Most AI content tools generate a post and stop. Reactable AI, a bootstrapped startup from Longford, Ireland, is betting that the real value is in the feedback loop. The company's pitch is that its software doesn't just create social media posts and landing pages, it watches how they perform and uses that data to improve the next batch of content [The Irish Times, Oct 2025].
The wedge of proprietary UX
Reactable's stated differentiation sits in a layer above the generative AI models. Co-founder Michal Malewicz brings over 25 years of experience designing for global brands, and the team claims to have codified that knowledge into a proprietary set of UX rules [The Irish Times, Oct 2025]. The idea is to apply proven design principles for conversion to the AI's output.
Founders and Team
Incorporated in June 2023, Reactable AI is led by CEO Brian Egan and co-founder Michal Malewicz [Vision-Net, retrieved 2026] [The Irish Times, Oct 2025]. The team's small size, reported as three people in 2025, reflects its bootstrapped, pre-seed stage [GetLatka, 2026]. The company gained validation by being selected for the 2025 AI Ecosystem Accelerator Programme at University College Dublin's NovaUCD [NovaUCD, 2025].
| Founder | Role | Background Note |
|---|---|---|
| Brian Egan | CEO & Co-Founder | Previously CEO of Purpledecks, based in Longford [LinkedIn, retrieved 2026]. |
| Michal Malewicz | Co-Founder | Design professor, author, and lecturer with 26+ years of experience [michalmalewicz.com, retrieved 2026]. |
| Szymon Adamiak | Co-Founder | Listed as a founder in accelerator materials [NovaUCD, 2025]. |
| Barry Gough | Co-Founder | Listed as a founder in accelerator materials [NovaUCD, 2025]. |
The scaling challenge for a bootstrapped bet
Reactable AI's current metrics, while unverified, paint a picture of early traction. Third-party data service GetLatka reported 2025 revenue of $330,000 (estimated) on a team of three [GetLatka, 2026]. The company's participation in the NovaUCD accelerator suggests a focus on refining its model. Reactable's success hinges on proving that its self-learning mechanism and design rules deliver measurably better results than simpler, cheaper alternatives.
The technical breakdown is straightforward in concept but complex in execution. The system needs to ingest performance data, map it back to specific design and copy elements, and adjust its generation parameters accordingly. At scale, the feedback loop's effectiveness depends on receiving consistent, high-quality engagement data from users.