Three service tiers for different stages of AI adoption. All projects start with a scoping call where I tell you exactly what I'll build, how long it will take, and what it will cost — before you commit to anything.
For businesses with a specific, defined manual process they need to automate. A repetitive workflow eating hours every week — form processing, API syncing, report generation, data movement. I build the pipeline, test it, and hand it off with documentation so your team can maintain it.
A multi-agent analytics layer on top of your existing data infrastructure. Your team asks questions in plain English, the system returns accurate, structured answers in under a second. Role-based access control built in. Replaces expensive BI tool licensing at a fraction of the ongoing cost.
End-to-end agentic AI ecosystem for organizations that need production-grade multi-agent infrastructure with enterprise security, governance controls, and full team enablement. Built for organizations where AI infrastructure is a strategic competitive advantage.
Every project comes with a scoping call, a written proposal, and a fixed timeline before you commit. Here's what the build process actually looks like.
Every engagement starts with a 30–60 minute call where we define the exact problem, map your data sources, clarify the access control requirements, and establish success criteria. By the end of this call, you'll know what I'm going to build, how long it will take, and what it will cost.
I don't charge for the scoping call. If after the call I don't think the project is a good fit — wrong data infrastructure, budget mismatch, timeline incompatibility — I'll tell you directly and point you toward a better path.
I work in short iterations with regular check-ins. You get access to a staging environment before anything goes to production, so you can test the system with real queries against your actual data before I consider the project complete.
I test edge cases, unusual query structures, permission boundary conditions, and load scenarios before delivery. You're not finding bugs in production — I'm finding them in staging.
Live deployment includes CI/CD configuration, monitoring setup, and alert routing. If something breaks after launch, you'll know before your users do.
Documentation is written for the people who will actually maintain the system — not for me to demonstrate that I documented things. Technical runbooks for your engineering team, user guides for your business users.
I don't deliver strategy documents, AI readiness assessments, or roadmaps that require another engagement to execute. If that's what you need, a larger consulting firm is the right choice.
I also don't take on projects where the data foundation isn't ready to support the system. If your data is in a state where an agentic layer would produce unreliable results, I'll tell you what needs to be fixed first — even if that means you're not ready to work with me yet.
The financial case for an AI analytics system versus traditional BI licensing depends on your user count and current tooling. Here's a representative example based on a mid-market company with 200 BI tool users.
Thirty minutes. No commitment. I'll tell you what I'd build for your situation, what it would cost, and whether I'm the right fit for it.