Hi everyone! I've been lurking for a bit and finally have something to share. I'm Linda, and I'm relatively new to using AI tools like You.com in our workflow. We're a small team trying to get better at tracking leads and conversations, and I've been experimenting with You.com's API to pull out some really interesting search and summary data for our CRM (we use a popular basic SaaS one).
Honestly, I was getting a bit overwhelmed. The data from You.com is fantastic for context, but the format was a little messy for our needs—lots of extra text, citations mixed in with the core insights, and the structure wasn't always consistent for creating neat contact notes or activity logs. I wanted a way to clean it up automatically.
So, I spent my weekend (maybe too much of it!) and built a pretty simple Python script. It basically takes the raw text output I get, strips out the citation markers and boilerplate phrases, isolates the key summaries or answers, and formats it into a clean note. Then I can just paste that note into a contact's timeline or use it to update a deal stage. It’s not fancy, but it’s saved me hours of manual copying and cleaning already.
I was wondering if anyone else here has tried something similar? I'm sure there are better ways to do this, and I'd love any advice. Specifically, I'm not sure if I'm missing other useful data points I could extract, like the tone of the summary or categorizing the type of query. Also, if there are any project management or marketing automation folks here who've integrated this kind of cleaned data into a sequence or a task, I'd be so curious to hear about your workflow!
Thanks for being such a welcoming community. I'm really looking forward to learning from you all.
Hey Linda, that's a great weekend project. I've done similar text cleanup for feeding webhook data from various sources into our CRM. The inconsistent structure is the real challenge.
One thing I learned the hard way: if you're using regex to strip citation markers, make sure you account for different numbering formats (like [1] vs (1) or even just asterisks). It's easy to break the actual content if the source API changes its format.
Is your script running locally, or have you set up a small endpoint to process the data automatically? I found wrapping a script like this in a tiny Lambda function (or a container) made it a lot easier for the team to use without needing Python on their machines.
Cloud cost nerd. No, I don't use Reserved Instances.
Great point about regex for citations, it's a real rabbit hole. I've had to update mine a few times already.
Wrapping it in a small serverless function is definitely the way to go. I used Pipedream for mine - dead simple to set up a webhook endpoint and it runs reliably. Saves me from managing any infra.
dk
Totally agree on serverless making it simple to share. That regex rabbit hole is exactly why I moved away from trying to parse everything perfectly on ingestion.
I use a similar setup for my own workflows, but I always add a simple "raw text" field to the output that holds the original, messy data, alongside the cleaned version. It's saved my skin more than once when a pattern I missed needed a quick script fix.
Pipedream's a great choice for getting started. Do you find its built-in data stores useful for any temporary logging, or do you pipe everything out immediately?
That's a smart strategy. I follow a similar pattern, but I log the raw data to a separate 'staging' table in the warehouse rather than embedding it in the output. This lets me reprocess from the raw layer if the cleaning logic changes, without needing to touch the CRM's history.
Regarding the Pipedream data stores, I've found them useful for short-term operational debugging, like checking the last 100 payload shapes. For anything needing more than a few days retention or actual analysis, I pipe to a dedicated logging table immediately. The built-in stores are a bit of a black box for query performance.
That's a really good point about storing raw data separately. I'm still at the stage where I'm just saving the original JSON file next to the cleaned CSV locally. But a staging table sounds much cleaner, especially if the source API changes its schema.
When you say "reprocess from the raw layer," do you mean running a full script over the old staging data? I imagine you'd need to version your cleaning logic somehow to track what changed.
Absolutely agree on the lambda or container endpoint being the right move for team usability, but you're already in for a world of maintenance pain with that regex approach.
The citation format problem is just a symptom. The real issue is you're now responsible for parsing an undocumented, potentially unstable output from a third party. Every time You.com tweaks their model's summarization style or adds a new feature, you're the one debugging broken contact notes at 2am.
I'd argue the script should fail fast and loud on any structure it doesn't explicitly recognize, and alert you, rather than trying to silently clean it. Log the raw payload, as others mentioned, but also build in a validation step that compares the cleaned output length or key term count against the raw input. If the delta is outside a threshold, it flags for review. Otherwise you'll silently lose data.
Measure twice, migrate once.