AI consulting and automation

Put AI to work on a real business bottleneck.

We identify repetitive work, pick a pilot worth testing, connect the right information, and teach your team how to use it responsibly.
The problem

A few people use AI tools. Nobody knows what’s working.

One salesperson drafts emails with a chatbot. Someone in operations pastes spreadsheets into another. Nothing is shared, nobody knows what data is safe to use, and the same manual tasks still eat hours every week.
The U.S. Chamber of Commerce reported that 58% of surveyed small businesses used generative AI in 2025, up from 40% in 2024. Adoption is common. A measurable result is less so.
The sequence

Five steps, one workflow at a time.

1

Identify

Map three to five high volume, repetitive workflows, the data each touches, and where a person must review.
2

Choose a pilot

Pick one with a clear baseline, low risk, and enough volume to matter. Record how long it takes today.
3

Implement

Connect the tools and information it needs, with a review step before anything consequential goes out.
4

Train the team

Hands-on sessions, written guardrails, and a named owner for the workflow.
5

Measure

Compare time, quality, and response speed against the baseline after 30 days.
Entry offer · AI Opportunity Audit
Covers steps one and two: the workflow map, a data and review assessment, baseline time estimates, one recommended pilot, and a 30 day roadmap. Useful on its own, even if you implement in-house.
Human review, built in

AI drafts. People decide.

For anything consequential or customer facing, a person reviews before it goes out. The pilot is designed around that step.
1 · Intake

Work arrives

A form, an email, an order, or a teammate’s request.
→
2 · AI first pass

Draft or summary

Using only the documents and data you’ve approved.
→
3 · Human review

Approve, edit, or reject

The workflow owner checks accuracy and tone.
→
4 · Action

Sent, filed, or routed

Logged so you can measure volume and time saved.
A team member reviewing an AI-drafted proposal on screen, pen in hand, the original client request printed beside the keyboard. A person checking a specific document, not an abstract “AI” scene.
Example workflows

Where pilots usually start.

Lead intake and routing

Summarize each inquiry, tag the service line, and suggest who should respond.
Review: sales confirms route

Proposal drafting

Assemble a first draft from past proposals, pricing rules, and discovery notes.
Review: owner edits and approves

Support knowledge retrieval

Answer staff questions from manuals, policies, and past tickets, linked to the source.
Review: staff verify before replying

Product content assistance

Turn spec sheets into consistent product descriptions and attributes.
Review: product lead checks specs

Reporting summaries

Pull weekly numbers from several tools into a short written summary.
Review: owner checks figures

Your workflow

Repetitive, high volume, and follows rules someone could write down? It’s a candidate.
What success means

Measured against the baseline you recorded first.

Time saved

Hours per week on the workflow, before and after.

Quality

How often drafts need heavy edits or get rejected.

Response time

How quickly a customer or teammate gets an answer.

Adoption

Whether the team still uses it after the first month.
Questions

Tools, data, and scope.

Whatever fits the workflow and your existing systems. Often that’s a business plan of a mainstream assistant, or the AI features already inside tools you pay for. The recommendation comes after the audit.
The audit notes what data each workflow touches and whether it should go to an AI tool at all. Pilots use business accounts with data controls, only documents you approve, and written rules about what never gets pasted in.
No. The work is choosing, configuring, and connecting existing tools to your processes, then training your team. If you need model development or formal enterprise compliance work, I’ll say so and point you elsewhere.
One workflow, one team, a defined scope, and a measurement point about 30 days after launch. Small enough to finish, large enough to show whether it’s worth expanding.
Then you’ve learned that cheaply, and the baseline tells you why. Sometimes the fix is a simpler automation or a process change with no AI involved.
AI discovery call · 30 minutes

Bring one task your team repeats every week.

We’ll talk through whether it’s a good pilot and what the audit would cover.