Dev.to: We had AI pitching our customers' aunts. Here's the three-axis classification fix.
A software company's AI mistakenly sent sales pitches to users' family members, including a customer's aunt, due to flawed contact classification. They fixed the issue by replacing a single-segment system with a three-axis model separating relationship, approach, and goal. This reduced customer complaints from 7% to under 1% and improved message relevance.
- ▪The AI initially classified contacts using a single 'segment' field, leading to inappropriate sales messages being sent to family members.
- ▪The new system uses three independent axes: relationship (user-defined), approach (AI-inferred pitch angle), and goal (user-set engagement level).
- ▪A hard rule prevents pitching to contacts marked as family or close friends, regardless of inferred business relevance.
- ▪Customer complaints about inappropriate drafts dropped from ~7% to under 1% after the fix.
- ▪Operators now frequently adjust the 'goal' setting to maintain personal relationships without pitching.
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| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/aaron_815c6e462bfcdfb46ba/devto-we-had-ai-pitching-our-customers-aunts-heres-the-three-axis-classification-fix-3h6l |
| Publication time | Sun, 17 May 2026 04:20:46 +0000 |
| Retrieval time | 2026-05-17T04:33:58.434Z |
| Last seen | 2026-05-17T04:33:58.434Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 4JCCPjqLmCr8 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3934236) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Aaron Posted on May 17 Dev.to: We had AI pitching our customers' aunts. Here's the three-axis classification fix. #softwaredevelopment #saas #ai #llm The bug report A customer wrote in: Why is the tool drafting a SaaS sales pitch to my aunt? I didn't know whether to laugh or hide. We'd just shipped warm-market draft-generation in our Chrome extension — a tool that scrapes a user's Facebook + LinkedIn connections and drafts personalized outreach messages they can edit and send.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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