FAQ
Process
The diagnostic call is a working session where I review your operational pain points with you. You leave with an initial diagnostic of where AI could measurably reduce operational overhead, accelerate execution, or replace manual work.
I evaluate each candidate on 4 axes: ROI, feasibility, risk, and production complexity. A strong fit has repeated work, clear business impact, available data, a workflow owner, and a path to daily use in your operations.
Build
Every agent is built to order, fitted to your workflows, your systems, and your business requirements. It fully integrates with the enterprise stack you already run.
Most engagements move from discovery to a production agent system in 6 – 12 weeks, depending on workflow complexity and the systems involved.
Your team provides context on the workflow and the systems involved so what I build matches how work actually happens. This takes roughly < 20 hrs total across all your employees, concentrated in the first 2 weeks of discovery.
Afterwards, I handle all AI development and deployment while keeping your teams in the loop. That is, unless your team wants to be part of the development too!
Trust
Data privacy and security are designed into your agent systems upfront — both what data your agents can access, and what data is retained by model providers.
If your enterprise isn't already on a Zero Data Retention agreement with your chosen provider, I'll walk you through the process.
I closely monitor the agent system after deployment. Performance, failures, exceptions, and business impact are quantitatively tracked and used to improve the system over time.
You get a live KPI dashboard tracking agent performance, cost savings, and time recovered. I stay with you until the system is optimized.
Engagement
Engagements are scoped based on workflow complexity and integration depth, with agreed success metrics before build begins. I share full pricing once I understand scope. Most enterprises that work with me recover cost within 120 days of production, and spend far less than it takes to hire an in-house AI engineering team.