Case study / UAE automotive marketplace / 2025

Relevant buying conversations, found with less manual research.

A 137-node pipeline where seven coordinated AI agents find relevant discussions, draft grounded replies, and post them under guardrails.

Counts from the production workflow · Client identity withheld

01 / What changed

From activity without a record to a system you can inspect.

Before

Finding conversations worth joining was manual research, and generic AI replies read as spam and put the account at risk.

After

A scheduled pipeline that filters for relevance first, grounds every draft in real inventory and articles, and checks it before anything is posted.

02 / How it works

AI lead-discovery engine

Simplified for publication

Scheduled sweep

Article corpus

Inventory API

Posts + comments

Relevance filter

Qualify intent

Draft reply

Guardrail check

Post comment

State store

Identifiers, endpoints and customer data removed.

03 / The decisions

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

04 / What it changed

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

What the evidence shows

Node and agent counts describe the system's shape, not its commercial result. Engagement outcomes were not measured in a way that can be published.

Your next chapter

Bring one process, one bottleneck or one growth goal. Let’s find a useful next step.