Anyone experimenting with AI or automation alongside FieldPulse?

YMMV on this one, but I've got a Make scenario running that pulls completed jobs every hour and feeds them into a small classification model we trained. Basically trying to predict which jobs are likely to generate a callback based on tech notes, time of day, and parts used.

Heads up: the webhook reliability for high-frequency jobs is not amazing, so we batch it instead. Worth noting that FieldPulse's rate limiting on the read API (100 req/min last I checked) becomes your bottleneck real fast if you're trying to hydrate a lot of historical data.

Here's what worked for me:

// webhook receiver → queue in Redis → worker processes batches
every 15 min: fetch /work_orders?status=completed&since=${last_run}

The model's only about 70% accurate so far, but it's enough to flag jobs for QA review. Small wins.

Oh interesting — you're doing the prediction layer. I got as far as "this seems possible" and then got distracted by the shinier generative stuff. How much training data did you need before it started being useful?

Also yeah, the webhook ordering thing is real. I had a job completion fire before the photo uploads finished and the vision API got... creatively confused by placeholder URLs.

We need something like this working. My team can't move forward on the volume we're handling without better triage. What's the actual lift to get a basic version running?

No small talk — just rough hours for someone with decent API experience.

About 8 months of historical before I trusted it enough to surface to dispatchers. The feature engineering was way more important than model choice — stuff like "ratio of estimate to actual time" and whether the customer had a prior complaint were stronger signals than I expected.

And lol yeah, webhook race conditions are a thing. I ended up adding a 5-min delay buffer before processing, which defeats some of the "real-time" promise but saves sanity.

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.

40-60 hours to get something that doesn't embarrass you in production. Depends heavily on whether you need the prediction in real-time or next-day is fine. Next-day is way easier.

Next-day works. Thanks.

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.

lol y'all are overthinking it

i got a zap that reads my completed jobs and dumps a summary into chatgpt api and posts to slack. took like 20 min to set up. is it perfect? nah. does it save me scrolling through 50 jobs? yep

tbh half the "AI" stuff is just better search and formatting. don't need a policy for that

Seconding this — we actually paused our most ambitious experiment after a compliance review. Not killed, just... properly governed now. Which is slower but safer.