Engineering Monkeys Product Talks

Product Talks – Peldi (Giacomo Guilizzoni) telling the story behind Balsamiq

In this episode of Product Talks, Peldi (Giacomo Guilizzoni) talk to us about his journey at Balsamiq:

* Peldi, founded Balsamiq, a low-fidelity wireframing tool, in 2008 after working at Macromedia and Adobe. The idea for Balsamiq originated from observing a product manager struggle to create mockups in Excel and PowerPoint to express visual ideas.

* Peldi decided to quit his job at Adobe to spend a year building Balsamiq, which quickly became successful upon its launch. Despite initially wanting to remain a “solopreneur,” the overwhelming success and customer service requests forced him to start hiring, with the company now employing around 30 people across Italy, Germany, France, Illinois, and California.

* Balsamiq’s growth strategy has been to expand as slowly as possible without outside investment, aiming for a small, efficient team with good profitability rather than rapid, high-revenue growth, to avoid corporate politics and maintain focus on customer problems. The core concept of the product – low-fidelity wireframing for user interfaces – has remained consistent, evolving primarily through adding and pruning features and transitioning from desktop to a cloud-primary version based on customer feedback and market forces.

* The company’s current structure includes three main departments: CX (Customer Experience, encompassing marketing and support), UX (User Experience, covering product, user research, design, and engineering), and EX (Employee Experience, handling admin, HR, and legal), with product engineering making up 18 of the 30 employees. Feature prioritization follows a “bottom-up” process, where ideas are categorized as “must-do” or “would really like to do,” with decisions based on company goals and strategy.

* Balsamiq is integrating AI into its wireframing tool, introducing an AI assistant that can generate initial site flows from prompts while still allowing for manual editing, aiming to make prototyping much faster without generating shippable code. This approach aims to accelerate iteration and enhance communication, especially for non-designers, recognizing that current AI prototyping tools are often too slow or complex for quick feedback.