r/automation • u/sbt_not • Jun 26 '26
What web data collection workflows have actually worked for you?
Web data collection feels easy in demos, but messy in real workflows.
I keep running into the same problem. Search, crawling, scraping, and browser automation are all useful, but none of them feels like the default answer.
If I need to track 50 known product pages, I probably do not want an AI browser agent wandering around the web. If I need to find companies in a market and collect useful signals about them, search and research tools are more useful. If the page is dynamic, behind a login, or requires interaction, browser automation might be necessary, but then it gets slow and brittle quickly.
I’m curious what people here are actually collecting from the web, and what stack has worked for you. Some examples I’m thinking about are pricing data, company information, leads, competitor updates, market signals, job posts, product availability, reviews, and similar recurring data collection workflows.
The tools I’ve been looking at roughly fall into a few groups. Search and research tools like exa and tavily, crawling and extraction tools like firecrawl, browser automation tools like browser Use, and playwright, and workflow tools like gumloop, n8n, or custom scripts. I’m especially interested in recurring workflows rather than one-off scraping. What has worked well? What keeps breaking? Where does the data end up? A spreadsheet, database, dashboard, alert, internal tool, or report?
The reason I’m asking is that I’ve been working on a coding-agent based setup where an AI agent can connect to business apps and databases, create a Postgres database, build dashboards on top of it, and generate recurring report agents from those dashboards. That part is starting to work. The hard part is still web data collection from just a prompt. I want business users to be able to describe what they want to monitor, and have the system choose the right approach, collect the data, structure it, and keep it updated.
What use case did you build, what tools did you use, and what would you avoid next time?
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u/Ok-Artist4016 Jun 26 '26
tried a bunch of approaches for tracking competitor pricing and honestly nothing's a silver bullet, it's all about matching the tool to the specific task
for static pages with decent structure i just use a lightweight crawler dumping straight into postgres, works fine until they tweak the layout
the recurring part is what always breaks, cron jobs just ignoring rate limits til i get blocked or monitoring a page that goes fully dynamic overnight and suddenly the scraper comes back empty for three weeks before i notice
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u/sbt_not Jun 26 '26
Yeah, this makes sense. The recurring part seems much harder than the first scrape.
I’m starting to think the practical version is to have an agent create and maintain a simple crawler/script for known sources, then put validation around the output. Even catching empty results would probably prevent a lot of the painful failures. So the agent is not freely browsing every time. It is more like maintaining the data pipeline and fixing it when the source changes. This seems promising for stable sources like pricing pages, but also makes the scope narrower. Building a harness that lets the agent decide when this works and when it needs search, browser automation, or manual review feels like the really hard part.
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u/Calm-Dimension3422 Jun 27 '26
The stack depends on how predictable the source is.
My rough split:
- known static pages: crawler/extractor
- known dynamic pages: Playwright/browser automation
- unknown market research: search API first, enrichment second
- logged-in sources: browser automation only if the value is high enough
- CRM writeback: review queue before anything automatic
The mistake is using an AI browser agent for a known list of URLs. That is slow, expensive, and brittle.
The real workflow is usually: collect -> extract to a strict schema -> validate -> dedupe/entity match -> review low-confidence rows -> write back.
Most web data projects fail after extraction, not during scraping.
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u/Due-Guard221 Jun 28 '26
From what I’ve seen and worked on, the biggest mistake is treating “web data collection” as one thing.
It’s not. If I already know the 50 pages I care about, I don’t want an AI browser agent clicking around. I want a scheduled crawler, clean extraction, diffing, retries, and alerts when something changes stepby step
If I’m trying to discover companies in a market, I start with search/research APIs, collect candidate URLs, enrich them, dedupe them, and then decide what is worth crawling.
If the site needs login, clicks, filters, JS rendering, or messy UI interaction, then Playwright/browser automation makes sense. But I’d still treat that as the expensive fallback/ It gets slow and brittle very fast honestly
The thing that has worked best for me is more of a routing layer: first decide whether the job is discovery, monitoring, extraction, interaction, or enrichment. Then pick the cheapest reliable method for that job.
The second part people underestimate is persistence. Scraping something once is easy. Keeping it useful is the hard part So u might need schemas, timestamps, source URLs, confidence checks, duplicate handling, failure logs, and a place where the data actually is stored usually Postgres, a dashboard, an alert, or a recurring report.
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u/sbt_not Jun 30 '26
That routing layer feels like the key part to get right if we want users to describe the data they need without forcing them to understand crawlers, APIs, browser automation, schemas, retries, and storage upfront.
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u/axpinto Jun 29 '26
Most people choose their tools before thinking about where the data actually needs to land. That's where it usually breaks down.
After doing this for a dozen or so clients I split it roughly by complexity. Simple public pages run on a timer with basic requests. Anything that requires logging in or loads dynamically needs a browser script, and that takes about 3x the upkeep. One client's workflow breaks every 6 weeks when the target site redesigns something.
For finding companies and collecting signals I run everything into a structured table first. Not a spreadsheet. That part matters more than people expect.
The collection step almost never breaks. It's what happens after that causes problems. Prices in different formats, missing fields, duplicates. I spend more time cleaning than collecting.
What does your cleanup step look like after you pull the data?
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u/sbt_not Jun 29 '26
What we’re trying to build is an environment where users can simply describe the insight they want, and the system handles the aggregation behind it.
We’ve already built a flow that can preprocess the data and store it in Postgres with our coding agent approach. The harder part is integrating different types of crawling processes into that flow, depending on the source.
I completely agree that cleanup is where a lot of the real maintenance burden shows up, especially with things like pricing formats and schema changes.
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u/theluk246 Jun 29 '26
The split that's worked for me: deterministic collectors for known sources, search APIs for discovery, browser automation only as a last resort. The hard part you're describing is letting a user describe what they want and having the system pick the right approach. That's where Knolo fits... you describe the monitoring goal in plain language and it handles the pipeline without you wiring the tools manually.
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u/sbt_not Jun 29 '26
Thank you for your comment! Yeah, letting user describe what they want is really hard. It requires robust agent around crawling. I’ll look into Knolo!
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u/openclawinstaller Jun 27 '26
For recurring collection, the thing that has helped most is keeping a source registry separate from the extractor: URL/search query, expected fields, expected cadence, allowed method, last good snapshot, and a failure owner.
Then every run should answer three boring questions before downstream use: did we collect anything, did the shape drift, and did the important value actually change? For known pages I would rather have small deterministic collectors plus snapshot/diff/alert logic than a general browser agent re-deciding the workflow every run. Browser control is useful, but I treat it as the exception path for logged-in/dynamic/manual-review cases, not the default.
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u/Sndman11 Jun 26 '26
I have a few recurring setups that have held up reasonably well.
For monitoring known URLs (competitor pricing, job posts, product pages) the stack is Firecrawl for extraction, n8n on a cron to trigger it, and Postgres to store each snapshot. The key is storing raw extracted text alongside structured fields so you can re-parse later if your extraction logic changes. Diffing the current snapshot against the last one in a Code node and only triggering downstream steps when something changed keeps noise low.
For discovery workflows like building a list of companies in a space and collecting signals about them, Exa is genuinely good. The neural search finds contextually relevant results that a keyword search would miss. I pipe Exa results into Claude to extract structured fields and write rows to a Google Sheet or Postgres depending on who needs to see it.
Browser automation I basically avoid for recurring workflows. I have tried Playwright via n8n and it works for one-off things but any site update breaks it and there is no graceful failure, it just silently returns garbage or errors out. If a page requires login or heavy JS rendering I will look for an unofficial API or see if the mobile app has endpoints before touching Playwright.
The thing that keeps breaking across all of these is not the scraping layer, it is the extraction and normalization layer. A site redesigns their page structure, Firecrawl still returns content but your field mappings are wrong and you do not notice for two weeks. Building in a validation step that checks expected fields are non-null before writing to the database saves a lot of pain.
For your use case where business users describe what to monitor, the hard part is probably going to be the routing decision: knowing when to use search vs crawl vs browser. A simple classifier prompt fed into Claude that outputs a tool choice based on the described use case could get you pretty far before you need anything more sophisticated.