Most AI tools hand you an answer and stop there. The more interesting move is to build a specialist you can reuse — one that follows the rules your industry actually runs on — and then hand it to someone else. It took about twenty minutes, no code, and I pointed it at a question I care about: what do patients ask about their condition, and does anyone answer them where they're looking?
I did it with Grok Bot, an AI assistant that can research the web, work with files, run tasks in the background, and remember how you like things done. I described what I wanted and we built it in conversation.
Building it was a conversation, not a project
I told it I wanted a bot focused on AEO and SEO for biopharma. From there it mostly built itself:
- It set its own guardrails — stay on-label, flag everything for MLR review, never invent a number.
- It saved repeatable skills for a visibility audit and for patient-question mining, so the workflow runs the same way every time instead of depending on how I happen to phrase the prompt.
- When I asked what else it could do, it pitched options and I picked. The strategist-facing additions — patient-journey mapping, unmet-need insights, competitor sentiment, monthly trend tracking — came out of that back-and-forth.
- It packaged the whole thing into a shareable template with a short onboarding chat, so someone else can install their own copy and get running.
That last part is the point. A one-off prompt is a party trick. A guardrailed workflow you can hand to a colleague is a tool.
What it found on sickle cell
I asked it to run on sickle cell disease and kept working while it went. A few minutes later it came back with:
- 90 real patient and caregiver questions, pulled from Reddit, Google, and advocacy sites and grouped by theme.
- Content gaps where patients ask constantly but only journal articles answer — life expectancy, ER care, gene therapy versus transplant. These are exactly the questions an answer engine has to source from somewhere, and right now it isn't sourcing them from brands.
- A major health system page still listing a treatment withdrawn in 2024. Stale content that engines and patients are both still reading.
- 8 plain-language FAQ drafts, each with sources, marked for MLR review — a starting point for review, not published copy.
- Posts that read like possible adverse-event reports, set aside for a safety team instead of being mined for content.
- A spreadsheet with all of it.
It was also honest about what it couldn't see — Google's AI Overviews, for one — and said so instead of guessing. In a regulated context, a tool that names its blind spots is worth more than one that papers over them.
Why it matters
Every other channel in pharma has an owner and a workflow. The answer layer — what patients read before they reach any brand site — mostly doesn't. A small, guardrailed specialist that mines the real questions and flags the compliance edges is how you start treating that layer like a channel instead of a mystery.
For marketers, strategists, and agencies, that's the difference between a clever demo and something a team actually uses.
Try it
I packaged mine as Patient Convo Tracker. Install it, give it a condition you work on, and see what your patients are actually asking. If you build your own for a different use case, I'd like to see it.