Healthcare Market Intelligence

Useful product feedback and sales conversations were spread across more than 30 Reddit communities. Checking them by hand took too long. Urgent posts, such as a person asking for a recommendation, could become stale within hours.

I built a scheduled research system that collects public posts, reads the surrounding discussion, removes repeats and ranks each item. Urgent findings are emailed immediately. The rest go into a twice-daily digest.

The output is a research queue with source links and draft talking points for review.

1. Poll more than 30 subreddits through the Reddit API. Focused communities are read broadly. General communities are filtered using competitor and category terms.
2. Load each candidate post and its full public comment tree so the ranking uses the surrounding discussion.
3. Remove posts already processed and discard cached items after 24 hours.
4. Ask a language model for a strict JSON result containing High, Medium or Low relevance, an urgency flag and one short reason. Validate the returned fields before use.
5. Combine that result with upvotes and comment count to produce a clear ranking score.
6. Send urgent recommendation requests as immediate alerts. Cache the remaining items for digest emails at 9 AM and 9 PM UTC.
7. Build a readable HTML email with source links, the reason each item was selected and response ideas for manual review.
8. Protect unattended runs with file locks, exponential retry, structured logs and a failure email if the job stops.
9. Deploy through a repeatable shell script that sets up an Ubuntu server, Python environment, restricted credentials and cron schedule.

Scanning and sorting more than 30 communities would take five to seven hours each week. The two daily digests reduce that to about 20 minutes of review a day, saving roughly four hours each week.

Urgent recommendation requests arrive within one polling cycle. Routine findings are grouped into two predictable reviews instead of interrupting the day.