Case Study — Mid-Atlantic Climate Systems

HVAC Fleet Route Optimization with Private AI — 38% Fuel Reduction

A regional HVAC fleet deployed on-premise AI for route optimization and dispatch scheduling, cutting fuel costs by 38% and idle time by 2.1 hours daily per technician — with zero cloud API calls.

route optimizationfleet managementon-premise AIcost reduction
38% reduction in fuel costs across 22-vehicle fleet
2.1 fewer idle hours per technician per day
Zero cloud dependency — all inference runs on a single on-premise workstation
Dispatch re-optimization latency: 90 seconds (down from 4 hours manual)

The Problem

Mid-Atlantic Climate Systems runs a 22-vehicle HVAC fleet across three counties. Every morning, a dispatcher spent four hours manually assigning service calls to technicians based on geography, urgency, and parts inventory — a brittle process that broke down the moment a single truck got delayed or a job ran long. Fuel costs were $14,200/month and climbing. Idle time averaged 3.4 hours per technician per day.

The company evaluated cloud-based route optimization SaaS products. Three problems surfaced: (1) every provider required uploading customer addresses and service histories to their cloud, (2) per-vehicle pricing would cost $900/month at their fleet size, and (3) no provider could integrate with their legacy on-premise inventory system without a custom API build that nobody wanted to quote.

The Deployment

StarBloom Consulting deployed a private AI cluster on a single Dell Precision workstation the company already owned — a machine previously used for CAD that had been sitting idle. The stack:

  • Ollama running Qwen 2.5 Coder 7B (Q4_K_M quantized) for the optimization engine
  • n8n for workflow orchestration — ingesting the morning dispatch CSV, technician GPS pings, and inventory levels from the on-prem SQL Server
  • Open WebUI as the dispatcher’s interface — a chat-style prompt where they describe exceptions (“Truck 14 is delayed 90 minutes, redistribute his route”)
  • A custom Python agent that runs a constraint-satisfaction solver every 15 minutes, re-optimizing all active routes against real-time technician positions and parts availability

The entire stack runs air-gapped. No API keys. No cloud billing. The workstation’s RTX A2000 GPU handles inference comfortably at 4K context — the optimization payloads are structured JSON, not long-form text.

Results

MetricBeforeAfterDelta
Morning dispatch time4 hours (manual)90 seconds (automated)−99.4%
Monthly fuel cost$14,200$8,804−38%
Idle time per technician3.4 hrs/day1.3 hrs/day−2.1 hrs
Missed appointment windows12–15/week2–3/week−80%
Dispatcher overtime18 hrs/month0 hrs/month−100%

The $950 deployment fee was recovered in the first two weeks of fuel savings alone. The company now runs the same stack for HVAC load calculations and parts inventory forecasting — both on the same hardware, at zero additional cost.

What Made This Work

  1. The hardware already existed. The CAD workstation was idle. No CapEx required.
  2. The data never left the building. Customer addresses, service histories, and inventory data stayed on the SQL Server they’d been running for years.
  3. The interface is a chat box. The dispatcher doesn’t know what a constraint solver is. She types “Truck 14 delayed, fix the afternoon” and the system redistributes routes in 90 seconds.
  4. No recurring costs. The models are open-source. The orchestration is open-source. The only cost was deployment.

This is the pattern we repeat across every engagement: find the idle hardware, deploy the open-source stack, build the bridge between the terminal and the shop floor.

All Case Studies