4-weekahead
Demand forecasts with calibrated confidence bands
Service Demand Prediction
ML models trained on weather, geography, and equipment age to forecast service calls 2-4 weeks ahead.
HVAC Analytics
Predict service calls before they come in
Demand forecasting and technician routing for HVAC companies — built on service history, weather, foot-traffic signals, and equipment-age context, not customer surveys.
Weather and seasonal demandService-history contextTechnician routing
The status quo
What HVAC companies deal with
- Technicians drive across town for one call when three others are nearby — wasted hours every day
- Seasonal demand swings catch you off guard — scrambling to hire in summer, idle trucks in spring
- You know which neighborhoods generate the most calls, but you can't act on that data systematically
What we deliver
How we help HVAC operations
02
Technician Routing
Optimized daily routes that minimize drive time and maximize billable hours per technician.
03
Service Area Heat Maps
Visualize call density, revenue per zone, and market penetration to focus your marketing spend.
04
Staffing Optimization
Predict seasonal demand peaks so you hire and train ahead of the rush, not during it.
05
Trade Area Analysis
Drive-time polygons and competitor proximity for territory planning and expansion.
Proof-led positioning
What this page needs to make obvious
HVAC demand forecasting, HVAC technician routing, and HVAC service area analytics.
Weather and seasonal demand
Heat waves, cold snaps, replacement cycles, and maintenance patterns mapped by territory.
Service-history context
Use past calls and customer density to identify coverage gaps and route pressure.
Technician routing
Turn demand signals into dispatch zones, route plans, and revenue-per-truck decisions.
Proof workflow
Input, review, evidence, output.
Modeled on the live Geospatial Solutions demos: the page should show what the buyer sends, what they review, what evidence stays visible, and what they receive.
Input
Service history, current service area, truck count, weather/seasonal concerns, and territory goals.
Review surface
We map demand signals, equipment-age proxies, customer density, weather pressure, and route constraints.
Evidence
Each recommendation shows source signals, assumptions, and dispatcher review points.
Output
Service-area map, demand heatmap, dispatch recommendation, CSV, or report.
Source and limits
Technical trust should stay visible.
Confidence
HVAC forecasts improve when service history is available.
Caveat
Demand models need local business rules and seasonal validation.
Source
Service history, weather, equipment age proxies, territories, foot traffic, and demographics.
QA boundary
Source notes, model assumptions, dispatcher review, and route feasibility.
Export path
Service-area map, demand heatmap, dispatch recommendation, CSV, or report.
Before the first call
What you send · What you get
No vague discovery phase. You bring four or five things, we return a specific plan you can evaluate.
→ What you send
- 112+ months of service history (CRM export or equivalent)
- 2Territory boundaries and any current scheduling logic
- 3CRM/dispatch platform name for integration scoping
- 4Seasonal or geographic patterns you have noticed
← What you get back
- 1Data quality assessment — what is usable and what needs cleanup
- 2Baseline forecast accuracy on your data (held-out backtest)
- 3Model architecture recommendation with reasoning
- 4Dashboard wireframe showing recommended actions
- 5Retraining schedule with automatic drift detection plan
Deliverables
What you walk away with
- Demand forecast dashboard with 4-week ahead service predictions
- Territory and site selection scoring tied to real foot-traffic signals
- Model card and backtest results so you can audit accuracy
- Monthly retraining pipeline with automatic drift detection
How we work
A scoped path from sample data to running system
No open-ended retainers. No "discovery phases" that bill for months without producing anything you can evaluate.
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01
Data intake
Your service history (12+ months ideal), territory boundaries, and any seasonal context. We assess data quality and recommend baselines.
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02
Model build
Foot-traffic signals from Foursquare + Overture, weather, demographic, and competitor data combined with your service history. Tuned to your geography.
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03
Dashboard
Action-oriented view: recommended staffing levels, route assignments, expansion candidates. Not a raw model output — a decision surface.
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04
Retrain
Monthly retraining on accumulated data. Model drift surfaced automatically. We can transfer the pipeline or keep operating it under SLA.
Live on geospatialsolutions.co
Click into the actual work
These open the real, interactive demos on our main site — not screenshots, not videos. Click around before you decide to talk to us.
Why teams trust us
- Real foot-traffic data from Foursquare + Overture Places — millions of US POIs, regularly refreshed
- PostGIS, Mapbox, Python, TensorFlow, and custom ML forecasting models
- Operated by Geospatial Solutions LLC — Washington, DC
Questions teams ask before they engage us
Common questions, answered honestly
How does demand prediction work in HVAC specifically?
We combine weather forecasts (lead indicator), equipment age and maintenance history (your CRM data), neighborhood demographics, and foot-traffic patterns. Output is 4-week ahead service call forecasts by zip code or service area.
Do we need to integrate with our CRM?
Yes for best accuracy. We integrate with ServiceTitan, Housecall Pro, FieldEdge, and most major HVAC platforms via API. Without CRM data, predictions are weaker but still useful for territory planning.
What about emergency calls — those aren't predictable, right?
Emergencies are partially predictable. Cold snaps cause heat-call spikes; heat waves cause AC failures. We forecast the volume — when extreme weather is coming, you staff up before the calls hit. Individual emergencies aren't predicted; the volume is.
How does technician routing tie into this?
Optimized routing uses the demand forecast to pre-position technicians in high-likelihood zones, then re-optimizes throughout the day as actual calls come in. Drive time drops 15-20% in typical deployments.
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Book a free HVAC analytics demo
Drop a pin. We will show you the foot traffic and demand forecast live.
Bring a territory or trade area. We will pull real foot-traffic data and a demand forecast on the call so you can evaluate the signal before any engagement.
Map my HVAC service area