One person. Three ways to help.

Most AI projects fail not because of the technology, but because of a lack of user adoption. My background in psychology and behavioural economics allows me to design AI systems with human-centric adoption baked in from day one. By understanding mental models and reducing friction, I build tools that teams actually want to use. Combined with full-stack development experience, this ensures your AI investment delivers both technical excellence and real-world results.

Partnering with a senior specialist ensures high-trust execution and direct access to expertise without the overhead of account managers or junior developers learning on your project. I build systems your team can own, prioritising clean architecture, thorough documentation, and seamless knowledge transfer. To ensure long-term stability, I work with clients to either train their internal teams or establish clear, sustainable maintenance agreements.

Off-the-shelf tools are great for general tasks, but custom AI infrastructure is required for deep integration with your tech stack, proprietary data usage, and enforcing complex business rules. I build operational systems that automate multi-step workflows — syncing CRM data, running inventory checks, routing proposals for approval — creating a competitive advantage that a standard chatbot or API wrapper cannot provide.

I bridge the gap where most AI projects fail: the translation between business requirements and technical feasibility. My background as a Foundation of Business scholar combined with hands-on engineering means I can architect systems that are both technically sound and economically sensible. This ensures your project is aligned with your CFO's goals while remaining highly implementable for your engineering team.

I define clear success metrics — cost reduction, time saved, or revenue impact — before writing a single line of code. By validating assumptions with small, early experiments, we avoid the trap of building AI for its own sake. If a simpler automation or even a spreadsheet solves the problem more efficiently, I will tell you. My focus is on solving your business problem, not on maximising billable hours.

The choice depends on your goals and timeline: n8n and Make are ideal for rapid prototyping and validating workflow ideas with minimal investment, while custom code in Python or Elixir/Phoenix is reserved for high-performance applications, complex business logic, or production-grade reliability requirements. I select the right tool for your current stage, ensuring you optimise for speed today without sacrificing scalability tomorrow.

I prioritise production-ready engineering over hype-driven demos. By working directly with your technical teams, I ensure every implementation is robust, maintainable, and survives the scrutiny of experienced developers. My reputation is built on delivering reproducible results that add long-term value, rather than one-off prototypes that collapse after the initial excitement fades.