update. Most options fell into one of two traps: they either returned inaccurate answers that couldn’t be relied on or they required significant, ongoing engineering support to stay useful.

As the company narrowed down its options, they ultimately chose WisdomAI’s Enterprise Context Layer and Analytics Harness, betting the value wouldn’t come from the underlying language model but from the product wrapped around it. While the context engine grounds every answer in Property Finder’s actual business definitions and data, the harness orchestrates how agents reason over that context and autonomously execute on it.

During the vetting process, the team prioritized three key requirements:

1. Self-service by design: Anyone in the organization, regardless of their technical skills, needed to be able to ask questions and receive answers without roping in the data team.

2. Non-technical context management: Business context needed to be something Oktem’s team could build and maintain directly, without requiring engineering resources for every update.

3. Continuous improvement without added headcount: The platform needed to get smarter and more comprehensive over time instead of forcing Property Finder to hire new technical staff to keep up with demand.

“Where WisdomAI comes into the picture is having a product around our needs, continuously improving, but also giving a non-technical user the ability to develop the solution with the tools in place,” Oktem explains. “That was the main decision driver for us.” 

Instead of having everyone contribute to context building across the company, Property Finder put the task on the plate of a small group of domain owners inside Oktem’s team, each of whom was responsible for a specific business area (e.g., commercial, product, and marketing). Oktem found that spreading context ownership too widely led to inconsistency and conflicting definitions. By putting a small group of dedicated owners in charge, it was much easier to keep context accurate as business needs evolved.

To drive adoption, Property Finder leaned heavily on both internal marketing and the product itself. Out of the gate, the company branded its internal WisdomAI implementation “Jarvis,” a nod to Tony Stark’s sidekick, building office-wide campaigns around the idea Have you asked Jarvis? Leadership reinforced the shift by redirecting all data requests back to the tool instead of answering them directly — even if those requests came directly from senior executives.

As they continue utilizing WisdomAI, Property Finder tracks two metrics on an ongoing basis: fulfillment rate, or the share of questions WisdomAI answers with data, and accuracy, or the share of answers that are correct. Oktem’s team broadcasts these metrics across the company regularly, seeking to build trust and transform WisdomAI into a tool people rely on to support their day-to-day work.

Results: Trust, speed, and measurable business impact

With WisdomAI in place, Property Finder’s data team is no longer a bottleneck between employees and the answers they need. Now, nearly every corner of the business can ask questions and act on the answer the same day, without waiting on anyone else.

“I’m getting the data, but I don’t know if it’s correct or not,” Oktem says of the scrutiny new AI tools often face. “So, as part of our internal go-to-market, we continue to share the accuracy. We build trust in the product so they can continue and expand their usage.”

Thanks to WisdomAI, Property Finder has seen:

• 2x productivity on the data team: WisdomAI now handles the equivalent workload of a data team twice its actual size, Oktem estimates. By taking requests off the data team’s plate, Property Finder’s analysts can focus on the most important and complex work.

• Measurable commercial impact: Commercial team members with higher WisdomAI adoption receive automated talking points and client comparisons ahead of every meeting, helping them achieve 25% higher sales performance against target than their peers.

• High, sustained adoption: 80% of Property Finder’s eligible employees use WisdomAI on a weekly basis, asking an average of 10 questions each week — ad hoc questions that no longer require the data team’s involvement.

• Near-total retention: Once employees start using WisdomAI, they stick with it; Property Finder’s retention rate is close to 100%.

• Trusted accuracy at scale: WisdomAI answers questions with roughly 98% accuracy, a number Property Finder tracks continuously and shares with employees to encourage broader adoption.

“This allowed us to leverage our bandwidth in a much more impactful way because [of] the low-complexity tasks we no longer need to do.”

—Volkan Oktem, Vice President of Analytics, Property Finder

The impact of WisdomAI is perhaps most visible in how Property Finder’s commercial team prepares for client conversations. Before WisdomAI, a salesperson heading into a renewal or monthly check-in had to manually pull data from multiple dashboards and build a narrative from scratch — enduring a tedious process that depended on each person’s analytical skills and the time they had to spare. Today, WisdomAI automatically generates structured, client-specific narratives before every meeting; Property Finder is currently extending that same automation to build client-facing presentation decks through WisdomAI’s API to further streamline the preparation process.

“One side benefit is we are now a lot more consistent in how we communicate to our clients, as opposed to everyone running their own show.”

—Volkan Oktem, Vice President of Analytics, Property Finder

That consistency only truly matters, though, if people actually trust the numbers behind it.

“If every time you pull the data, you have to start questioning if the data is correct, then you might use it once or twice, but you’re not going to use it a third time,” Oktem says.

Looking ahead: The next data hire won’t need to know SQL

As WisdomAI adoption spreads across Property Finder, Oktem sees a change in both how the company works with data and who does the work in the first place.

“In an ideal world, and I don’t think we’re far from it, maybe in a year or two we’ll get to the point where 10 to 20% of the team will still need to write queries and maintain the data, but 80% of the team will no longer require any technical skills,” Oktem says. “They don’t need to know SQL. What they will need to have is strong problem-solving and analytical skills, but not technical skills.” 

This shift is already reshaping who Property Finder hires. Historically, the company looked for candidates possessing both consulting skills and strong technical chops, a combination Oktem describes as “somewhat unicorns.” With WisdomAI handling more of the technical lift, Property Finder can hire for judgment and curiosity instead, opening the door to a much broader talent pool.

“Now what’s differentiating the employees is you need to be curious and you need to be able to operate in a self-sufficient way to keep up with the latest and greatest of what’s happening in the world, specifically in your domain.”

—Volkan Oktem, Vice President of Analytics, Property Finder

Looking further into the future, Oktem expects the role of the data team to change just as much as the skills practitioners possess. While data teams have traditionally been measured on whether infrastructure got built instead of the actual business outcomes they delivered, Oktem sees that standard eroding as data becomes democratized.

“I do see data teams evolving in the sense that they will be responsible for delivering business impact with heavy data involvement, partnering with relevant business functions. I do expect this shift across other companies where the data team is going to have to own business outcomes together with the relevant partnering function.”
—Volkan Oktem, Vice President of Analytics, Property Finder

For leaders starting their own AI journeys, Oktem offers some advice: don’t try to solve everything at once and secure organizational buy-in with clear success metrics defined before a pilot begins.

Property Finder is already putting that same philosophy to work outside its own walls. Having proven the model internally, the company is now extending WisdomAI embedded in its own product to the real estate agencies that list on its platform. The goal is simple: closing the loop between question and answer for its customers the same way it did for its employees.

“What we’re trying to achieve with this external rollout is to eliminate all that process and give the end user — the real estate agency decision maker — the ability to pull and analyze that data on their own,” Oktem concludes.