A Spaid engineer connects to your existing ServiceTitan, Housecall Pro, or FieldEdge, deploys AI-powered automation for follow-ups, dispatch intelligence, and performance monitoring, and delivers measurable operational improvement in 30 days — without replacing your current software.
Most field service operators have tried automating parts of the operation — built-in FSM workflows, Zapier triggers, scheduled reports. The automations fire. The problems don’t get fixed. The gap isn’t the tooling. It’s that the automation has no context about your operation.
35–50% of unsold estimates never get a follow-up because the process depends on someone remembering to do it. FSM job status changes that should trigger automated sequences don’t because nobody configured them. $150K–$350K/year leaves the business through the follow-up gap alone.
35–50% of unsold estimates receive no follow-upEvery field service operation generates thousands of data points per week across FSM, calls, and invoices. Almost none of it is analyzed until the monthly P&L shows a problem. By then, the pattern has been running for 4–6 weeks. The data to catch it earlier already existed.
4–6 weeks between a drift pattern forming and a monthly P&L catching itGeneric workflow tools (Zapier, built-in FSM automations) trigger on surface events. They don’t know which job types have high callback risk, which CSRs have low booking rates, or which techs are drifting on margin. Automations without context produce noise, not insight.
Generic automation misses 90% of the patterns that matterAutomation tools trigger on events. AI-powered field service automation understands context — which tech, which job type, which pattern, which intervention.
Triggers generic events. Doesn’t know your job types, your callback patterns, or which CSR is losing bookings. Produces noise when it fires and silence when it should have fired.
Limited to what ServiceTitan or HCP exposes. Doesn’t cross-reference call data, invoice data, and job data simultaneously. No pattern detection.
Shows what happened. Doesn’t act on it. Requires someone to look, interpret, and respond. That’s the manual step that breaks.
Addresses human behavior, not system behavior. Works for 2 weeks, then back to baseline.
Knows which job types generate callbacks, which CSRs drop bookings, which follow-up sequences have the highest recovery rate. Context before automation.
Estimate sent, job complete, membership offered but not closed. Every status change that should trigger a follow-up does, without anyone remembering to do it.
Flags variance before it compounds. 3–4 week window to address root cause before a monthly P&L review shows the damage.
No adoption problem. No new platform. The automation layer runs on data your team already generates.
Four automation layers. Live within 72 hours of FSM connection. No migration. No new platform.
Automated follow-up sequences in ServiceTitan require two things: a trigger (the FSM job status change) and a sequence built from your highest-converting patterns. Most ServiceTitan setups miss the second part — they trigger generic messages that produce noise, not recovery. Spaid builds sequences from your actual top-performer close data before activating them.
Connects to ServiceTitan, Housecall Pro, FieldEdge, Jobber, or Service Fusion via API. AI reads 6–12 months of job records, invoices, pricebook, dispatch history, and membership data. No manual exports. No replacement required. The automation layer is live before Week 1 ends.
AI triggers follow-up sequences from FSM job status changes — estimate sent, job complete, membership offered but not closed. Sequences are built from your top-performer patterns: the follow-up timing and language that actually converts. No manual handoff from dispatch or CSR. 15–25% of previously dropped estimates recovered.
AI analyzes 6–12 months of dispatch outcomes — callback rate by tech and job type, GM per assignment, first-call resolution. Builds skill-match rules from historical outcomes and scores dispatch decisions in real time. Wrong-tech assignments flagged before the truck rolls. 25–35% callback reduction.
AI monitors GM per job, CSR booking rate, follow-up completion rate, and callback rate daily — across every tech, every CSR, and every job type. Flags variance 3–4 weeks before it shows in the monthly P&L. No dashboard to check. Automated alerts when patterns deviate from baseline.
The word "automation" gets applied to everything from a Zapier trigger to a full AI stack. Here’s the working definition that matters for a $20M–$100M field service operator.
Automation is not replacing your techs, your dispatchers, or your CSRs. It’s not a new platform your team has to learn. It’s not a dashboard someone has to remember to check every morning. And it’s not a generic workflow tool that fires the same message every time a job closes, regardless of job type, tech performance, or customer history.
Generic automation has no context. It fires on events but doesn’t know which events actually matter for your operation — or why.
Automation is connecting the data your FSM already captures — job cost, invoice, dispatch history, call recordings, pricebook, membership status — and surfacing the patterns that require action. Every week, your ServiceTitan or HCP instance generates thousands of data points. Almost none of it gets analyzed until the monthly P&L shows a problem. By then, the pattern has been running for four to six weeks.
Automation closes that gap. It reads the data continuously, identifies the patterns — which follow-up sequences aren’t firing, which dispatch decisions are producing callbacks, which CSRs are drifting on booking rate — and acts on them in real time, not at month-end.
The data is already there. ServiceTitan, Housecall Pro, FieldEdge, and Jobber all capture it. The problem is that nobody is reading across all of it simultaneously — job records, call recordings, invoices, dispatch history — and correlating the patterns that indicate a revenue leak.
A forward-deployed engineer who connects your FSM and call data via API, runs pattern analysis across 6–12 months of history, and deploys automation on top of your existing workflow closes that gap without replacing the systems your team already runs on.
Most operators have tried to fix operational problems with one of these approaches. Here’s why each one falls short — and what it actually takes.
| What operators try | What it actually requires |
|---|---|
| Buy more software | Connect the software you already have. Your FSM captures job cost, dispatch history, and invoice data. The gap isn’t more data — it’s a layer that reads across all of it simultaneously. |
| Train techs on new tools | Surface insights inside the tools techs already use. New logins create adoption problems. Automation that runs on top of your existing FSM requires zero change from field staff. |
| Monthly P&L reviews | Daily drift detection that flags variance before month-end. A margin problem that’s visible in the P&L on the 30th has been running since the 1st. The data to catch it on day 5 already existed in your FSM. |
| Hire a dispatcher with “better instincts” | Surface the dispatch patterns already in your job history. Six months of dispatch outcomes — callback rate by tech and job type, GM per assignment, first-call resolution — contains more signal than any individual’s instinct. AI that reads your actual history scores dispatch decisions against what your data already shows works. |
| Send techs to training | Deliver job-specific context before they arrive. A tech who knows the customer’s service history, the equipment age, and the previous call notes before walking in the door converts and resolves at a higher rate than a tech with the same skill set and no context. |
A Spaid engineer arrives with a defined process, not a discovery phase. Here’s what actually happens in the first month.
The engineer connects to your FSM (ServiceTitan, HCP, FieldEdge, or Jobber) and telephony system via API. No manual exports. No spreadsheet dumps. The connection reads job records, invoices, pricebook, dispatch history, membership data, and call recordings. Within the first week, the system has a complete picture of your operation — what your techs close, where dispatch sends them, which CSRs book at what rate, and where the follow-up sequences are breaking.
Pattern analysis across your FSM and call data surfaces the specific leaks in your operation: which job types have the highest unsold estimate rate, which dispatch assignments are generating callbacks, which CSRs are dropping booking rate, which follow-up sequences are firing too late or not at all. Each finding comes with a dollar estimate — not a generic benchmark, but a number calculated from your own job volume and margin data. If the audit doesn’t identify at least $200,000 in recoverable annual revenue, Phase 1 is refunded in full.
Automated follow-up sequences go live first — triggered from FSM job status changes, built from your highest-converting follow-up patterns, not generic templates. Dispatch intelligence is calibrated from your historical callback and margin data and begins scoring dispatch decisions in real time. Daily drift detection is set to your operational baseline and begins monitoring GM per job, CSR booking rate, and callback rate across every tech and every job type. Everything runs on top of your existing FSM. Techs and CSRs log into nothing new.
After the automation layer is live, the forward-deployed engineer monitors drift weekly and adjusts sequences and scoring rules as your operational patterns shift. Seasonal job mix changes, tech turnover, and new service lines all change what “normal” looks like. The system recalibrates continuously. Your ops leader gets a weekly summary of what moved, what flagged, and what was acted on — about 30 minutes of review time per week, no more.
Field service automation software connects the data your FSM already captures — job cost, invoice, dispatch history, call recordings — and acts on it automatically. In practice, that means follow-up sequences that fire when a job status changes (not when someone remembers), dispatch scoring that surfaces the right tech for the job type based on your actual callback history, and daily monitoring that flags margin or booking rate variance before it compounds over a full month. Spaid deploys this on top of your existing ServiceTitan, HCP, FieldEdge, or Jobber. The automation reads your data and acts on it. No new platform for your team to log into.
No. Automation removes the manual steps that break, not the people who make judgment calls. A dispatcher using AI-powered dispatch scoring still decides who gets the job — the system surfaces which tech has the lowest callback rate on that job type based on 6–12 months of your own dispatch history, so the decision is informed rather than instinct-dependent. A CSR supported by automated follow-up sequences still handles escalations and inbound calls — the automation covers the 35–50% of estimates that never get a human follow-up at all. The goal is to stop the operational leak, not to reduce headcount.
Spaid connects to ServiceTitan via API and reads your existing job records, invoices, dispatch history, pricebook, and membership data — no manual exports, no CSV uploads, no new fields to maintain. Follow-up sequences are triggered directly from ServiceTitan job status changes: estimate sent, job complete, membership offered but not closed. Dispatch intelligence is built from your ServiceTitan dispatch history and scores decisions in real time. Daily drift detection monitors GM per job, CSR booking rate, and callback rate across every record in your ServiceTitan instance. Techs and CSRs see none of it — it runs in the background and surfaces only what needs action.
Spaid’s engagement starts with a 30-day Full-Operation Audit, priced at founding-customer rates — 40% below standard. If the audit doesn’t identify at least $200,000 in recoverable annual revenue, Phase 1 is refunded in full and you keep all deliverables. Ongoing automation is scoped after the audit, based on the specific leaks identified in your operation, your FSM, and your revenue. Operators in the $20M–$100M range with 40–120 techs typically see the automation layer pay for itself within the first 90 days through follow-up recovery alone. The 45-minute diagnostic is the right starting point — it costs nothing and gives you a preliminary read on where your biggest leaks are.
FSM and telephony connections go live via API within the first week. Automated follow-up sequences are running before Week 1 ends. Dispatch intelligence and drift detection are calibrated from your historical data across Days 1–30. The full automation layer — follow-up sequences, dispatch scoring, daily drift monitoring — is live within 30 days of kickoff. This is not a software implementation project with a 90-day rollout. There is no new platform for your team to adopt, no training required, and no migration. The automation runs on top of the FSM your team already uses.
Operators on Spaid’s automation layer typically see three categories of measurable improvement: follow-up recovery (15–25% of previously dropped estimates recovered within 60 days), callback reduction (25–35% fewer callbacks as dispatch decisions improve against historical data), and earlier drift detection (variance caught 3–4 weeks before it shows in a monthly P&L). These are outcomes from your data, not benchmarks — the 30-day audit measures your starting baseline before any automation goes live, so you know exactly what moved and by how much. If the $200K threshold isn’t identified during the audit, you pay nothing.
Founding operators only · $20M–$100M revenue · ServiceTitan, HCP, FieldEdge, or Jobber
Our Full-Operation Audit (Days 1–30) maps every revenue leak — field and back of house. 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 call data from your FSM and call system, show you the biggest leaks, and scope the engagement. Full access happens only if you proceed to the audit.