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AI auditing and consulting

An engineering audit for AI opportunities: map workflows, data, risk, review gates, and the smallest useful prototype or build plan.

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Engagement
Estimated after discovery
Timeline
2-4 weeks
Recent project: Märkische Projekt BauRead the case study

When this is the right step

You need proof before a full build

A strategy deck is too abstract, but a production system is too much to fund blindly. A prototype makes one workflow tangible enough for your team to judge.

Your workflow has messy exceptions

The useful work is not the happy path. Documents vary, approvals branch, and data arrives half-structured. The prototype tests those edge cases before architecture hardens.

AI advice has not turned into operations

Generic consulting leaves you with tool names and uncertainty. This sprint ties the recommendation to a working flow, review gates, and the path to production.

The Approach

We choose one repeatable workflow, map inputs, decisions, exceptions, and review points, then build a small prototype against realistic examples. The goal is not a demo with perfect sample data. It is a practical test of whether AI can remove work safely and what the production system would require.

The Outcome

You leave with a prototype your team can inspect, a clear recommendation on whether to build, and a production plan for the smallest useful automation.

Märkische Projekt Bau: Märkische Projekt Bau: a relaunched website and in-house AI portal for a Brandenburg construction firmMärkische Projekt Bau: Märkische Projekt Bau: a relaunched website and in-house AI portal for a Brandenburg construction firmMärkische Projekt BauRead the case study

What you get

Build focus

  • Workflow selection and process mapping
  • Prototype scope for one high-friction task
  • Data, privacy, and human-review requirements
  • Prompt, retrieval, and tool-flow design
  • Working prototype with realistic sample inputs
  • Production build plan with risks and next steps

Included

  • A working AI prototype for one clearly bounded workflow
  • A process map showing inputs, decisions, exceptions, and review points
  • Test cases using realistic examples from your team
  • Risk notes for data handling, model behavior, handoffs, and operations
  • A production build plan with architecture, cost drivers, and rollout steps

The service deck as a PDF

The compact PDF for this service: starting point, approach, scope, and next step on three pages.

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Frequently Asked Questions

What does the AI automation prototype include?
It includes process mapping, data and review requirements, a scoped working prototype, test cases with realistic examples, and a production build plan for the smallest useful automation.
Is this consulting or implementation?
Both, in a deliberately small scope. The consulting is tied to a working prototype, so the recommendation is based on the actual workflow rather than generic AI advice.
Do you also build the production version?
Yes, when the prototype proves the workflow is worth automating. The next step is usually a production build with monitoring, fallbacks, permissions, and clear ownership.
What makes a workflow a good candidate?
The best candidates are frequent, costly, and reviewable: document intake, routing, enrichment, reporting, research, content operations, or handoffs where a human still approves the result.