Orel Emil Ohayon is building for the moment AI makes the recommendation

A company can spend years explaining itself and still meet a prospective customer through somebody else’s sentence. When that sentence comes from an AI assistant, the brand may never see the question that prompted it. For Orel Emil Ohayon, that gap is a business worth building around.

Ohayon is the founder and CEO of igeo.ai, a platform focused on how brands appear in AI-generated answers. His premise is straightforward: being available on the web and being recommended to a buyer are different outcomes. The work begins with understanding the distance between them.

A career across software and marketing

Sara Garlick’s September profile for UK Titles traces Ohayon’s entrepreneurial career to Power, a wedding RSVP and seating-software business he founded in 2010 and sold three years later. It describes his subsequent acquisition of Polaris Digital Marketing, followed by a place on HiBob’s early founding team.

According to that profile, he joined HiBob as employee number two and stayed until 2019, later serving as VP of R&D at Studio Designer in Los Angeles. In 2020 he became CEO of PWM, working on enterprise cloud and software systems before founding igeo.ai.

The range matters to the business he is now building. Software development asks whether a system works. Marketing asks whether anyone finds it compelling. Enterprise customers ask whether it can fit into the way their organisation operates. A product that connects AI answers with marketing decisions has to contend with all three.

Garlick also recounts a more personal signal: Ohayon noticed his father, previously uncomfortable with technology, turning to ChatGPT for everyday questions. The significance was the change in habit. A new interface had become useful to someone outside the usual circle of early adopters.

The difference between a mention and a recommendation

The published notes for Ohayon’s July interview with Laurent Cochet on the AI InterConnect Podcast capture his argument in one line: “Visibility is one thing, recommendation is everything.” The conversation’s stated themes include AI search, brand discovery and the difficulty of connecting visibility with revenue.

Consider an imaginary software buyer comparing tools for a growing team. An assistant might acknowledge that a particular product exists, then explain why two alternatives are better suited to the buyer’s requirements. A mention count would record a presence. The buyer would leave with a reason to choose someone else.

That distinction gives Ohayon’s proposition its commercial focus. The useful question is what a potential customer learns about a business, how that account compares with the evidence, and whether it helps the customer make an informed choice. Repetition of a company’s name is only one part of that picture.

The podcast’s description also points to the wider information environment: reviews, video, social platforms and user-generated content can shape AI visibility. That expands the problem beyond writing a better homepage. A business has to consider the evidence a customer might encounter across its public presence.

Building igeo.ai between Cyprus and Israel

igeo.ai dates its start to 2024. Its company page identifies Nicosia as its headquarters and Tel Aviv as its R&D location, alongside a remote team. It names Ohayon as founder and CEO, Avner Warner as CMO and Prof. Alon Dumanis as chairman.

The company describes its work as generative engine optimization, or GEO: measuring and improving how a brand is understood and cited in generated answers. That is a distinct measurement task alongside conventional search optimization. A search ranking describes a page’s position; an answer can also interpret, compare and qualify what a business offers.

For Ohayon, this makes the product as much a translation exercise as an analytics exercise. A marketing team needs to turn an unfamiliar response from an AI system into a familiar business decision: which explanation needs improving, which claim needs evidence, and which audience is being poorly served.

Turning an answer into something a team can work on

The monitoring product described by igeo.ai groups its information around prompts, brand mentions, sentiment, competitors, sources and cited pages. Its stated workflow starts with a brand, its topics and its rivals, then follows answers over time and across regions.

Those are useful distinctions because they preserve context. A positive answer to a broad introductory question means something different from a recommendation in a specific buying comparison. Knowing which page was cited also gives a team something concrete to inspect, rather than leaving it with an unexplained score.

Imagine that a company’s current documentation explains a capability clearly, but an AI answer describes an older version. A sensible response would begin by examining the cited material and the clarity of the current documentation. That is a more useful starting point than publishing another generic article about the company.

This is an illustrative example, rather than a reported customer result. Its point is the connection between observation and action: a measurement becomes valuable when someone can use it to decide what to change.

Giving the marketing team a next step

On its current website, igeo.ai presents a workflow that moves from identifying gaps to preparing content and distributing approved work. It describes recommendations delivered into the channels where teams already work, with people reviewing and approving material before publication.

The emphasis on approval has a practical purpose. Brand content contains factual commitments: what a product does, who it serves and what evidence supports its claims. A useful assistant can reduce preparation work while leaving those commitments with the people responsible for them.

The same website illustrates a common marketing problem with a fictional email platform: a competitor has stronger customer evidence, while the brand’s own comparison material concentrates on features. The proposed response is to improve the proof available to buyers. The product’s ambition is to help a team make that kind of focused change repeatedly.

Execution introduces its own test. Producing more material is easy to count. Producing material that answers a real customer question requires judgement about relevance, accuracy and timing. Ohayon’s opportunity lies in helping teams use their effort well as the answers they monitor keep changing.

The measurement has to reach the business

igeo.ai’s analytics page describes bringing AI referrals and crawler activity together with information from Google Analytics, Search Console, Bing Webmaster Tools and Cloudflare Analytics. The intended result is a connected view of discovery, website visits and conversions.

That is the right territory for the business question, although the categories should remain distinct. A crawler reading a page is evidence of access. A citation is evidence that a page appeared as a source. A visit and a purchase are subsequent events. None automatically proves the next, and a change over time needs interpretation before it becomes a claim about cause.

For a founder selling a new discipline, explaining those differences is part of the work. StartupCon Europe’s 2026 programme lists Ohayon with a workshop on making GEO profitable. The title reflects the question a customer is likely to bring to any new marketing category: how does this help the business?

A life beyond the product roadmap

Away from software, Garlick describes Ohayon as an angel investor and former co-owner of Tel Aviv’s Mondo 2000 rooftop venue. She also notes his interest in house and techno DJing, tennis and golf.

A global ambition, built one decision at a time

The AI InterConnect episode description frames part of Ohayon’s ambition as building a global AI company from Cyprus. It is an ambition with a demanding product challenge underneath it. Different customers will ask different questions, in different markets, while the AI services answering them continue to evolve.

That leaves room for a company able to connect technical observation with clear editorial and commercial judgement. It also places a premium on restraint: a recommendation is more useful when a team can see the evidence behind it and understand why the action fits its goals.

Ohayon’s next chapter brings those concerns together. The question facing igeo.ai is whether it can help businesses become easier to understand and better supported by public evidence at the moment a customer asks for advice. For a founder whose career has crossed software and marketing, it is a fitting place to put the next product to work.

Reporting note: this original Sirlero profile draws on the UK Titles feature, AI InterConnect’s published episode notes, the StartupCon programme and igeo.ai’s public product materials. Company descriptions are attributed to those materials; the examples and analysis are Sirlero’s.