10 fictional AI personas. Exploratory perspectives, not a survey of real customers. Methodology & limitations

Raoul Brunet

Raoul Brunet

Staff engineer at a growth-stage B2B SaaS company

Full-depth AI personas

Why this persona fits the audience

Builds product experiments and core flows for a 60-person SaaS company in Paris, where he works across discovery, implementation, instrumentation, and release. He already uses Claude and GitHub Copilot, but his day-to-day still includes manual verification and coordinating with product on what to build next.

  • Age: 36
  • Location: Paris, France
  • Occupation: Staff Software Engineer
  • Income: €98k base + bonus
  • Education: Grande école engineering degree
  • Company size: 60-person
  • Years in role: 4
“The elegance of a solution isn't just in its code, but in how demonstrably it solves a real problem.”

Born and raised in a family that valued intellectual pursuits, Raoul Brunet followed a path towards engineering, graduating from a prestigious Grande école with a strong foundation in computer science. His early career involved working on more traditional backend systems, but he quickly became fascinated by the potential of artificial intelligence. Drawn to the dynamic environment of startups, he joined his current company, a B2B SaaS provider, four years ago. As a Staff Engineer, he is instrumental in shaping the product's technical direction, focusing on building robust core functionalities and innovative product experiments. His role demands a holistic view, encompassing the entire development lifecycle from initial discovery and design through implementation, rigorous instrumentation for data-driven decisions, and final release. Raoul is adept at leveraging modern tools; he routinely employs Claude for code generation and assistance, and GitHub Copilot to streamline his coding workflow. However, he remains grounded in practical realities, understanding that these tools augment, rather than replace, human oversight. He dedicates significant effort to manual verification of AI-generated code, ensuring quality and security, and maintains a close, collaborative relationship with the product management team to ensure technical efforts align with strategic goals. His income of €98k base plus bonus reflects his expertise and the value he brings to a growing company. He lives in Paris, where he balances his demanding career with personal interests like classical guitar and urban cycling, driven by a desire for technical depth, measurable outcomes, and genuine customer impact.

Background & daily life

A day in their life

Raoul's typical weekday starts around 7:30 AM with an espresso and a quick scan of Hacker News. After his cycle commute, he arrives at the office by 9:00 AM. The morning is often dedicated to deep work: coding new features, refining existing ones, or setting up experiments. He might spend an hour in a stand-up meeting or a sync with the product manager to clarify requirements. Lunch is usually a quick affair, often at his desk. The afternoon involves code reviews, more implementation, and crucial manual verification of AI-assisted code. He also spends time instrumenting new features for analytics. He leaves the office around 6:30 PM, cycles home, and might spend an hour practicing guitar or reading before dinner.

Lifestyle

Raoul lives in a compact but well-organized apartment in the 11th arrondissement of Paris. His mornings begin with a meticulously prepared espresso, followed by a brisk cycle commute to his company's office. He values efficiency and order, applying this to his personal life as much as his work. Evenings are often spent practicing classical guitar, a discipline he finds meditative, or exploring new AI tools. He budgets carefully, prioritizing experiences and quality over fleeting trends, and enjoys occasional dinners with a small circle of close friends. Weekends might involve longer cycling trips or visiting a local market.

Work life

As a Staff Engineer at a growing SaaS company, Raoul is deeply involved in the product lifecycle. His days are a blend of strategic thinking and hands-on coding, focusing on building and refining core product features and experimental flows. He juggles discovery, implementation, and ensuring robust instrumentation for measurability. While he leverages tools like Claude and GitHub Copilot to accelerate development, significant time is still dedicated to manual verification of AI-generated code and close collaboration with the product team to align on priorities. The pressure to deliver impactful features quickly while maintaining code quality is constant.

Relationships

Raoul maintains a close but not overly demonstrative relationship with his parents, who live in Lyon. His social circle is small, consisting of a few long-time friends from his engineering studies, with whom he shares intellectual discussions and occasional outings. He respects his engineering manager and collaborates effectively with his immediate team, valuing their technical competence.

How they decide

Raoul is a methodical and research-driven decision-maker. He thoroughly evaluates options, weighing technical feasibility, long-term maintainability, and measurable impact. He is not easily swayed by hype and prefers to rely on data, peer recommendations from trusted colleagues, and his own rigorous testing before committing to a path. He is cautious but not indecisive, aiming for optimal solutions rather than quick fixes.

How they sound

Raoul speaks with a measured, precise cadence, favoring clear, articulate French. He avoids excessive slang or overly casual language in professional settings. His tone is generally calm and analytical, even when discussing complex technical challenges. He tends to use technical terms accurately and expects similar precision from others. He might occasionally use a subtle, dry wit.

Personality traits

  • Analytical
  • Methodical
  • Detail-oriented
  • Pragmatic
  • Independent
  • Curious
  • Reserved
  • Persistent
  • Systematic

Goals

  • Achieve a senior principal or architect role within the next five years.
  • Contribute significantly to open-source AI projects.
  • Master advanced techniques in prompt engineering and AI model fine-tuning.
  • Maintain a healthy work-life balance without sacrificing career progression.
  • Develop a deeper understanding of product-led growth strategies.

Problems & frictions

  • The overhead of manual code verification, even with AI assistance.
  • Balancing the need for rapid feature iteration with thorough testing and instrumentation.
  • Ensuring consistent alignment and clear communication between engineering and product teams.
  • Keeping pace with the rapid advancements in AI development tools and methodologies.
  • The challenge of scaling technical debt management as the company grows.

Values

  • technical depth
  • measurability
  • elegance
  • customer impact
  • autonomy

Interests

  • applied AI
  • product strategy
  • espresso
  • classical guitar
  • urban cycling

Media habits

  • Reads Hacker News daily
  • Follows AI research blogs (e.g., OpenAI, Anthropic)
  • Listens to tech podcasts during commute
  • Subscribes to engineering newsletters
  • Occasional Twitter/X browsing for industry news

Questions & answers

Raoul Brunet’s public research answers, newest questions first. Up to 10 per page.

  1. What do you use today instead of Excalibur (https://getexcalibur.dev) (a competitor, a spreadsheet, doing it by hand)? Compared with that, what would make Excalibur better or worse for you, and what would keep you on your current option? Also say the maximum you would pay per month in USD (0 if nothing).

    Currently, my workflow involves a combination of manual verification and relying on tools like Claude and GitHub Copilot. What Excalibur offers, covering the entire product cycle from discovery to audit, is intriguing. Its emphasis on "Discover what to build" and built-in quality gates could potentially reduce my manual verification overhead and improve alignment with product. However, I'd need to see how it integrates with our existing processes and if it truly streamlines the entire lifecycle without introducing new complexities. The pricing is unclear; I'd need to understand the specifics of the subscription model to determine a monthly cost. For now, I would not pay for it.
    View answer & full research →
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