02 · Businesses building AI apps

Your AI experiments are becoming real software.

Internal tools and customer-facing apps can now appear without a traditional software project. Once people depend on them, somebody still needs to understand how the pieces fit together and make the important technical decisions deliberate.

The starting point

Let people experiment without turning the business into an accidental software estate.

The awkward bit is what happens after experiment number three becomes application number twelve: different teams, data flows, hosting choices and integrations, all solving similar problems in slightly different ways.

I help businesses introduce enough technical ownership to keep AI-built applications useful and maintainable — without crushing the initiative that made them possible.

You may be here if

Different teams have built apps, agents or automations and nobody has a complete view of what exists.
An internal prototype is becoming business-critical or is about to face customers.
Shared business data and integrations are being handled differently in every project.
You need someone technical to review decisions and suppliers without building a whole department.

How I help

Map the application estate, ownership, dependencies and data flows.
Review higher-risk applications before they become harder to change.
Create lightweight standards for hosting, data handling, deployment and operational evidence.
Consolidate shared foundations and integrations where duplication is costing you.
Provide ongoing fractional technical leadership or take on bounded engineering work.

What good looks like

People can keep experimenting, but the business knows what it owns, which systems matter and how successful prototypes become dependable software.

Practical technical ownership

Bring me the apps you are worried are becoming important.

We can start with one application, or map the wider estate if the problem is already bigger than that.

Talk about your AI apps