Startup Profiles

Marvelous Is Building AI Infrastructure for Offline Sales

Merve Isler, Founder and CEO of Marvelous, sits down with Ventureport to discuss how AI can help revenue teams find the right rooms, guests and opportunities.

Updated

July 3, 2026 11:39 AM

Marvelous, an AI product company. PHOTO: MARVELOUS

Most B2B sales teams spend their days inside software. They track leads in a customer relationship management (CRM) tool, send emails, build outbound lists and measure every digital touchpoint. Still, many important business relationships move forward in person. A dinner after a conference, a small founder roundtable or a private customer event can do what dozens of cold emails cannot: build trust quickly.  

That is the gap Marvelous wants to fill. The bootstrapped San Francisco-based AI startup is building what founder and CEO Merve Isler calls “Salesforce, but for real life”. Her idea is simple: if companies already use software to manage digital sales, they should also have software to manage the real-world moments that help close deals.  

Marvelous brings data, automation and targeting into in-person go-to-market (GTM) work. It helps B2B sales, revenue, marketing and growth teams plan in-person events such as curated dinners, launch events, mixers, happy hours and conference side events. These formats are already familiar to companies selling to enterprise buyers. The problem is that they are often managed through spreadsheets, venue calls, scattered guest lists and manual follow-ups.  

That creates two problems. First, the process is slow. Teams can spend weeks figuring out who to invite, where to host and how to manage responses. Second, the return is hard to measure. Companies spend heavily on conferences, dinners and private events, yet they often do not have a clear way to know whether an event will influence pipeline before they commit the budget.  

Marvelous wants to pull those pieces into one AI-powered event management platform.  

“Our main target customer is revenue and sales teams,” Isler said. “Because about 40% of deals close in person, but there's no infrastructure built for that—so that's what we do.”  

For Isler, the idea comes from experience. Before starting Marvelous, she worked at Google from Istanbul, managing developer product launches and go-to-market programs across Turkey, Central Asia and the Caucasus. During that time, she helped build more than 100 communities around the world and coordinated thousands of events a year across eleven time zones. The work was fast, local and very manual.

That experience shaped the foundation for Marvelous. Isler saw that building products had become easier, while distribution remained difficult. A startup can now ship software faster than ever. Reaching the right people, in the right setting, is still a different challenge.  

At Google, Isler had to understand how people gathered, communicated and built trust in different markets. A product launch might require developer meetups in dozens of cities, but the right approach could change from country to country. In Kazakhstan, she said, Telegram worked better than WhatsApp. In Afghanistan, Facebook mattered more. Each market had its own habits, and growth depended on understanding those details. Those details and adapt quickly.  

Marvelous grew out of that playbook. Isler saw that offline distribution had patterns, even when it looked chaotic from the outside. The right guest list, the right room, the right timing and the right follow-up could change the outcome of a sales conversation. Most of that knowledge, however, lived in people’s heads. Marvelous is her attempt to turn it into software.  

Inside the platform, a company can start by choosing the kind of satellite event it wants to host: a launch event, a mixer, a happy hour, a brunch, a lunch or a dinner. From there, users can connect their CRM. When Salesforce is connected, Marvelous analyzes contacts and creates relationship scores based on signals such as buying intent, past event activity and how warm a relationship appears to be. If a team also uses an event platform such as Luma or Eventbrite, Marvelous can bring that event data into the picture as well.  

A screenshot of the Marvelous platform. PHOTO: MARVELOUS

The product is built around Maven, Marvelous’ AI assistant. Maven can help plan an event through Slack, iMessage or the Marvelous platform itself. For instance, a user might ask Maven to plan an executive dinner for 18 people within a set budget. From there, Maven can find warm contacts, estimate who is likely to attend, build a guest list, recommend venues, send invitations and run follow-up sequences across email, LinkedIn and SMS. The goal is to let sales teams focus on conversations and closing deals instead of worrying about logistics. Put simply, Marvelous helps a company find the right guests, secure the right venue and understand which event format is likely to work. Instead of guessing whether a US$10,000 dinner will pay off, the company gets a clearer view of the potential return.

A photo of Merve hosting at a recent AI Insiders event. PHOTO: MARVELOUS

AI Insiders, Marvelous’ invite-only network of verified AI and enterprise leaders, is also part of the company’s go-to-market strategy. Rather than asking every customer to build an event from scratch, Marvelous can group several companies that want to reach the same audience into one curated event. Such a setting creates revenue, product feedback and fresh data about what works in different markets. The network spans verticals such as cybersecurity, fintech, robotics, healthcare AI and enterprise SaaS. It now includes members from more than 475 companies, with C-level executives making up nearly half of the community. So far, AI Insiders has facilitated more than 2,000 introductions and contributed to over US$560 million in deals. Isler describes it as one of Marvelous’ strongest go-to-market channels.  

The first AI Insiders event took place at AWS GenAI Loft in San Francisco in April 2026. It brought together 150 curated guests, including AI founders, tier-one investors, enterprise executives and researchers for an evening focused on high-impact conversations and collaboration.  

The timing may work in Marvelous’ favor: Digital outreach is getting louder, and AI will make it easier for companies to send more emails, messages and automated pitches. That may make high-trust in-person conversations more valuable, especially in enterprise sales where relationships take time. At the same time, event budgets need proof. Revenue leaders want to know whether a dinner, roadshow or customer event is worth the cost.  

Marvelous is betting that offline sales will become a measurable category for software. If the company can prove that its relationship scores, event intelligence and AI agent help teams create a stronger pipeline, it could become an important tool for B2B go-to-market teams. The core message of Marvelous is easy to understand: the deals may happen in the room, but data can still power the room.  

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Artificial Intelligence

What Happens When AI Writes the Wrong References?

HKU professor apologizes after PhD student’s AI-assisted paper cites fabricated sources.

Updated

January 8, 2026 6:33 PM

The University of Hong Kong in Pok Fu Lam, Hong Kong Island. PHOTO: ADOBE STOCK

It’s no surprise that artificial intelligence, while remarkably capable, can also go astray—spinning convincing but entirely fabricated narratives. From politics to academia, AI’s “hallucinations” have repeatedly shown how powerful technology can go off-script when left unchecked.

Take Grok-2, for instance. In July 2024, the chatbot misled users about ballot deadlines in several U.S. states, just days after President Joe Biden dropped his re-election bid against former President Donald Trump. A year earlier, a U.S. lawyer found himself in court for relying on ChatGPT to draft a legal brief—only to discover that the AI tool had invented entire cases, citations and judicial opinions. And now, the academic world has its own cautionary tale.

Recently, a journal paper from the Department of Social Work and Social Administration at the University of Hong Kong was found to contain fabricated citations—sources apparently created by AI. The paper, titled “Forty Years of Fertility Transition in Hong Kong,” analyzed the decline in Hong Kong’s fertility rate over the past four decades. Authored by doctoral student Yiming Bai, along with Yip Siu-fai, Vice Dean of the Faculty of Social Sciences and other university officials, the study identified falling marriage rates as a key driver behind the city’s shrinking birth rate. The authors recommended structural reforms to make Hong Kong’s social and work environment more family-friendly.

But the credibility of the paper came into question when inconsistencies surfaced among its references. Out of 61 cited works, some included DOI (Digital Object Identifier) links that led to dead ends, displaying “DOI Not Found.” Others claimed to originate from academic journals, yet searches yielded no such publications.

Speaking to HK01, Yip acknowledged that his student had used AI tools to organize the citations but failed to verify the accuracy of the generated references. “As the corresponding author, I bear responsibility”, Yip said, apologizing for the damage caused to the University of Hong Kong and the journal’s reputation. He clarified that the paper itself had undergone two rounds of verification and that its content was not fabricated—only the citations had been mishandled.

Yip has since contacted the journal’s editor, who accepted his explanation and agreed to re-upload a corrected version in the coming days. A formal notice addressing the issue will also be released. Yip said he would personally review each citation “piece by piece” to ensure no errors remain.

As for the student involved, Yip described her as a diligent and high-performing researcher who made an honest mistake in her first attempt at using AI for academic assistance. Rather than penalize her, Yip chose a more constructive approach, urging her to take a course on how to use AI tools responsibly in academic research.

Ultimately, in an age where generative AI can produce everything from essays to legal arguments, there are two lessons to take away from this episode. First, AI is a powerful assistant, but only that. The final judgment must always rest with us. No matter how seamless the output seems, cross-checking and verifying information remain essential. Second, as AI becomes integral to academic and professional life, institutions must equip students and employees with the skills to use it responsibly. Training and mentorship are no longer optional; they’re the foundation for using AI to enhance, not undermine, human work.

Because in this age of intelligent machines, staying relevant isn’t about replacing human judgment with AI, it’s about learning how to work alongside it.