AI automation case studies — Metro Detroit SMBs

The Problem

A 10-person accounting firm in Farmington Hills was at a breaking point. Their senior CPAs were spending close to 60% of their billable day manually reconciling mismatched ERP data between client systems and the firm's own ledger. The work was unglamorous, repetitive, and entirely unsuited for the people doing it. Two senior staff had flagged burnout in their last reviews. Client advisory capacity had stalled because the people who should be doing advisory work were buried in reconciliation.

The Solution

We started with the AI Readiness Audit to confirm the reconciliation bottleneck was the right first target — it was. We then deployed a single reconciliation agent: a workflow that reads the mismatched data from both systems, applies the firm's reconciliation logic, flags exceptions above a defined confidence threshold for human review, and syncs the resolved entries automatically. No new software platform. Built on top of the tools they already had.

• Automated ERP-to-ledger reconciliation agent
• Confidence-scored exception routing for human review
• Direct sync to existing accounting platform
• 4-week build sprint including team training and documentation

The Problem

A 22-person service business in Novi was generating a solid volume of inbound leads through their website and referral network — but converting less than they should. The issue was not the quality of the leads. It was the gap between when a lead submitted a form and when a human followed up. The average response time was 4.2 hours. The industry data is consistent: 78% of buyers go with the first vendor that responds. They were losing deals they had already won on paper.

The Solution

The fix was a lead response agent that activates the moment a form is submitted. It sends a personalized SMS and email acknowledgment within 30 seconds, asks two qualifying questions, and — if the lead responds — books directly to the owner's calendar with a pre-populated meeting brief. The agent uses the firm's specific service language, handles common objections in the qualification step, and routes high-priority leads with a separate notification. The owner sees a booked call, not a cold name in a spreadsheet.

• Instant SMS + email acknowledgment on form submission (< 30 seconds)
• Two-step AI qualification flow with objection handling
• Direct calendar booking with pre-populated lead brief
• Priority routing and owner notification for high-value leads

The Problem

A 38-person manufacturing-adjacent supplier in Auburn Hills was making inventory and purchasing decisions based on data that was, on average, three weeks old. Their reporting process required a dedicated half-day every month: one operations manager manually pulling figures from four different systems — their ERP, their supplier portal, their logistics platform, and a spreadsheet that only she fully understood — and assembling them into a summary for leadership. Decisions that needed to be made on Tuesday were being made with last month's numbers. The carrying cost of over-ordered inventory had become a consistent drag on margin.

The Solution

We mapped the four data sources, identified the 12 metrics that actually drove purchasing decisions, and built two connected automations. The first is a weekly KPI digest that pulls from all four systems every Monday at 6 a.m. and delivers a plain-English summary — current inventory levels, open POs, supplier lead time changes, and a three-week demand signal — to the leadership team's inbox before the week starts. The second is an exception alert that fires whenever inventory for a tracked SKU falls below a defined threshold, triggering a suggested reorder with quantities pre-calculated from their historical usage patterns.

• Automated weekly KPI digest from 4 connected data sources
• Plain-English narrative summary with leadership-ready format
• Exception alerts with pre-calculated reorder suggestions
• Real-time inventory threshold monitoring for critical SKUs