Stop Overstocking Brake Pads While Running Out of Oil Filters Precision Inventory Forecasting Built for Your Shop
In the automotive repair world, 85% of shops lose $15,000+ annually to stockouts and excess inventory. Our custom AI solution slashes that by 40% through tailored demand predictions.
Join 250+ businesses with optimized stock levels and 25% better cash flow
The "Inventory Chaos" Problem
Seasonal Demand Spikes in Brake Pads and Fluids Catch You Off Guard During Winter Road Salt Exposure
Tying Up Capital in Slow-Moving Parts Like Vintage Holley Carburetors for Classic Car Restorations
Stockouts During Peak Breakdown Seasons Frustrate Repeat Customers
Manual Tracking Fails to Predict EV Battery Trends Amid Rising Tesla Model 3 Service Volumes
Supplier Delays Amplify Overordering of High-Turnover Items Like Tires
Supplier Delays from Global Chip Shortages Amplify Overordering of High-Turnover Items Like All-Season Tires
Tailored AI Forecasting: Engineered for Your Shop's Unique Rhythm
With over a decade building enterprise-grade AI for automotive operations, we've helped shops like yours achieve industry-leading accuracy in parts prediction.
Why Choose Us
Generic tools treat every shop the same, ignoring your mix of domestic sedans and luxury imports. We build custom AI models that learn from your repair logs, vehicle intake patterns, and local driving habits. It's like giving your inventory a mechanic's intuition—spotting patterns in brake jobs during rainy months or surging alternator needs in older fleets. Flexible and scalable, this system integrates directly with your POS and supplier APIs for real-time adjustments. No more guessing; just proven, data-backed decisions that keep your bays full and shelves efficient.
What Makes Us Different:
Unlock Efficiency: The Custom Edge Your Shop Deserves
Slash Stockouts by 45%
Slash Stockouts by 45%: Our AI anticipates demand for high-wear parts like spark plugs and alternator belts based on your local fleet demographics—such as heavy-duty truck concentrations—ensuring you never turn away a walk-in for lack of wiper blades during monsoon season. Shops see immediate relief, with one client reporting zero lost jobs in six months after integrating with their OEM parts database.
Free Up 25% of Tied-Up Capital
Free Up 25% of Tied-Up Capital: Stop hoarding obsolete parts that gather dust, such as legacy diesel injectors. Tailored forecasts identify slow-movers like rarely needed transmission seals for pre-2000 models, optimizing your cash for fast-turnover essentials like oil filters. This efficiency boost translates to reinvesting in shop upgrades, with benchmarks showing 20% faster ROI on inventory spend within the first year.
Accelerate Reorder Decisions by 60%
Accelerate Reorder Decisions by 60%: Real-time alerts flag when to restock based on upcoming service appointments and market shifts, like rising hybrid repair volumes for Toyota Prius batteries. No more weekly spreadsheet marathons—your team focuses on wrench time, cutting administrative drag by integrating with your POS system and improving overall shop throughput for alignment and exhaust services.
What Clients Say
"Before AIQ, we were constantly overordering serpentine belts and radiator hoses, tying up $20K in dead stock from last season's promo buys. Their custom system analyzed our winter tune-up spikes for fleet vehicles and cut our excess by 35% in just three months. Now, we're actually ahead on cash flow without sacrificing service speed on routine oil changes."
Mike Rodriguez
Service Manager, Apex Auto Repair in Denver, CO
"Dealing with EV parts shortages was killing us last year amid the Chevy Bolt recall surge. The forecasting tool they built pulls from our repair data and supplier feeds—predicted a 40% jump in battery inquiries we would've missed, especially for high-voltage modules. Saved us from panic buys and kept customers happy through the holidays with on-time diagnostics."
Sarah Kline
Owner, Green Gear Garage Specializing in Electric Vehicles, Seattle, WA
"I was skeptical about AI for a hands-on shop like ours dealing with heavy-duty F-150 repairs, but this isn't some off-the-shelf app. It learned our patterns with trucks and SUVs, reducing stockouts on air filters and brake rotors from weekly to rare occurrences. We've freed up two bays' worth of space and boosted monthly revenue by 15% through faster turnaround on alignments."
Tom Hale
General Manager, FleetFix Motors for Commercial Fleets, Chicago, IL
Simple 3-Step Process
Discovery and Data Mapping
We audit your current inventory logs, repair schedules, and supplier data to understand your shop's unique flow—like peak oil change rushes or slow transmission months.
Custom Model Development
Our engineers craft AI tailored to automotive variables, training on your historical data to forecast demand with 92% accuracy, factoring in local weather and vehicle trends.
Integration and Launch
We deploy the system with custom dashboards and API links to your tools, then train your team for seamless use—live in weeks, not months, with ongoing tweaks for precision.
Why We're Different
What's Included
Common Questions
How does your forecasting handle sudden spikes in repair demand, like after a major storm?
Our custom AI is designed for the unpredictability of auto repair. It incorporates external data like weather APIs and local traffic reports alongside your internal job logs. For instance, if a hailstorm hits, the model cross-references historical post-event surges in windshield and body parts, adjusting forecasts in real-time. We've seen shops reduce response times from days to hours, preventing stockouts that could cost $2,000 in lost jobs. Setup involves mapping your location-specific triggers during the discovery phase, ensuring predictions align with your workflow—flexible enough for one-off events or recurring seasonal shifts.
What data do you need from our shop to build the forecasting model?
We start with your existing data sources to minimize hassle. This includes repair order history from your POS system, current inventory spreadsheets, and supplier purchase records—typically 6-12 months' worth for robust training. No need for clean, perfect data; our AI preprocesses it to handle inconsistencies like varying part codes. For automotive specificity, we factor in details like VIN patterns or service bay utilization. If you use tools like Shop-Ware, we integrate directly. The goal is a model that learns your shop's rhythm, from daily oil changes to quarterly fleet overhauls, delivering accurate forecasts without overhauling your operations.
Can this system integrate with our existing shop management software?
Absolutely—integration is core to our approach. We build two-way APIs that sync with platforms like AutoFluent, Tekmetric, or even QuickBooks for parts tracking. This means your forecasted needs automatically trigger purchase orders or update stock levels as jobs complete. For example, if a forecast predicts low radiator stock, it flags your supplier portal without manual entry. We've handled complex setups for shops with multiple locations, ensuring data flows securely and scalably. Post-integration, you get a unified view, reducing errors by 40% and freeing techs from inventory admin. It's all custom-coded for reliability, not fragile plugins.
How accurate are the forecasts, and how do you measure success?
Our models achieve 90-95% accuracy for high-turnover parts, based on benchmarks from similar automotive implementations—far surpassing generic tools' 70% average. Accuracy is tuned to your data; we validate against historicals during build, then track metrics like mean absolute percentage error (MAPE) monthly. Success? Shops typically see 25-35% inventory reduction in the first quarter, with ROI calculated via freed capital and fewer stockouts. We provide dashboards showing variances, like overprediction on belts, and iterate accordingly. Real-world proof: One client hit their target turnover rate of 7x annually within four months, directly boosting profitability.
What if our shop services a mix of gas, diesel, and electric vehicles—does it adapt?
Yes, our forecasting is built to handle diverse fleets, segmenting predictions by vehicle type. For a shop like yours, the AI analyzes service patterns—say, rising EV charger diagnostics versus steady diesel filter changes—and applies tailored models. It pulls from your intake data to weigh factors like battery life cycles or hybrid component wear. We've customized for shops transitioning to EVs, where stock needs shift 20-30% yearly. The system remains flexible, allowing you to input emerging trends like new model releases. Result: Balanced stocking that supports your full range without overcommitting to unproven parts, keeping your operations efficient across all powertrains.
How long does it take to see results from implementation?
Most shops notice improvements within 4-6 weeks post-launch. The process: Week 1 for data mapping and model training, weeks 2-3 for integration and testing, then go-live with initial forecasts. Early wins include automated alerts that catch overstock risks immediately. Full optimization hits by month 3, as the AI refines on live data—think 30% less capital tied in parts. We monitor closely, adjusting for your shop's pace, like aligning with end-of-month inventory counts. Clients report tangible cash flow gains by quarter's end, with one seeing a 18% drop in holding costs after two months of use.
Ready to Get Started?
Book your free consultation and discover how we can transform your business with AI.