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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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  • Another way to think about this — the automation layer is becoming as important as the core platform. We're evaluating FieldPulse partially on how well it supports these experimental workflows.

    From a strategic standpoint, the teams that figure out how to augment field operations with intelligence (whether statistical or generative) will outpace those treating this as a novelty. The operational impact is real, but so is the risk of building on shifting sand if API stability or webhook reliability changes.

    I would be interested to hear from FieldPulse on their roadmap for first-party AI features, as this would substantially change our build-vs-buy calculus.

  • in reply to Nadia Petrov

    Nadia — appreciate you surfacing this. We are actively exploring several AI-powered capabilities that would sit natively within FieldPulse rather than requiring external orchestration.

    Our current focus areas include:

    • Intelligent job summarization — auto-generating completion narratives from technician inputs and photo context
    • Predictive scheduling — surfacing likely duration overruns based on historical patterns
    • Smart dispatch suggestions — recommending technician assignments based on skill matching, proximity, and workload

    We do not have a public timeline for these capabilities, but I can confirm they are prioritized for our H1 roadmap. The webhook and API infrastructure you are using today will remain supported and will likely gain additional event types to better enable these workflows.

    If you are interested in early access or research participation, please reach out directly. We want to ensure native features address the real patterns emerging in customer implementations.

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  • in reply to Nadia Petrov

    Nadia — appreciate you surfacing this. We are actively exploring several AI-powered capabilities that would sit natively within FieldPulse rather than requiring external orchestration.

    Our current focus areas include:

    • Intelligent job summarization — auto-generating completion narratives from technician inputs and photo context
    • Predictive scheduling — surfacing likely duration overruns based on historical patterns
    • Smart dispatch suggestions — recommending technician assignments based on skill matching, proximity, and workload

    We do not have a public timeline for these capabilities, but I can confirm they are prioritized for our H1 roadmap. The webhook and API infrastructure you are using today will remain supported and will likely gain additional event types to better enable these workflows.

    If you are interested in early access or research participation, please reach out directly. We want to ensure native features address the real patterns emerging in customer implementations.

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