UAE automotive marketplace · 2025
AI lead-discovery engine
A 137-node pipeline where seven coordinated AI agents find relevant discussions, draft grounded replies, and post them under guardrails.
Simplified for publication. Identifiers, endpoints and customer data removed.
The problem
Finding genuinely relevant conversations to join was a manual research task, and generic AI replies were worse than no reply at all — they read as spam and risked the account.
What I built
- 01
Swept target communities for posts and comments, then filtered for relevance before any model was invoked, keeping inference cost proportional to signal
- 02
Grounded reply drafting in real sources — published articles and a live inventory API — so responses referenced actual availability rather than inventing it
- 03
Split the work across specialised agents (qualify, extract, draft, check) instead of one prompt attempting everything
- 04
Tracked every candidate through an external state store, making the pipeline resumable and preventing the same thread being answered twice
Outcome
Discovery and drafting became a scheduled background process instead of manual research
Replies were grounded in real inventory and real articles, which is what kept them from reading as spam
Resumable state meant a failed run could be re-entered without duplicate posting
Stack
- n8n
- OpenAI
- Reddit API
- Google Sheets
- JavaScript
Want something like this built?
Most of this is portable. The dedup, throttling and failure-handling patterns apply to any channel you send on.
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