One of the projects I run is a content platform that needs to stay on top of new scientific publications. Doing that by hand means someone scans databases every morning, reads abstracts, decides what's relevant, and writes summaries. Classic candidate for automation — high volume, repetitive, clear quality criteria.
Here's the shape of the pipeline we ended up with, because it generalizes to a lot of "monitor → filter → summarize → publish" workflows:
The pipeline
- Ingest — a scheduled job queries public research APIs for anything new matching our topics since the last run.
- Filter — cheap pass first: dedupe, language check, keyword prefilter. Only survivors go to the model. This cut our token spend massively.
- Enrich — an AI pass scores relevance, extracts the key finding, and drafts a plain-language summary with a strict output schema. Anything that fails validation gets retried once, then parked for human review.
- Publish — approved items land in the site database and appear in a daily digest. A human can veto anything, but doesn't have to touch the happy path.
What I'd tell a client from this build
The schema is the product. The single biggest quality jump came from forcing the model into a rigid output structure and validating it, not from a better prompt or a bigger model.
Plan for empty days. The first version looked "broken" on days with no relevant papers. Automations need to communicate "nothing today, and that's fine" — silence reads as failure.
The human veto is what makes people trust it. Nobody had to approve every item, but knowing they could is what got the pipeline accepted. Full autonomy is something you earn after weeks of clean output, not something you ship on day one.
Cheap filters before expensive models. Obvious in hindsight, skipped in most builds I see.
Total build time was days, not months — because the scope was one pipeline with one clear output, not "AI for our content team." That scoping discipline is transferable to almost every workflow we automate.