Implement it, run it, prove it.
Salesforce Field Service that fills the schedule, not just the calendar
Most Salesforce Field Service projects ship a configured org and call it done. But a field operation lives or dies on what happens after go-live, when reality stops matching the plan and a tech calls in sick at 7am. SkySync implements Field Service against the metric that actually moves money — first-time fix, jobs per tech per day, truck rolls avoided — then runs the schedule and the agents on top of it, with our fee tied to the result.
The part the SOW skips
You can stand up work orders, service territories, a dispatcher console, and a mobile app in a few weeks. None of that is where Field Service pays off, and none of it is where it fails. It pays off in the optimization engine that decides who goes where, and it fails the first time a job runs long, a part is missing, or a tech is out and the schedule unravels with no one tuning it. Standing up the config is the part the proposal is honest about. The part that determines ROI — the data the optimizer reasons over, and the operating discipline after launch — is usually a line item nobody owns once the consultants leave.
Pick the number before you pick the config
Field Service has a hundred settings and exactly one job: get the right tech to the right site with the right parts on the first trip. We start by naming the metric that converts to cash for your operation, then implement backward from it — because the optimizer's objective function, your territory design, and your data model all change depending on which number you're actually protecting.
- First-time fix rate — every avoided second trip is a truck roll, a part, and a day of capacity recovered.
- Jobs per technician per day — the difference between hiring and scheduling better with the crew you already have.
- Schedule adherence and overtime — what optimization and live re-scheduling actually protect when the day goes sideways.
- Mean time to repair and SLA attainment — the numbers your customers and contracts grade you on.
The optimizer is only as good as the data under it
Scheduling and Optimization decides dispatch from skills, territory rules, travel time, parts availability, working hours, and asset history. The engine itself isn't the risk — its inputs are. If a tech's skill matrix is stale, the candidate list it filters from is wrong before optimization even runs. If parts data lags reality, it confidently books a job that can't be completed on arrival. This is the same failure mode that sinks agents: garbage in, expensive dispatch out. We unify asset, entitlement, inventory, and workforce data — on Data Cloud where those sources genuinely live in silos — so the engine schedules against the truth instead of a guess, and the score it optimizes reflects what's actually true in the field.
Where Agentforce earns its place in the field
Field service is full of high-volume, repetitive coordination that drains dispatchers and delays techs. That is exactly the work an agent should own — but only once the data and process underneath are solid, or the agent just automates the mistakes faster.
- Inbound triage and self-scheduling so simple, well-defined jobs never touch a dispatcher.
- Pre-visit parts and skills checks that catch a doomed first trip before the truck leaves the yard.
- Field-tech copilots that surface asset history, manuals, and the next-best step on the mobile app.
- Automated follow-up, debrief capture, and rescheduling when a job slips so the open slot gets refilled, not lost.
We run the schedule after go-live
An optimization policy that was right on launch day is wrong by next quarter as territories shift, demand seasons change, new SKUs land, and crews turn over. The decay is quiet — no error message, just a slowly rising overtime line and a first-time-fix rate that drifts a point at a time until the original savings are gone. Most partners hand you the keys before that starts. We treat Field Service like a running system: reviewing optimization outcomes against the metric, tuning rules and agents as the operation moves, and reporting every period — so the value compounds instead of eroding.
Why our incentives fit field work
Time-and-materials consulting rewards a bigger build. Field operations reward the opposite — fewer trips, less overtime, more throughput from the crew you already have. The further you go with us, the more of our fee is tied to that result, so we optimize for the same number your COO does instead of for scope. It's built on platform depth, not theory: our founder built Agentforce as a Senior PM at Salesforce. If you want to pressure-test the model against your own field metric, start at /start.
Frequently asked
What's the difference between implementing Field Service and actually getting value from it?
Implementation gets work orders, territories, the dispatcher console, and the mobile app live — usually in weeks. Value comes from the optimization engine and the data behind it making good dispatch decisions every day, and from someone tuning that engine as your operation changes. We implement against a field metric and run the optimization after launch, which is exactly where most projects quietly lose the ROI.
Do we need Salesforce Data Cloud for Field Service?
Not for basic scheduling. But the optimizer and any field agents are only as good as the asset, inventory, skills, and entitlement data they reason over — and that data usually lives in disconnected systems. Where it does, Data Cloud unifies it so dispatch and agents act on the truth rather than a stale fragment. Where your data is already clean and in one place, you may not need it, and we'll tell you so.
Can Agentforce agents help in field service, or is it just chatbots?
There's real, repetitive coordination work agents can own: triaging inbound requests, letting customers self-schedule simple jobs, checking parts and skills before a tech is dispatched, and giving field techs a copilot for asset history and next steps. We add agents only once the data and process underneath are solid — an agent on a broken process just misfires faster.
Will optimization keep working after the consultants leave?
Only if someone maintains it. Optimization policies that were right at launch drift as territories, demand, parts, and crews change — usually without any visible error, just slowly worse outcomes. We run Field Service as an ongoing system — reviewing outcomes, tuning rules and agents, and reporting on the metric each period — rather than handing over a config that decays.
How do you tie your fee to field service outcomes?
We agree on the field number that converts to cash for you — first-time fix, jobs per tech per day, overtime, truck rolls avoided — and the further you go with us, the more of our fee is tied to moving it. We win when your crew does more with the same headcount, not when the build gets bigger.
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Tell us one number you'd like AI to move. We'll show you how we'd do it, what it's worth, and how we'd tie our fee to getting you there.