Add Buyer Intent Detection to Your OpenClaw Agent
Not every community post is a lead. SignalPipe's two-pipeline process — 3-stage scoring filter then a 3-judge AI drafting swarm — finds the 15% that are.
Install SignalPipe on OpenClaw
openclaw plugins install signalpipeAvailable on ClawHub · MIT licensed · Open source on GitHub
What is OpenClaw Buyer Intent Detection?
Buyer intent detection is the core challenge in community-based lead generation. Most posts are noise. SignalPipe's pipeline was designed to find the minority of posts that indicate genuine, near-term purchase intent — without flooding your queue with false positives.
How SignalPipe adds Buyer Intent Detection to OpenClaw
Two sequential pipelines. First the scoring filter: multi-factor semantic scoring measures how closely a post matches the language your buyers actually use, urgency and specificity are weighed, and a sarcasm check removes false positives — together these drop the bulk of posts before any expensive model runs. Surviving leads go to a 3-judge AI drafting swarm that reads each one and writes a reply. Only a small fraction reach your OpenClaw session, each with a draft ready.
How to set up Buyer Intent Detection in OpenClaw
Define buyer phrases
Write anchor sentences from the buyer's perspective — phrases that describe the problem your product solves. These train the embedding similarity stage.
Add anchor sentences
Write (or have your agent draft) the sentences your buyers actually say when they are looking for what you sell. These anchors are what every post is scored against — they matter far more than any keyword list.
Tune with feedback
Approve and reject leads in your queue. The RL system learns from your decisions and adjusts scoring weights per-station — productive feeds rise, noisy feeds fall, and your product's good sources never get penalised for one bad source.
Review only what matters
After a week of training, your queue contains only high-confidence buying signals. Reviewing 5-10 leads per day replaces hours of manual scanning.
Frequently asked questions
How accurate is SignalPipe's intent detection?
We are not going to quote an accuracy percentage, because the number would be misleading: the biggest determinant of queue quality is not the scoring, it is which communities you point it at. The same engine on a feed where buyers state requirements looks excellent, and on a feed full of people who build in your category looks poor — those people discuss tools constantly and then build their own. What the engine does is read each post against your buyers own language, weigh how specific and urgent it is, and put it to three judges who assess independently and often disagree. Weak signals are dropped rather than forwarded, and every approve and reject tunes the scorer for your account. Judge it on the first week of your own queue.
What makes the 3-judge drafting swarm better than a single model?
A single model can be confidently wrong. The Skeptic-Analyst-Optimist voting structure forces adversarial evaluation of each lead before a reply is drafted. Ensemble veto logic means overly optimistic assessments never produce a reply.
Can I adjust the scoring threshold?
Yes. You can configure the minimum score threshold per product. Higher thresholds mean fewer but higher-confidence leads. Lower thresholds mean more volume with more noise.
Does it detect competitor mentions?
Yes. Competitor mention detection is built in. Posts mentioning your competitors are automatically flagged and score-boosted — these convert at the highest rate.
Add Buyer Intent Detection to your OpenClaw agent
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Full setup guide →
Full toolset, anchor sentences, station types
How intent scoring works →
Two-pipeline scoring process explained
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