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Anyone experimenting with AI or automation alongside FieldPulse?

Eli Torres (Staff)

Hey folks — Eli here from the dev side of the house. Been tinkering with some automation workflows around FieldPulse and got curious what the rest of you are up to.

I've got a little side experiment going where I'm using a webhook → work_order.status_changed → triggering a GPT-4 call to auto-draft customer follow-up emails based on the job notes. It's... surprisingly not terrible? The prompt engineering is the hard part, honestly. Getting it to sound like "us" and not a robot took some iteration.

Also played with:

  • Auto-categorizing photos from the mobile app (sending to a vision API to tag "panel photos" vs "wiring photos" etc)
  • Slack bot that summarizes the day's completed jobs and flags anything with suspiciously short duration
  • Trying to extract parts lists from free-form technician notes — this one is still pretty rough, edge cases everywhere

Yeah this one tripped me up too when I first looked at it — the webhook payload doesn't include the full job history, so if you need context from previous visits you're doing extra API calls. Worth keeping in mind if you're building something similar.

Anyway, curious if anyone's actually production-ized anything AI-adjacent on top of FieldPulse, or if it's all still weekend experiments like mine. What's working? What's a waste of time?

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  • Let me tell you how we do it — we're not using "AI" per se but we've built some solid automation around the same infrastructure. The key is treating FieldPulse as the system of record and layering intelligence on top, not inside.

    Our dispatcher gets a morning briefing auto-generated from overnight completions. It's just templated logic plus some conditional formatting (high-cost jobs, callbacks flagged, etc), but it's eliminated the 8am scramble to figure out what happened yesterday.

    Pro tip: Start with structured data extraction before you try anything fancy. If your technician notes are free-form chaos, no model will save you. We spent two months just standardizing Problem Description dropdowns before any automation touched it.

    Also worth mentioning — if you're doing anything customer-facing with AI-generated text, get your legal/compliance folks involved early. We learned that one the hard way.

  • in reply to Gwen Albright

    From a governance perspective, this is critical. It is worth noting that several jurisdictions now require disclosure when AI is used in customer communications, and your liability framework needs to account for hallucinated content.

    I would recommend:

    1. Establishing a formal AI use policy before any production deployment
    2. Implementing human-in-the-loop review for all customer-facing outputs
    3. Documenting model versioning and training data provenance for audit purposes

    The security implications of webhook exposure to third-party AI services also warrant review. Ensure your endpoint authentication and payload validation are robust.

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  • in reply to Gwen Albright

    From a governance perspective, this is critical. It is worth noting that several jurisdictions now require disclosure when AI is used in customer communications, and your liability framework needs to account for hallucinated content.

    I would recommend:

    1. Establishing a formal AI use policy before any production deployment
    2. Implementing human-in-the-loop review for all customer-facing outputs
    3. Documenting model versioning and training data provenance for audit purposes

    The security implications of webhook exposure to third-party AI services also warrant review. Ensure your endpoint authentication and payload validation are robust.

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