Zooming in on 9 Operators who became AI Builders

When asked about their experience at the RMEP AI Hackathon, one portfolio company operator had this to say:

The hackathon was an empowering, game-like learning experience.
I really enjoyed the program, especially because it felt like a game. Building automations is so much fun, you can get lost in it. The hackathon was great for getting out of our usual routine and working together. There was a big learning curve.
Giorgio De Palo - CRM & Digital Market at calimoto

And they weren't alone in their enthusiasm.

Over the course of 5 months, 15 portfolio companies from RM Equity Partners (and the investment team!) participated in a comprehensive AI enablement program, culminating in a hands-on hackathon where operators turned theory into practice. The program followed a three-phase approach: an AI Literacy online course, an AI Automation Cohort, and finally, the AI Hackathon where participants built real solutions for their companies.

The results? Portfolio company operators building everything from automated invoice processing to customer sentiment analysis tools - solving real business challenges with AI automation, often for the first time.

We asked participants to share what they built during the hackathon, and the submissions showcased incredible creativity and business impact. From finance teams cutting hours of manual work to operations managers creating new visibility into their processes, these builders proved that with the right enablement, anyone can become an AI operator.

Meet 9 operators who implemented a use case for their company during the RMEP AI Hackathon.

Gabor Domsitz: Financial Controller at Alpy

About Alpy

Alpy is an online platform for booking ski and snowboard rentals across Europe. Customers can reserve equipment in advance at ski resorts.

Use case

Automated Strategy Initiative Tracking

The challenge

Alpy uses Miro's visual cards to track strategy initiatives and KPIs. To improve tracking and accountability, the team wanted a more structured system with department filters, automated notifications, standardized forms, and a change history log.

The solution

Gabor built an Airtable-based Strategy Execution Hub with structured tables, linked KPIs, and standardized intake forms. He set up an interface for users to submit and update initiatives, and created Kanban boards, department filters, and timeline views for teams and leadership. He automated reminders, missing-data notifications, and weekly digests via Make, and added a change-history log to track all updates. He also enabled finance to link investment assumptions and ROI.

Business value

The system improves strategy execution, increases accountability, and helps initiative owners keep information up to date. Leadership gains accurate visibility into progress and bottlenecks, teams work from consistent data, and the solution eliminates manual tracking. The links between KPIs, ROI, and milestones enable better decision-making and more reliable quarterly reviews.

Alexander Katzmaier: CEO at Bergfex

About Bergfex

Bergfex is a leading European mountain tourism platform with over 9,500 webcams across Austria and Europe, serving 65 million users annually with weather, ski resort info, bookings, and tour planning.

Use case

Automated Webcam Discovery and Integration

The challenge

Bergfex's webcam network is mostly in Austria, which brings in visitors and keeps users engaged. To expand across Europe, the team needed to find webcams, identify their owners, verify access, and gather information about each one-a manual process that involved research across many different websites.

The solution

Alexander created an automated system to find and add public webcams from across Europe. He set it up so users simply enter a location in a Google Sheet, and the system automatically searches the internet and Windy.com for webcams in that area. The system collects webcam links, removes duplicates, identifies owners, and saves all information in Airtable. He also planned to add a feature that automatically extracts key details from each webcam's website.

Business value

This automation reduces manual effort, improves the accuracy and scalability of webcam integration, and accelerates European coverage. By expanding the webcam network, Bergfex increases its potential reach, attracts new user groups, and strengthens its market presence, supporting millions of additional sessions.

Mariia Terekhina: Investment Analyst at RM Equity Partner

About RM Equity Partner

RM Equity Partners is the investment arm of a 100+ year family business that specializes in acquiring, managing, and operating profitable niche companies. The firm seeks majority stakes in profitable asset-light businesses, with a particular interest in scalable online platforms across Europe.

Use case

Automated AI-Generated Deal Summaries

The challenge

Preparing overviews and summaries of new investment opportunities required extensive manual research to understand each company’s business model, team, financials, and market position. Information provided by M&A advisors or companies was often incomplete or unclear, forcing analysts to spend significant time gathering and verifying data from multiple public sources. This manual process was inefficient, inconsistent, and slowed down the preparation of materials for the Investment Committee.

The solution

Mariia built an AI agent using Make to automate the collection of key company information from public sources-including company websites, LinkedIn, news articles, financial data, and competitors-and, when available, from North Data via API. She set up the agent to parse, clean, and normalize the data, then use an LLM to generate a structured summary covering business model, team, metrics, market, competitors, and key news. The summary is automatically posted to the relevant Notion deal page, with citations and alerts for missing or low-confidence items.

Business value

This automation reduces manual research time, improves the depth and accuracy of investment analysis, and accelerates deal preparation. Summaries are more consistent and comprehensive, enabling the Investment Committee to make better-informed decisions earlier in the process.

Claudiu Silaghi: Developer at Publi24

About Publi24

Publi24 is Romania's leading online classifieds portal, founded in 2009 and headquartered in Oradea. Users can post free ads across categories including cars, real estate, electronics, and services. The platform has over 1 million app downloads.

Use case

AI-Powered Ad Competitiveness Scoring

The challenge

Sellers often wonder why their ads underperform. Common issues include pricing, photo quality and quantity, description strength, or missing keywords. Helping sellers identify and address these issues manually is time-intensive.

The solution

Claudiu implemented an AI Competitive Score system that automatically evaluates each ad against similar listings in the same category. He built it to analyze price competitiveness, photo quantity and quality, description strength, keyword richness, and overall listing completeness. The system generates a competitiveness score for each ad and provides actionable recommendations to help sellers improve their listings. Sellers can apply the suggested improvements and re-score their ads to track progress.

Business value

This solution enables sellers to instantly identify and address weaknesses in their ads, leading to higher-quality, more trustworthy listings. The platform might see faster ad conversions, reduced seller churn, and increased seller satisfaction. The automated scoring and feedback process also ensures more consistent and complete listings across all categories, supporting Publi24’s broader goals of improving user experience and operational efficiency.

Mo Kanneh: SEO Manager at Dv Corporate

About Dv Corporate

Digital Ventures (DV Corporate) is a London-based digital company that has specialized in online marketplaces for over 25 years. The company creates safe and trusted online environments that connect people across the world.

Use case

LLM-Optimised Content Auditing

The challenge

Dv Corporate’s SEO and Content team needed to optimise existing website content for Large Language Models (LLMs) to increase the likelihood of being cited in AI Overviews and generative search results. Traditional SEO tools only addressed technical and on-page audits, leaving a significant gap in assessing content structure for LLMs. The manual process of content modification, competitor analysis, schema creation, and internal linking was time-consuming and inefficient.

The solution

Mo built a tool that allows users to input a URL, which is then analysed by an LLM (such as GPT-4o or Gemini) to generate an “LLM Readability Score.” The tool automatically segments content, checks for clear structure (headings, bullet points, concise paragraphs), identifies question-answer pairs, analyses tone for clarity and factuality, and suggests related topic clusters. It also generates appropriate schema markup for FAQs and “How-To” articles. The output is a prioritised, actionable report with recommendations to improve LLM readability and content authority.

Business value

This solution reduces manual effort, improves the accuracy and consistency of LLM-focused content optimization, and enables the team to scale their SEO strategy. As a result, Dv Corporate enhances its brand visibility in AI-driven search, improves user experience, and increases website traffic.

Giorgio De Palo: CRM & Marketing at calimoto

About calimoto

Calimoto is a motorcycle trip planner, GPS, and tracker app. The platform helps riders discover scenic routes, plan trips in the app or on desktop, navigate with turn-by-turn voice guidance (even offline), and analyze rides by metrics like lean angle, acceleration, and elevation gain.

Use case

AI-Powered Zendesk Ticket Tagging

The challenge

Zendesk previously assigned ticket tags automatically using keyword matching, but this approach sometimes misinterpreted the true context of support requests. Many tickets needed manual correction by the Support team, affecting data quality for analytics.

The solution

Giorgio built an AI-powered ticket classification system that analyzes the full text of incoming Zendesk messages using ChatGPT, rather than relying on simple keyword detection. She set it up so each new ticket is sent to ChatGPT, which identifies the correct topic, category, and device context and returns a structured result. Zendesk then automatically applies these fields and tags.

Business value

This solution delivers more accurate, context-aware tagging of support requests, saving time for the Support team and reducing manual data cleanup. Product and Marketing teams gain more reliable insights into user issues and trends, supporting better decision-making. Improved categorization also enables faster ticket routing and reporting.

Sarah Bernhardt: Support Team Lead at calimoto

About calimoto

Calimoto is a motorcycle trip planner, GPS, and tracker app. The platform helps riders discover scenic routes, plan trips in the app or on desktop, navigate with turn-by-turn voice guidance (even offline), and analyze rides by metrics like lean angle, acceleration, and elevation gain.

Use case

Automated Help Center Comment Alerts

The challenge

Calimoto’s Support team struggled to track new user comments on Zendesk Help Center articles because only article authors received notifications. The existing workaround relied on manual email forwarding from a colleague’s inbox to a Slack channel, which also included duplicate notifications when agents replied. This process was error-prone, difficult to maintain, and cluttered Slack with irrelevant updates, making it easy to miss important user comments.

The solution

Sarah built an automation using Make to connect Zendesk Help Center directly to Slack. He set up a scenario that triggers whenever a new comment is created on a Help Center article, filters out comments from calimoto agents, and sends a Slack message with the comment text and article link to the #comments-help-center channel.

Business value

The automation provides immediate and accurate visibility of new Help Center comments for the Support team in Slack, eliminating manual filtering and duplicate notifications. This leads to faster response times, improved coordination, and fewer missed user comments.

Léna Cerrada: CMO at CBK

About CBK

CBK Interactive is a Paris-based company founded in 2007 that specializes in online dating platforms. Their flagship product is NousLib, a dating site that connects open-minded people and has expanded internationally.

Use case

Social Network Posts Full Automation

The challenge

CBK’s social media content workflow was highly manual and time-consuming, involving multiple steps and exchanges between writers and translators. Creating, translating, and scheduling posts required significant coordination and effort, leading to inefficiencies and delays. The process was further complicated by the need to manage translations and visual assets, making it difficult to scale and track progress.

The solution

CBK implemented several automations to streamline the entire social media posting workflow. Using Notion to store post content and Metricool for scheduling, the team automated content creation with AI-generated posts and translations, allowing translators to simply verify AI outputs. Canva’s API was integrated to automatically generate visual assets from templates, and Make was used to connect Notion, Metricool, and other tools, enabling seamless end-to-end automation. Writers and translators were integrated into the process for content approval and quality control.

Business value

This automation will save CBK around 5 days of work each month, significantly accelerating the content creation and publication process. The integrated workflow improved accuracy, reduced manual effort, and made it easier to track and approve content, supporting greater scalability and efficiency in CBK’s social media operations.

Pascal Keck: Project Manager at Russmedia

About Russmedia

Russmedia is a progressive media company headquartered in Schwarzach, Austria (Vorarlberg), with nearly 100 years of history. With about 1,000 employees across 19 European locations, Russmedia combines daily and weekly newspapers, online portals, a printing plant, logistics, radio, and IT services under one roof. The company attracts over 20 million unique visitors monthly through its digital properties.

Use case

Automated Local News Scraping

The challenge

Monitoring 2,092 Austrian municipalities, local companies, and clubs for news updates manually is not economically viable due to the volume and fragmentation of sources. Tracking scattered local announcements, events, tenders, and emergencies across so many sources requires significant effort.

The solution

Pascal built an MVP scraping cockpit for 10 municipalities, featuring automated agents that continuously scrape websites of municipalities, companies, and clubs. He included regular cron jobs, logging, and alerting, as well as normalizers and parsers for common CMS patterns. He added a basic classifier that sorts content into categories such as announcement, event, tender, emergency, or other, and set up a centralized database to store all data. He used Firecrawl for scraping jobs and GenAI to generate articles from structured data.

Business value

The automated scraping cockpit enables the creation of hyper-local news platforms and newsletters, unlocking new opportunities for unique local stories and targeted advertising. The solution delivers significant time savings, improved accuracy, and scalability, allowing Russmedia to efficiently aggregate and publish local news at a national scale.

Conclusion

These nine operators represent just a fraction of the innovation that emerged from the RMEP AI Hackathon. Each solution tackled a real business challenge, saved time, and proved that AI automation isn't just for technical teams—it's for anyone willing to learn.

The hackathon was the most practical part; I definitely recommend it.
I liked the hackathon because it was the most practical part where you build and can ask questions. You discuss your use cases and share ideas with other participants. AI is the future, so you should definitely learn how to do it, not be afraid of it, and master your skills there.
Mariia Terekhina - Investment Analyst at RMEP

To learn more about 9x's AI enablement programs for portfolio companies and explore how your organization can build similar capabilities, check out our cohort programs.

Congratulations to all the RMEP hackathon participants and happy building!