u/azesen

Been poking around with some of the Gemini-based workflow stuff lately, specifically looking at whether it can, slot into our lead routing and enrichment pipelines without needing a dev involved every time something breaks. BuildShip and AppSheet both look promising on paper, and the Gmail trigger stuff in Workspace is genuinely handy for some of our ops use cases. But every time I get into the actual config, there's always some JSON handling or rate, limit quirk that makes me wonder if the 'no-code' label is doing a bit of heavy lifting. The community vibe I've seen elsewhere is that Gemini is decent for drafting and summarisation, but gets inconsistent when you need it to reliably handle logic across multiple steps or files. For anyone actually running Gemini integrations in a sales or revenue ops context, how's the reliability holding up in production? I keep seeing n8n pop up as the go-to for people who want more, control, but curious if anyone's found a setup that genuinely stays stable without constant babysitting.

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Been thinking about this a lot lately. We've had enrichment running in our CRM for a while now and the time savings are real, but I keep noticing gaps where the data just. isn't right. Wrong job titles, emails that bounce, company sizes that are way off. And I get it, some decay is inevitable, recent data suggests somewhere between 30-50%, of CRM data goes stale pretty quickly, so no tool is going to be perfect. But there's a difference between normal decay and systematically bad data that reps are actually acting on. The thing that bugs me is how much the claimed accuracy numbers diverge from what people actually report using these tools day to day. Some tools are advertising 90%+ match rates but users are seeing bounce rates well above the 3-5% threshold that starts seriously hurting deliverability in practice. So you end up with reps reaching out to the wrong person at a company, that restructured six months ago, which isn't just wasted effort, it's a bad first impression. And honestly the landscape has gotten more complicated, not less. Waterfall enrichment across multiple providers helps with match rates but it also means more sources to QA and more privacy compliance stuff to think about. Meanwhile agentic AI is getting layered into a lot of these tools now for real-time qualification and routing, which is cool, but garbage in garbage out still applies. Curious how others are handling this. Are you doing any kind of spot-checking or validation layer on top of your enrichment, or, just accepting that some percentage of the data will be wrong and building your sequences around that?