AI readiness assessment that ends in working systems

The assessment is designed to end in builds, not a binder. We scope, build, and harden agentic workflows around your highest-value use cases, with human-in-the-loop gates that survive production. Not every process earns an agent: the five-question test we run before building one is written up in Agentic workflows: when not to use them. Alongside the builds runs an adoption and governance program: training curricula, champions programs, approval gates, and ROI measurement, the change-management layer that determines whether AI sticks.

You get systems your team runs without us. The AI practice sits alongside the rest of the service line, so marketplace and monetization work can draw on the same team. When the gap is leadership rather than a build, the fractional executive practice embeds our founder part-time.

The repetitive work stays manual because automating it feels risky, and the automations that do get built die in the gap between a convincing demo and daily work, where nobody owns the workflow and nobody finds out when the output is wrong. AI automation consulting here gives those hours back with a verification gate in front of the result, so the saving is real and a wrong answer is caught before it reaches anyone.

The work starts with the automation decision, not the tool. An agentic workflow earns its cost and its latency only where the task is multi-step, adapts to what it meets, and has to decide on intermediate results. Where the decision tree can be drawn, we build the decision tree: explicit branches cost less, can be tuned at every node, and stay controllable. Reaching for an agent because the tree is tedious to write rather than impossible to know is the common and expensive error, and the signals that separate the two cases are in when not to use an agentic workflow.

The gate is the other half, and its job is verification rather than approval: compare the output against a reference, cheaply, every time, which is the argument in human in the loop AI, verify rather than approve. Autonomy stops being defensible when errors are both costly and hard to detect, so the question in every build is what plays the role that tests play for code; if nothing does, the work runs as a script or a person checks it. A gate also has to be a control that runs outside the discretion of the model, because an instruction written into a prompt raises the odds of compliance and guarantees nothing.

Where automation does not fit, we say so. Fully specified work becomes a script, not an agent. High-stakes steps with no cheap check stay with a person until one exists. And anything we build gets run after launch: we track real usage, watch for quality slipping, and size testing to the cost of a mistake.