Consulting that ships a working workflow, not a deck.

AI automation consulting is advisory work on which business workflows to automate with AI, in what order, with what architecture, and under what governance. Traditional consulting stops at the slide deck. Our model continues into production: same team scopes the strategy AND builds the workflow.

Three differences. (1) Engagement length: 2-3 weeks Discovery vs 3-6 months strategy phase. (2) Deliverable: working production workflow + the deck vs the deck alone. (3) Outcome accountability: we baseline KPIs in Discovery and report against them weekly during Run; traditional firms transfer ownership to your internal team at the end of strategy. Pricing also differs by 5-15× per equivalent workflow.

Phased fixed-price. Discovery (the strategy phase) is $5-8k for 2-3 weeks and includes workflow mapping, KPI baseline capture, risk model, architecture recommendation, and Build SoW. If you commit to Build, $15-40k for 6-10 weeks. Optional Run at $2-6k/month, month-to-month. Discovery is the only commitment to start.

Some do. Most don't. The typical AI consulting engagement ends with a recommendation; you then hire engineers (or a separate vendor) to build. We close that gap: same team scopes the strategy AND ships the production workflow. Eliminates the handoff loss that kills 60-70% of AI initiatives between strategy and execution.

Hire a pure consultant if you need brand-name cover for an investment decision (e.g. you need McKinsey's logo on the recommendation for the board). Hire an AI agency if you need the workflow actually built. Our model collapses both into a single engagement — strategy + build with the same team. Cheaper, faster, and the team that scoped the work owns the result.

Common pattern: we own Discovery and the operating-model design, your team owns the Build, we stay on as advisory during Run. The agency engagement front-loads the architecture decisions and reference implementations; your team takes them in-house and adapts. Documented in the Build SoW as a handover plan.

Practically, they overlap, and we do both. AI process automation targets a repeatable business process end-to-end — intake, triage, decision, routing, response — like claims adjudication, invoice coding, support ticket resolution, or compliance review. AI workflow automation targets the steps inside that process: classification, extraction, summarization, drafting, retrieval. Most engagements are both: we automate the high-volume steps and stitch them into the process with humans kept on the decisions that matter. The label is less important than the question we actually answer in Discovery — which steps are worth automating, in what order, and where a human stays in the loop.

A production AI workflow you can put real traffic through, plus everything required to own it without us. Concretely that means: the deployed workflow itself (API/service/UI as scoped); the prompt set and prompt-versioning setup; an evaluation harness with a labelled test set of real cases so you can prove accuracy and catch regressions; the integration code into your systems; a KPI dashboard wired to the metrics we baselined in Discovery; reviewer-queue and escalation logic for the human-in-the-loop steps; and operational runbooks. All source, prompts, and evals are yours and handed over. The Discovery phase additionally delivers a strategy report, an architecture recommendation, a risk register, and a fixed-price Build statement of work. Nothing important leaves with the consultant.

6-10 weeks from the day Discovery starts. The sequence is 2-3 weeks of Discovery, then a 6-10 week Build with a deliberate milestone: a thin slice of the workflow is shipped to production on a slice of real traffic by roughly week 6, not at the very end. That milestone exists so you see real accuracy on your real data before the full build is finished — it de-risks the engagement and kills the classic consulting failure mode where everything looks great in the deck and falls apart on contact with production data. Compare that to a typical Big-3 strategy phase, which is 3-6 months before anyone writes a line of production code.

In-house. The same team that runs your Discovery — the people who scoped the workflow, picked the architecture, and baselined the KPIs — builds and ships it. We do not hand your strategy to an offshore body shop or a separate delivery partner, because that handoff is exactly the loss we exist to eliminate. This is the structural difference from the consult-then-go-find-a-vendor model: the people accountable for the recommendation are the people accountable for the result.

We find that out in Discovery and at the week-6 thin slice, not after a six-figure build. In Discovery we capture the baseline — current accuracy, throughput, cost, and turnaround of the existing human process — and we evaluate candidate approaches against a labelled test set of your real cases. If AI cannot credibly beat or complement that baseline at acceptable cost and risk, we tell you not to build, and Discovery is the only money you have spent. When we do proceed to Build, the week-6 production thin slice is the second checkpoint: if it does not perform on real traffic, we stop and fix the approach before scaling, rather than scaling a workflow that does not work. We instrument outcomes against the baseline rather than asking you to trust a slide.

You own all of it — the code, the prompts, the evals, the runbooks — and it is handed over at the end of Build with no license or lock-in. On data: we scope data handling, access, and retention in Discovery before any data moves, design around your existing security and governance posture, and map the workflow to the NIST AI RMF control stack (approved sources, prompt versioning, reviewer queues, audit logs). Model and deployment choices are made partly on governance grounds — for example self-hosted or your-cloud (Bedrock, Vertex) versus a third-party API — based on your IP-sensitivity and compliance constraints, not on what is easiest for us to ship.