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: (1) keyword gate blocks obvious noise, (2) multi-factor semantic scoring measures buyer intent, (3) sarcasm detection removes false positives — these three stages filter ~85% of posts. Surviving leads go to a 3-judge AI drafting swarm that evaluates the lead via ensemble weighting and writes a reply. Only the top 15% 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 keyword signals
Add buy_signal_keywords that indicate purchase intent in your category. These feed the keyword gate and boost scoring for relevant posts.
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?
After two weeks of feedback training, most users see false positive rates below 10% in their review queue. The RL system continuously improves as you approve and reject leads.
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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More OpenClaw use cases
Full setup guide →
Full toolset, anchor sentences, station types
How intent scoring works →
Two-pipeline scoring process explained
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