AI consulting services that ship production systems.

AI workflow automation connects AI models to the systems, data, and approval steps your team already uses. It is designed to reduce repetitive work while keeping the right human review and operational guardrails in place.

The free diagnostic takes about two minutes and helps surface where time may be leaking. The paid AI Workflow Audit is a two-week engagement that prioritizes 5–10 automation candidates by ROI and estimates implementation effort.

A scoped AI Automation Sprint takes 4–6 weeks. AI knowledge base engagements go live in 3–5 weeks, depending on content, integrations, and review requirements.

Your team owns everything Unlocked Consulting builds. Systems are delivered on your stack with handoff documentation, without proprietary platform lock-in or a required ongoing retainer.

Unlocked Consulting is based in Munich and works with clients across Europe and the United States.

Five engagements, each with a defined output: a two-week AI readiness assessment that ranks the automation candidates by return; a 4–6 week automation sprint that ships one production system; an AI knowledge base over your own documents, live in 3–5 weeks; AI enablement, so people outside engineering ship changes inside guardrails; and a fractional Chief AI Officer for direction and governance. Generative AI is used where it earns its place in a workflow, and every engagement ends with implementation rather than slides.

A fixed fee per engagement, quoted after a thirty-minute call once the scope is known — the readiness assessment is the cheapest way to find out which engagement is the right one. No retainer is required and there are no platform licence fees: what gets built runs on your stack. Prices are not published because a two-week assessment and a 4–6 week sprint are different amounts of work, and a number without the scope behind it would mislead.

Three questions. Will the work end in a running system, or in a recommendation you still have to build? Will your team own it afterwards — code, documentation and the ability to change it — or will you be paying for the platform it lives on? And can they show a measured result from a real deployment, with the sample size beside it, rather than a cohort claim? A company that cannot answer the second question is selling a dependency.

Hire in-house when AI work is continuous and you have the engineering leadership to direct it; bring a consultant in when you need one system shipped — or the first one, before you know whether a full-time role is justified. Many companies do both, in that order. The guide at /ai-automation-agency-vs-in-house sets the two options side by side.

Rarely because of the model. They fail on the plumbing around it: data the model cannot reach, a workflow nobody mapped before automating it, no owner once the pilot ends, and a demo that ran on a clean export instead of production data. Every engagement here starts by mapping the real process and ends with a named owner, because those are the two failures that cost the most and show up last.