Data, Software & AI Engineering | dleblanc.io
This might be you...
> You have an AI idea or prototype that looks promising, but turning it into something reliable and scalable feels murky.
> You want to move fast, but hiring a team or committing to a big build feels risky when the data and system boundaries aren't clear yet.
> Your demo works, but the real question is whether it will hold up once customers, uptime, or regulatory constraints start to matter.
Building AI is easier than ever.
Making it production-grade is not.
Most teams hit the same wall: the system works, but everything underneath it is brittle. Pipelines aren’t built for change and the operational problems show up late, usually under pressure.
The difference between a convincing prototype and a production-grade system isn’t a better model. It’s the unglamorous first-principles work that keeps things from breaking once real and users enter the picture.
How I work
I work closely with a few teams at a time, on technical direction, system design, and building the parts that have to hold up in production.
My role usually spans both strategy and implementation: I help make the consequential calls, then build the systems that depend on them.
Engagements are usually multi-month and deeply hands-on.
Case Studies
These were moments where a wrong technical decision would have been expensive to unwind.
> Shipping a HealthTech AI assistant under constraints
A founder had a solid AI concept but no clear path to production. I architected the HIPAA-compliant system and shipped a production-ready assistant instead of a prototype that wouldn't survive scrutiny.
> Scaling an AI system past its breaking point
An experienced AI team had a working system that couldn't scale and was difficult to debug. I rebuilt the data pipeline and software infrastructure, enabling 50× scale while cutting marginal costs by roughly 90%.