5 teams, 24 hours, we ended up with five working prototypes shipping this week.
Case study
NavVis
ProductivityA mixed marketing, sales and product team automated their own weekly routines in two days and kept the tools in daily use afterwards.
Built for
Social proof
What participants post on LinkedIn
Original posts from real Corporathon hackathons, embedded straight from LinkedIn.
View on LinkedIn
Key facts
60%
AI adoption in the team
10
Participants
2 days
Sprint length
Industry
Industrial technology
Team & focus
10 people from marketing, sales and product
Result
60% AI adoption
Format
Blaze (2 days)
Starting point
Research, reporting and content prep were manual and spread across roles. AI was a meeting topic, not part of daily work.
Approach
- Challenge selection along real weekly routines of the three functions
- Parallel building in mixed teams with technical review
- Tool stack setup and handoff documentation for every participant
Result
- 60% of the team keeps using the assistants they built
- Reporting and research steps now run automated
- Internal champions drive further use cases themselves
Customer voices
What participants publicly say about the hackathon
Summarised takeaways from approved LinkedIn posts by teams that built with us.
From “I should really learn AI” to a shipped product, with zero coding experience.
No months of prep needed: a team, a bit of chaos, and it turned into a working product.
5 teams, 24 hours, we ended up with five working prototypes shipping this week.
From “I should really learn AI” to a shipped product, with zero coding experience.
After day one, people who had never opened a terminal were building in Claude Code.
OW
Oke Wilhelm
NavVis
The biggest effect wasn’t the tooling, it was the mindset shift: “I can build this myself.”
NT
NavVis Team
Marketing & ops
After day one, people who had never opened a terminal were building in Claude Code.
OW
Oke Wilhelm
NavVis
The biggest effect wasn’t the tooling, it was the mindset shift: “I can build this myself.”
NT
NavVis Team
Marketing & ops
Next step
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Questions
Frequent questions about this case study
Research, reporting and content prep were manual and spread across roles. AI was a meeting topic, not part of daily work.
Challenge selection along real weekly routines of the three functions Parallel building in mixed teams with technical review Tool stack setup and handoff documentation for every participant
60% of the team keeps using the assistants they built Reporting and research steps now run automated Internal champions drive further use cases themselves
There is no certificate at the end, there is a working result: built workflows that keep running in daily operations.
For teams in marketing, sales, operations, product and IT. Coding skills are not required — the facilitation is built so every team ships a working prototype.
Usually one week: scoping call, use-case selection, access and tool setup. After that the sprint is ready to run.
You decide which data enters the sprint. Access, privacy and boundaries are agreed upfront and we only work with approved material.
You do. Prototypes, automations, workflows, prompts and generated code belong to your company after the sprint.
We work with the stack that fits you — LLM APIs, automation and data platforms, and your existing systems.
Teams of four to six work best. Overall we run everything from a single team up to several dozen participants.