Hey everyone, I've been lurking for a bit and learning so much from this community about building better pipelines. Seriously, thank you all 🙏.
I'm currently trying to automate some competitive research for a project, tracking features and pricing changes across a few SaaS platforms. Manually checking 10+ websites weekly is... not scalable. I saw that Poe added a 'browse' feature to some of their bots, and it got me thinking.
Has anyone here actually used Poe's browse feature for something like competitive analysis? I'm wondering if it could help with:
* Pulling the latest pricing pages
* Summarizing new feature announcements from blogs
* Checking for any UI changes on key product pages
My big worries are:
* Does it actually get the *current* live page data reliably, or is it using some cached/outdated version?
* Can it handle pages behind login walls (probably not, right)?
* How does it deal with heavily JavaScript-rendered content?
I tried a quick test on a public pricing page, and the bot gave me a summary, but I'm not 100% sure it captured *all* the plan tiers correctly. I'm nervous about building any sort of reporting on this if the data extraction is flaky.
If you've tried it, what was your experience like? Did you find it useful for gathering structured data, or is it better for just general webpage summaries? Any tips or pitfalls I should watch out for?
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Forget it. It's terrible for anything that needs reliable data. I tried it on three different CRM pricing pages last month and it got two of them completely wrong. Mixed up enterprise and starter features, hallucinated prices.
Your worry about cached data is spot on. Sometimes it feels like it's pulling from a three week old scrape. And JavaScript heavy pages? Might as well be looking at a blank sheet.
You're better off with a dedicated scraping service, even a simple one. At least then you know what you're getting. Building a report on Poe's output is asking for bad intel.
CRM is a necessary evil
Exactly. The data latency makes it unusable for tracking pricing changes. If you're analyzing SaaS cost trends and the numbers are old or wrong, your whole model is garbage.
It's the same as trying to do cloud cost analysis with last month's billing data. The numbers won't reflect current reserved instance utilization or spot market shifts. Stale data leads to bad decisions.
You need deterministic, timestamped results. A dedicated scraper you can schedule and audit gives you that control.
cost per transaction is the only metric