# How Muhammad Abdullah built Reddit lead discovery where a person approves every reply

Muhammad Abdullah built a system for a client that finds small business owners on Reddit describing problems the client's product solves. AI judges each post and drafts a reply in the founder's voice, then sends the draft to Slack. The system has no way to post: a person approves, edits or skips each reply and posts it by hand.

| | |
|---|---|
| **Who** | Muhammad Abdullah, AI engineer ([agenticabdullah.com](https://agenticabdullah.com), X [@abdullahaisys](https://x.com/abdullahaisys), [GitHub](https://github.com/MuhammadAbdullah95)) |
| **Problem** | A founder found customers by hand on Reddit and wanted it to scale without turning into spam |
| **Stack** | Reddit public RSS feeds, an LLM for intent and drafting, Slack (Socket Mode), AWS EC2, PostgreSQL, GitHub Actions |
| **Confidence** | High for the system (detailed first-person case study). The client is under NDA |
| **Last verified** | 2026-10-14 |

## The problem
The client, the founder of an AI phone agent company, found customers by scrolling Reddit and writing helpful replies. That only worked when he happened to be in the right community at the right time. His personal account was the asset at risk. The rule from day one: "the system finds and drafts, and a person decides and posts."

## The blueprint
1. **Read:** public feeds from 56 communities across 14 small-business sectors, checked every four hours and paced to respect Reddit's limits.
2. **Screen:** drop old, empty and automated posts.
3. **Match:** a post must come from a business owner in a target sector **and** describe a relevant problem. Mentions of competing tools count as the strongest signal.
4. **Judge:** AI assesses intent and returns a structured verdict.
5. **Draft:** AI writes a reply in the founder's voice. Each draft is checked for length, tone and content rules, with automatic revision if it fails.
6. **Review in Slack:** one card per post, with the original post and the draft in separate colors and a label ("Likely buyer" or "Worth a helpful reply"). Only named people can Approve, Edit, Rewrite or Skip. The person posts by hand.
7. **Learn the voice:**
   - The founder's before-and-after edits become examples for future drafts.
   - An instruction he gives three times becomes a standing rule.
   - A daily summary of rejected posts has "Should have reached me" and "Fair call" buttons, so the filter learns from him too.
8. **Guardrails:**
   - At most two comments per community per week.
   - No competitor mentions in drafts.
   - Posting isn't built in at all.

## Stated results
- Close to 6,000 posts assessed and more than 40 qualified prospects surfaced.
- An eightfold increase in qualified prospects after he corrected the scope against the client's brief.
- 474 automated tests.
- The filter turns down about 98% of posts. An AI re-read of 550 rejected posts disagreed with none of them.

## Caveats
- This is client work under NDA. The client and founder are unnamed, so the outcome for the client's revenue isn't stated.
- Abdullah's role was solo engineer. He wasn't the person doing the engagement.
- X: @abdullahaisys (verified 2026-10-14: linked from agenticabdullah.com, and the profile links his GitHub). The account is new, with 1 follower.

## Sources
- https://agenticabdullah.com/work/ai-community-engagement-pipeline