These are not four products. They are four stages of the same job — decide what to change, build it, put it into production, and get it used. We do all four.
AI transformation
What it is: Changing how the work itself is done — the process, the decision rights, the data, and the way results are measured. The test is simple: is AI making an existing process faster, or letting you redesign the process? Only the second one is transformation.
What you get: A value map that ranks where AI moves revenue, EBITDA, and working capital; the redesigned workflow behind the top opportunity; and a 30-day Proof of Value that puts a number on it before you commit further.
Agentic AI implementation
What it is: Agents that carry out multi-step work on their own — pulling the data, making the call, taking the action — instead of waiting to be asked a question. Production-grade means access control, evaluation, logging, and a hand-off to a person when confidence is low.
What you get: Agents running against a real workflow — reconciliation, churn scoring, pricing, exception triage — with confidence scoring, monitoring, and the runbook your team uses to operate them after we go.
Applied AI
What it is: Not a stage — a discipline. Start from one named business problem and build the smallest system that moves its number. The opposite of buying a platform and then hunting for use cases to justify it.
What you get: One problem, one system, one metric it has to move — with the baseline measured before we start, so the payback is a number you can defend at the board rather than an estimate.
AI enablement
What it is: The readiness and adoption layer — the skills, workflows, and ways of working that decide whether the system is still used ninety days after launch. Building the model is the First Mile. This is the Last Mile, and it is where most AI programs stall.
What you get: Workflow and role redesign, training for the people who do the work, and adoption tracked as a number — not a launch email and a hope.
