Hi, I'm Leo

I've been building software for 15 years: AWS, Salesforce, Python and, more recently, a lot of AI work with Claude and other LLMs. On the Salesforce side that's Apex, Lightning, CI/CD and deployment pipelines.

Lately a lot of my work is setting up and fine-tuning local models for teams that need to keep data in-house, and writing evals for agents before they go live. About a quarter of my own work is handled by Claude and the rest by local models. When someone asks me which model is better, my answer is always the same: let's test it.

Why Forcebench

Forcebench is where those two halves meet. Salesforce teams are being told AI will write their Apex, build their Flows and answer their admins' questions. Some of that is true. Much of it is untested. The public benchmarks are mostly Python and puzzles; they can't tell you whether a model knows what a mixed DML error is or will bulkify a trigger without being asked.

So I'm building the benchmark I wanted: real tasks, graded by running them, with error bars on every number and every answer in the open. If AI isn't the right tool for a job, I'd rather the numbers say so.

Azul Labs

Azul Labs (Azul Labs Pty Ltd) is my consultancy. I help startups and scale-ups work out what's actually worth building with AI, and then build it: agents, local and fine-tuned models, evals, and the Salesforce and Python work around them. When data can't go to the cloud, I can run the work privately.

My focus is sovereign AI: models my clients own, with open weights, running on infrastructure they control. For a fixed fee I benchmark a shortlist of models on a team's own kind of Salesforce work and report back with a recommendation.

I also keep a digital garden of perpetually incomplete notes on AI, neuroscience, maths and whatever else I'm learning.

Get in touch

The easiest way to reach me is leo@azl.au, or through azl.au. I'm also on LinkedIn and GitHub.