AI workflow consulting for Midwestern organizations

Practical AI consulting for small and mid-sized businesses.

I work alongside software and operations teams to identify worthwhile AI opportunities, test them with focused prototypes, and build solutions their people can safely use and maintain.

Together, we'll define clear goals for one document-heavy, coordination-heavy, or knowledge-intensive workflow. Your team helps test the work, evaluate the output, and learn what it takes to use it well.

Discuss Your Workflow See Engagement Options

No sales presentation. We'll discuss one workflow, whether AI is a reasonable fit, and what a sensible first step would be.

Enterprise engineering experience applied to practical AI adoption

Staff Software Engineer
20+ years building enterprise software
AWS and cloud experience
Technical leadership and team mentoring

Good Places to Start

Look for work that consumes time and attention.

The best first opportunities are usually bounded, repeatable, and easy for the people doing the work to evaluate.

Extract and route information from incoming documents
Make internal policies and technical knowledge easier to find
Assist developers with repetitive implementation work
Coordinate multi-step work across existing systems
Review and summarize high-volume operational material
Set up human review and approval for consequential actions

Focused Engagements

A defined next step—not an open-ended AI project.

AI Opportunity Assessment

For: Business or technology leaders who know a process is inefficient but are unsure whether AI is the right solution.
Includes: Stakeholder conversation, workflow review, opportunity, risk, and cost analysis, baseline measures, prioritized recommendations, and a proposed pilot.
Outcome: A clear decision about where to invest, what to avoid, and what to test first.

Agent Prototype Sprint

For: Teams with a promising workflow that need evidence before committing to production development.
Includes: Defined success criteria and baseline measures, a working prototype, an architecture outline, a risk and operating-cost review, and a demonstration.
Outcome: Evidence of feasibility and expected value, with a practical path toward—or recommendation against—production implementation.

AI Architecture & Readiness Review

For: Software organizations preparing to integrate AI capabilities into existing systems.
Includes: A review of architecture, data, security, integration, evaluation, observability, and oversight points matched to workflow risk.
Outcome: A prioritized readiness plan that identifies production risks before they become expensive.

Cost and Value

Cost, value, and a sensible starting point.

Before a prototype or pilot, we can estimate the adoption and operating costs for a specific AI-enabled workflow, identify where uncertainty remains, and decide what value is worth measuring.

Start with a bounded workflow

Define baseline measures and success criteria tied to practical outcomes, such as time, quality, or throughput, before deciding whether to invest further.

Control costs through design

Model and tool choices, usage limits, human review, and monitoring can help keep operating costs visible and appropriate for the workflow.

Use evidence to choose the next step

After an assessment or prototype, we can stop or scale down if expected value does not justify further investment, or focus additional AI usage where it is likely to be useful.

Discuss Your Workflow

How We Work

Reduce uncertainty one decision at a time.

01
Discovery conversation
Discuss the workflow, business problem, stakeholders, and constraints.
02
Opportunity assessment
Determine whether AI is appropriate, estimate adoption and operating costs, and define baseline measures, success, risks, and likely value.
03
Prototype or pilot
Test the most important assumptions with a narrowly scoped implementation.
04
Production implementation
Integrate the solution with appropriate security, evaluation, and review, approval, or escalation where judgment or consequence calls for it.
05
Team enablement
Document the system, work with your team as they use it, and assess what they can operate or extend after the engagement.

Engagements can stop after any stage. If expected value does not justify further investment, or AI is not the right answer, I'll say so and recommend a simpler direction.

Learning Is Part of the Work

A useful AI workflow needs more than a new tool.

In the World Economic Forum's 2025 survey of more than 1,000 global employers representing more than 14 million workers, 77% of employers said they plan to upskill their workforce to better work alongside AI.

That is a plan, not a promise of results. It does reinforce a practical point: teams need time to learn, test a real workflow, evaluate the output, and decide where human judgment belongs. A focused engagement can make that learning concrete by starting with one bounded process.

Read the World Economic Forum's Future of Jobs Report 2025 (January 2025). The report summarizes its global employer survey and its findings on planned workforce upskilling in response to AI.

About Todd

Enterprise engineering judgment for a fast-moving technology.

I'm Todd Brunia, a Staff Software Engineer with more than 20 years of experience building enterprise applications, APIs, cloud services, and software delivery practices. I also hold an MBA from Iowa State University.

I approach AI as an engineer, not an evangelist. That means starting with the business process, testing assumptions early, accounting for security and maintainability, and keeping people involved wherever judgment matters.

The objective is a useful business result, not an AI project for its own sake. You can review more of my background on LinkedIn.

Have a Workflow Worth Exploring?

Bring one process that consumes too much time or attention.

In an initial conversation, we'll discuss how it works today, where the friction lies, and whether AI is worth exploring.