Spaid embeds with HVAC operators running ServiceTitan or Housecall Pro, uses AI to analyze 6–12 months of dispatch outcomes, job margins, callback patterns, and CSR booking data, and deploys HVAC-specific operational intelligence that your FSM reports have never shown you.
HVAC margin loss isn’t one problem. It’s three or four compounding failures across dispatch, execution, and seasonal timing. Most HVAC operators can name the symptoms. Almost none have seen the dollar amount tied to each one — because the data sitting in their FSM has never been read that way.
Your best HVAC tech closes diagnostic calls at 38% GM. Your average runs 29%. The 9-point spread is visible in ServiceTitan or HCP job data — sorted by tech, by job type, by season. But the standard reports don’t surface it that way. By the time the P&L shows the margin compression, the season is half over.
8–14 point GM spread between top and bottom HVAC techs — recoverable $8K–$16K per tech per yearWrong part on a condenser replacement. Incomplete diagnosis on a no-cooling call. Wrong tech assigned to a commercial refrigeration job. Each of these callback root causes exists in your FSM job data — cross-referenced with tech, job type, part combination, and season. A 50-tech HVAC shop burning $18K/month on callbacks has a solvable data problem.
40% of HVAC callbacks involve a missing part or incomplete diagnosis — preventableWhen call volume spikes in July and August, HVAC operators send whoever’s available — not whoever’s right for the job. Callback rates climb. Pricing consistency drops. Parts shortages hit. The data to prevent this builds up in the FSM all winter. Almost nobody analyzes it before peak season starts.
$200K–$400K/year in avoidable margin loss during peak season for a 50-tech HVAC shopGeneric field service software tracks your HVAC jobs. HVAC operations intelligence tells you why your margins are where they are — and what to do about it before next season.
Shows revenue totals and job counts. Doesn’t surface GM variance by tech, callback rate by job type, or dispatch outcome patterns by season.
Addresses general business principles. Not built from your specific data, your job types, your equipment mix, your market.
Adds features inside the existing system. Doesn’t cross-reference your FSM data with call recordings or analyze 12 months of dispatch outcomes.
Adds headcount. Still requires a system to tell them what to look for, how to look for it, and what to do when they find it.
Pulls every job record, technician performance metric, and part combination from your existing FSM. No new platform. No migration.
Built from your actual HVAC job history — cooling, heating, commercial, residential. Not generic field service benchmarks.
Recommended parts list for the job type, callback risk flag, prior job history at the address, customer LTV. Delivered in the existing FSM job card.
Monitors every tech’s performance against baseline daily. Flags the patterns that predict a bad season before the season starts.
Four tools. One engineer on-site. The first 30 days are diagnostic — no changes, just numbers.
AI connects to ServiceTitan, Housecall Pro, FieldEdge, or Jobber via API and reads 6–12 months of HVAC job records — cooling, heating, commercial, residential, maintenance. Callback patterns by tech and job type. GM by equipment category. Dispatch outcomes by season. The full HVAC operational picture from your existing FSM data.
During the first 30 days, the Spaid engineer rides with your highest-margin HVAC techs — documents how they diagnose a no-cooling call, what they look for on a commercial refrigeration job, what they bring on a condenser replacement. Converts that into a repeatable briefing for every tech on every high-callback job type. Specific to your equipment mix, your market, your service area.
AI surfaces recommended parts list for the specific job type and equipment combination, callback risk flag from historical data, and prior job history at the address — delivered in the existing FSM job card before the tech departs. Not generic checklists. Built from your HVAC callback and parts data. Reduces wrong-part returns by 40%.
AI monitors GM per job, callback rate, and quote variance for every HVAC tech daily — by job type, by equipment category, by season. Flags when a tech is drifting on commercial pricing, when a job type is trending high on callbacks, when dispatch patterns are creating avoidable seasonal margin compression. 3–4 week lead time before monthly P&L review.
Our Full-Operation Audit (Days 1–30) maps every HVAC margin leak — tech performance, callback root causes, and dispatch patterns. If we don’t identify at least $200,000 in recoverable annual revenue, we refund Phase 1 in full. You keep all audit deliverables.
After kickoff, we ask for about 30 minutes a week of your ops leader’s time.
We’ll start with a recent export or sample job data from your FSM, show you the biggest margin gaps and callback patterns, and scope the engagement. Full access happens only if you proceed to the audit.