Post by BraivIQ AI Agency

A MedTech firm. Dozens of markets. All queued behind the same experts. So why not just use one model? Because one model would have worked. The drafts would have come back clean. And that's exactly the problem. A clean draft and a correct draft look identical. Somebody still has to say "don't trust this one." No model does that on its own. So we built three things: 1. Rule retrieval layer → Selenium and BeautifulSoup pull every live source → Rules land structured, not raw scraped text → New guidance gets picked up as it publishes 2. Step-by-step drafting chain → LangChain runs one task at a time → Summarise, classify, align to template → No step can carry an error forward 3. Scoring gate → ROUGE and BERTScore check draft against source → Every draft leaves with a score attached → Only the weak ones reach a human The first two write. The third one doubts. The results? ✔ Around 90% alignment, measured not assumed ✔ 40% reduction in cost per document ✔ 100+ documents a day, same small team ✔ Experts read the flagged few, not all of it Most people ask how accurate their AI is. It's the wrong question. Accuracy you can't measure is a guess. A guess nobody flagged is a finding. Ask instead whether the system knows what it doesn't know. Because most don't. They just produce, and everything downstream assumes it's fine. You already have the review capacity. It's just pointed at everything equally. Your experts are reading ninety clean documents to find the ten that aren't. Which is why this one's worth sitting with: What's the last thing AI wrote in your business that nobody checked? 📅 Want to know where your review time goes? Free 30-min AI audit → https://braiviq.com ♻️ Repost if this helps someone in your network.

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