AI automation case studies for Metro Detroit SMBs

The Problem

Picture a 10-person accounting firm in Farmington Hills. Its senior CPAs spend close to 60% of the billable day reconciling mismatched ERP data between client systems and the firm's own ledger by hand. The work is repetitive, and the most expensive people in the building are doing it. Two senior staff flagged burnout in their last reviews, and advisory work has stalled because the people who should be doing it are stuck in reconciliation.

The Solution

The AI Readiness Audit comes first, to confirm that reconciliation is the right first target. The build is a single reconciliation agent. It reads the mismatched data from both systems, applies the firm's reconciliation logic, sends anything below a set confidence threshold to a person for review, and syncs the resolved entries automatically. It runs on the tools the firm already has, with no new software platform.

• 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

Picture a 22-person service business in Novi with a steady flow of inbound leads from its website and referrals. The leads are good, but too few become booked consultations, because a lead waits an average of 4.2 hours between submitting a form and hearing from a person. By then, some of them have called someone else.

The Solution

The build is a lead response agent that starts the moment a form is submitted. It sends a personalized SMS and email within 30 seconds and asks two qualifying questions. If the lead answers, it books the call on the owner's calendar with a short brief attached. The agent uses the firm's own service language, handles common objections during qualification, and sends a separate alert for high-priority leads. The owner opens the calendar to a booked call with the context already written.

• 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

Picture a 38-person supplier in Auburn Hills making inventory and purchasing decisions on data that is three weeks old on average. Reporting takes one operations manager half a day every month. She pulls figures from four systems (the ERP, the supplier portal, the logistics platform, and a spreadsheet only she fully understands) and assembles them into a summary for leadership. Tuesday's decisions get made with last month's numbers, and over-ordered inventory keeps eating into margin.

The Solution

The audit maps the four data sources and finds the 12 metrics that drive purchasing decisions. The build is two connected automations. The first is a weekly KPI digest that pulls from all four systems at 6 a.m. every Monday and sends leadership a plain-English summary of inventory levels, open POs, supplier lead-time changes, and a three-week demand signal. The second is an alert that fires when a tracked SKU falls below its threshold and suggests a reorder, with quantities calculated from past usage.

• 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

The Problem

Picture a 12-person equipment maker in Metro Detroit selling direct from its own online store. The machines run a few thousand dollars each. The spare parts run well under a hundred. Google Ads has been running for two years, and the account reports conversions every month, so nobody has questioned it. Parts orders arrive steadily. Machine orders barely move. The store reports the same flat value to Google on every sale. The algorithm cannot tell a $40 part from a $3,000 machine, so it buys whichever is cheaper to win.

The Solution

The audit comes first and reads the ad account, the analytics property, and the store together. The fix runs in order. Real order values go to Google first, so bidding works toward revenue instead of order count. Then come the product pages the ad budget lands on, where the search listings are missing or cut off mid-sentence. Option menus get units written on them so a buyer knows what a quantity means. A quote request path is added for agencies and contractors who buy on a purchase order and cannot use a card. Until the values reaching Google are correct, we recommend spending less.

• Real order values reported from the store to Google Ads, replacing a flat per-order number
• Google Analytics 4 and Search Console connected, verified, and read monthly
• Search listings written for every product page the ad budget lands on
• Option and variant labels given units, and a quote path added for purchase-order buyers