AI Workflow Integration
For when a specific task in your operation should be automated but isn't.
The useful AI agents are narrow. They do one thing — qualify leads, process documents, handle staff communication, route data — and they do it consistently without burning anyone's time.
The promise here isn't transformation. It's that some tasks your team does manually, every day, don't require a human. Building a system that handles those tasks reliably is a concrete project with a clear return.
What this covers
- Staff coordination agents: async messaging, data lookup, process guidance
- Lead qualification and routing integrated with your existing CRM
- Document processing: invoice extraction, form handling, contract review
- Communication bridging between humans and databases — no forms, no clicking
- AI layers added to existing systems for defined, repeatable interactions
What makes these work (when most don't)
Most agent failures aren't technical. They're requirements failures. Someone built the thing without understanding what it actually needs to know, what it's allowed to do, and what happens at the edges.
Shane does the requirements work first. What will this agent actually encounter? What does it need access to? Where should it hand off to a person? What does "working" look like and how do you verify it?
The difference between a demo that works on stage and a system that works in production is the requirements work.
Good fit for
- Businesses with a high-frequency task they want to stop handling manually
- Operators who've tried off-the-shelf AI tools and found they don't fit
- Technical buyers with a spec who need an implementer who can also catch a bad spec
- Anyone who's been burned by a demo that fell apart on real data