Deploy, not advise. I’m here to put working systems in place, not to rent you my vocabulary. Every engagement follows the same shape — observe → design → build → transfer — and starts by understanding your workflow before choosing any model or tool.
1. Observe first#
We start with the workflow, the baseline, the risk, and the value case — before choosing a model or tool. That means looking at how the work happens today, where time leaks or rework shows up, how ready the data is, and what constraints matter (approvals, data residency, hosting).
The entry point is deliberately low-commitment: the value check gives you a rough estimate, and the first discovery call is 30 minutes, no charge, and no pitch deck — we work out whether there’s a useful workflow to build and what “observe first” looks like for your context.
2. Design#
Once the workflow is clear, I propose the smallest useful system that creates value: a workflow value map, a feasibility and risk review, and a practical path — with human review, permission boundaries, and governance designed in from the start, not bolted on later. If the honest answer is “no fit yet”, a smaller diagnostic, or deterministic automation, I’ll say so.
3. Build#
I build with tools your team can already support — Microsoft 365, Python, APIs, KNIME, MarkLogic, local models, or your existing systems — so nothing depends on a stack only I understand. Where it makes sense, we prove one workflow first: a working pilot with real users and human approval points, and before/after measurement, before anything scales.
4. Transfer#
You own what I build. Documentation and handover are part of the scope, not an extra invoice. Governance, permission boundaries, and a clean handover matter as much as the automation itself — so the system stays yours to run, with no lock-in. If it can’t be handed over cleanly, it isn’t finished.
What we’ll ask of you#
You don’t need to become AI experts, and you won’t be handed a system to run alone. What an engagement genuinely needs from your side is straightforward:
- Someone who knows the workflow. Observe first is mostly about understanding how the work happens today — so early on, the main ask is access to the people and examples that show the real process, its inputs, and where it hurts.
- Domain knowledge, not technical work. You bring the judgment about what “good” looks like and which constraints matter; I handle the building.
- A reviewer who stays in the loop. Human review and approval points are built into every phase — we agree who on your side reviews and signs off, so the system reflects your judgment, not just mine.
- The constraints, up front. Data residency, tool approvals, and risk are far easier to design around when they’re named early rather than discovered late.
The time commitment is agreed up front and scoped to fit your team’s capacity. Heavier involvement early — showing us the real workflow — tends to produce a better result, but the pace is set with you, for your context, not imposed.
Measure, don’t assume#
If we can’t point to time saved or risk reduced, we haven’t finished. Value is the test at every phase — which is also why optional success-fee or value-share terms only apply where value can be verified, never from website inputs.
How this maps to the packages#
The packages are entry points into the same model: an AI Quick Diagnostic is a light observe; an AI Value Finder is observe + design; an Agentic Value Pilot is build and prove one workflow; and a Managed AI Value Partner operates, monitors, and improves it over time. You start with the phase that fits.
Timelines and scope are worked out per engagement, for your context — this page describes the model, not a fixed schedule.
Start with the value check, or book a free discovery call to talk through what “observe first” would look like for your workflow.