AI auditing and consulting
An engineering audit for AI opportunities: map workflows, data, risk, review gates, and the smallest useful prototype or build plan.
- Engagement
- Estimated after discovery
- Timeline
- 2-4 weeks
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.
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.
Build focus
- 01
Workflow selection and process mapping
- 02
Prototype scope for one high-friction task
- 03
Data, privacy, and human-review requirements
- 04
Prompt, retrieval, and tool-flow design
- 05
Working prototype with realistic sample inputs
- 06
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
Frequently Asked Questions
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.
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.
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.
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.
Start your project
Describe the workflow, users, tools, and constraints. webvise turns that into a clear build plan with timeline and budget before implementation starts.
Start a Project