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
.jpg)
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.

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.

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.
Keep Reading
As workplace knowledge spreads across chats, AI firms are building systems that can structure, retrieve and preserve it over time.
Updated
May 26, 2026 5:40 PM

A messaging app on a phone. PHOTO: ADOBE STOCK
Votee AI, an enterprise AI company headquartered in Hong Kong, has partnered with its Toronto-based research lab Beever AI to launch Beever Atlas. The new platform is designed to turn workplace chats into searchable knowledge that AI systems can retrieve and understand.
The release focuses on a growing issue inside organisations. Much of today’s workplace knowledge now exists inside chat platforms such as Slack, Microsoft Teams, Discord and Telegram. Important discussions, project decisions and technical information often disappear into long message histories that are difficult to search later.
Beever AI developed the platform to organise those conversations into a structured system for AI assistants. The software connects with Telegram, Discord, Mattermost, Microsoft Teams and Slack, then converts conversations into linked records of people, projects, files and decisions.
The collaboration combines Votee AI’s enterprise infrastructure work with Beever AI’s research around AI memory systems. The companies are releasing two versions of the product. The open-source edition is aimed at individual developers, researchers and creators. The enterprise edition is designed for banks, government agencies and larger organisations with stricter security requirements.
The release also reflects a broader shift happening across the AI industry. Companies are increasingly looking at how AI systems store and retrieve long-term knowledge, rather than relying solely on large context windows or search-based retrieval.
Earlier this year, OpenAI founding member and former director of AI at Tesla Andrej Karpathy discussed the growing need for what he described as “LLM Knowledge Bases.” He argued that AI systems need structured and evolving memory rather than depending only on context windows and vector search.
Beever Atlas approaches that problem through workplace communication. Instead of focusing mainly on uploaded files, the system is designed around conversations that happen daily across team chat platforms. It can also process images, PDFs, voice notes and video files within the same searchable system.
The companies say the software is designed to work directly with AI assistants and coding tools such as Cursor, AWS Kiro and Qwen Code. Integrations for OpenClaw and Hermes Agent are expected later in 2026.
Pak-Sun Ting, Co-Founder and CEO of Votee AI said: "Hong Kong has always been known for property and finance. Beever Atlas is proof that world-class AI infrastructure can emerge from an HK-headquartered company and be shared openly with the world. Every growing organization faces the same silent liability: conversational knowledge loss. Beever Atlas turns this perishable resource into a compounding organizational asset."
A large part of the enterprise version focuses on privacy and access control. The system mirrors permissions from Slack and Microsoft Teams so users can only retrieve information they are already authorised to access. Permission updates are reflected automatically when access changes inside company systems.
The enterprise edition also includes audit logs, encryption controls and data retention settings for organisations handling sensitive internal data. Companies can run the software entirely inside their own infrastructure using Docker and connect it to their preferred AI models through LiteLLM.
The companies argue that organising information is more useful than simply storing chat archives. Jacky Chan Co-Founder and CTO of Votee AI said: "The key technical decision was to treat agent memory as a knowledge engineering problem, not a retrieval problem. Structure beats similarity — a typed graph of who works on what is more useful to an AI than vector search over a Slack archive."
The software also includes protections against prompt injection attacks and systems designed to reduce hallucinated responses. According to the companies, the AI is designed to return “I don't know” with citations when confidence is low instead of generating unsupported answers.
As workplace communication becomes increasingly fragmented across chat platforms, companies are beginning to treat internal conversations as information that AI systems can organise, retrieve and build on. Beever Atlas reflects a broader push to turn everyday workplace communication into long-term organisational memory.