I built a private AI job-intake assistant for a friend's electrical business

说明与用途

为小型电工业务创建了本地 AI 接口助手，处理混乱的客户请求，离线运行并保护客户隐私。

问题

Customers send messy requests via WhatsApp; the owner must manually parse and reply, leading to inefficiency and potential errors.

结果

The assistant reduces manual effort, ensures consistent summaries, flags emergencies, and prevents fabricated pricing; all processing stays offline on the owner’s laptop.

工作流

• 1. Customer submits request via public showcase form, which forwards to owner’s WhatsApp.
• 2. Owner pastes the WhatsApp message into the local intake script.
• 3. The script runs an open‑weight model (qwen2.5:1.5b or llama3.2:3b) via Ollama.
• 4. The model generates a one‑line summary, urgency flag, follow‑up questions, and a draft reply.
• 5. Owner reviews and sends the reply to the customer.
• 6. Public site displays job photos and contact details for future reference.

证据

Customers message him all day with requests like rewiring damaged buildings, rewinding pump coils, faulty switches, flat wiring, solar and CCTV, burnt wires, urgent equipment repairs, and ATS and contactor prices.

来源

I built a showcase site where a form sends each request to his WhatsApp, plus a private AI intake assistant on his laptop.

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Runs an open-weight model locally with Ollama and turns messy customer messages into a clean job summary, urgency level, follow‑up questions and a draft reply.

来源

Customer names, addresses and phone numbers stay on the owner's computer.

来源

I tested 8 real customer messages four times and kept every run: v1 missed a fire risk: "the wire in my room has burnt" was marked NORMAL.

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