---
title: "I built a no-code patient-insight tool in 20 minutes"
date: "2026-09-28"
summary: "No code, on-label, MLR-aware, and shareable — a specialist AI agent for pharma patient-question mining. I pointed it at sickle cell; here's what it found."
tags: [aeo, martech]
---

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- **A major health system page still listing a treatment withdrawn in 2024.** Stale content that engines and patients are both still reading.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](https://x.ai/bot/xfE5ddKdJ3qQcKN6o2EZI)**. 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.