SocialRadar

For most products there are, on any given day, a handful of public threads where mentioning it would genuinely help the person asking. There are also thousands where it would be noise, and communities that will correctly punish you for not knowing the difference.

Tools that automate this almost always optimise for volume, because volume is easy to measure. The result is the reply everyone recognises on sight, and a brand that is worse off than if it had said nothing.

You define a scope: the product, the problem space it addresses, the intents worth matching, the sources worth watching, and the rules it must respect — including how it is allowed to mention you at all.

Candidate threads are captured and then put through a staged pipeline rather than a single classification. Intent is matched against the scope. A separate stage asks whether replying would actually add anything, given what has already been said in the thread and how that community behaves. A third decides product fit and what mention style is appropriate, from a plain answer with no mention at all through to a direct recommendation. A final stage scores risk — spam signals, community rules, threads that are already heated.

What survives becomes a draft, with the disclosure you configured, for a person to approve, edit or discard. Nothing reaches a public thread without that step.

The pipeline is not the interesting part. Query phrasing is. In our own measurements, searching inside the right communities returned on-topic results around 96% of the time, while the equivalent site-wide search returned almost nothing usable — and two phrasings of the same intent, one conversational and one keyword-shaped, differed by the entire result set.

Browsing a community's newest posts, which was the obvious first design, turned out to be the wrong instrument entirely: high cost per thread, near-zero hit rate, because 'new' is dominated by whatever that community talks about all day rather than by the narrow question you care about.