Use Cases

What teams really build.

Not a catalog from a consulting brochure, but what actually gets built in our hackathons – sorted by department.

Aufgeräumter Schreibtisch mit Laptop
Person arbeitet konzentriert am Laptop
Arbeitsplatz mit Notizbuch und Laptop

By department

Use cases from real projects

Concrete applications teams built in our hackathons – sorted by department.

Marketing

  • Content engine: briefing in, blog articles, social posts and newsletters out
  • Campaign landing pages in hours instead of weeks
  • Competitor monitoring as an automated weekly report

More on this in the AI Hackathon for Marketing.

Sales

  • Account research agent with pain points and conversation hooks
  • Personalized outbound sequences from CRM data
  • Proposal and follow-up generator straight from call notes

More on this in the AI Hackathon for Sales.

HR & People

  • Onboarding assistant that answers new hires' questions
  • Job postings and interview guides from role profiles
  • Application screening with clear, documented criteria

Finance

  • Invoice and receipt extraction into structured data
  • Forecast commentary and variance analysis at the push of a button
  • Automated approval workflows with review steps

More on this in the AI Hackathon for Finance and Accounting.

Operations

  • Internal tools for recurring processes without an IT ticket
  • Connect systems: CRM, ERP, ticketing via automation flows
  • Automatically generate documentation from process recordings

Customer Support

  • Response suggestions from knowledge base and ticket history
  • Automatic classification and prioritization of requests
  • Self-service assistant for the top 20 questions

Legal & Compliance

  • Contract review against your own standard clauses
  • Policy assistant for questions from within the company
  • Risk classification of AI projects under the EU AI Act

Product & IT

  • Working prototypes for feature ideas in one day
  • Agentic coding in existing codebases: tests, refactors, migrations
  • Internal APIs and data pipelines as a working proof of concept

More on this in the AI Hackathon for Engineering Teams.

What other customers say

Voices from real Corporathons.

Verbatim excerpts from public LinkedIn posts by participants.

„24 hours in our office. Pizza everywhere. 5 teams racing to build something real with AI."

Marie Klamer

YOYABA · LinkedIn-Beitrag

„Turns out that's all it takes to go from "I should really learn AI" to actually shipping a working product. No coding experience needed."

Marie Klamer

YOYABA · LinkedIn-Beitrag

„By the end of day one, people who had never touched a terminal were working in Claude Code and building solutions of their own."

Okechukwu Ukaigwe

NavVis · LinkedIn-Beitrag

„You don't need months of preparation. Just a team, a bit of chaos, and the willingness to start."

Lisa Kral

YOYABA · LinkedIn-Beitrag

„24 hours in our office. Pizza everywhere. 5 teams racing to build something real with AI."

Marie Klamer

YOYABA · LinkedIn-Beitrag

„Turns out that's all it takes to go from "I should really learn AI" to actually shipping a working product. No coding experience needed."

Marie Klamer

YOYABA · LinkedIn-Beitrag

„By the end of day one, people who had never touched a terminal were working in Claude Code and building solutions of their own."

Okechukwu Ukaigwe

NavVis · LinkedIn-Beitrag

„You don't need months of preparation. Just a team, a bit of chaos, and the willingness to start."

Lisa Kral

YOYABA · LinkedIn-Beitrag

02 – Get Started

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