Observed Signal · Apr 17, 2026 · Technical Release · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Developer Builds Sales-Prep AI Using LLMs and LINE Bot
A developer built a Sales Prep AI accessible via a LINE bot (pre-talk.vercel.app) that takes a company or business-card input, runs web research, and returns a structured report. The system routes light input interpretation to Claude Haiku and heavy analysis/OCR to Claude Sonnet (the author initially used GPT-4o-mini), uses Tavily for web search, and stores reports in Supabase. Engineering challenges included Vercel Hobby's 10‑second timeout (worked around by streaming a heartbeat), hallucinations (mitigated via fact/inference separation and an output gate), official-site detection, and agent sprawl (reduced by tightening agent roles). Measured API cost per research run is roughly $0.40 (range $0.24–$0.52). The post is a technical case study describing architecture, costs, and practical mitigations rather than a commercial product announcement.
Practical engineering case study showing how to integrate LLMs, multimodal OCR, search, and bot interfaces (LINE) with concrete mitigation patterns for hallucinations and timeouts; useful for builders but not industry-shifting.
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Key Takeaways & Evidence Grounding
- Author built a Sales Prep AI accessible via LINE and published at https://pre-talk.vercel.app
- System uses Claude Haiku for input interpretation and Claude Sonnet for heavy analysis and OCR; the author started with GPT-4o-mini
- Web search provider changed from DuckDuckGo to Tavily to improve search quality
- Workaround for Vercel Hobby 10-second function timeout: stream periodic blank-space heartbeats to keep the connection alive
- Measured API cost per research run is approximately $0.40 (range $0.24–$0.52); reports stored in Supabase and auto-deleted after 30 days
Connected Companies & Entities
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