AI voice agents handle calls for businesses around the clock. They pick up right away, set up meetings automatically, check out leads super quick, and deal with pushback smoothly. It saves tons of time every day, no more slogging through calls. so your team can actually close more deals. Ends up meaning quicker growth, like 3x better conversion rates, and real revenue gains without bringing on extra people. Do not wait to grab the AI advantages.😀 Like the post and share your thoughts....

As mentioned in the title, I just delivered a project which is a 24/7 Voice AI Inbound Receptionist for trade businesses. Took me about two months working closely with my client and I implemented it using Vapi, n8n, as well as Simpro, which is a field service management software specifically for the trades. The entire system does the following: - Answers inbound calls automatically - Handles job bookings, rescheduling, cancellations - Forward calls to person in charge in case of emergencies - Answers general enquiries - Creates or updates records directly inside Simpro The goal was simple: Reduce missed calls and automate admin while technicians are on-site working. In total, my client paid me just over $2,000 for this implementation and service, it's definitely a huge win for me. Happy to connect with anyone exploring Voice AI automation for service businesses. ps. You can check out this website for the demo recordings: https://simpro-voice-agent.com/ pss. I would love to share a screenshot of the n8n workflows but I think my client's VPS server is down as of writing 🫠

I've been more active in the AIS+ community lately, but I think this workflow could be useful here as well. Most n8n chatbot tutorials use RAG: vector store, embeddings, retrieval chain, and so on. It works, but you've got lots of nodes, slower responses, higher API costs, and sometimes totally wrong answers. I wanted to simplify this. The result is a fully functional 4-node chatbot: Chat Trigger → AI Agent → OpenRouter LLM + Redis memory. The entire knowledge base lives in the system prompt. So, there is no need for a vector store or embeddings. Just clean, direct context. For a small business use case (knowledge base with 5,000–10,000 words of content) it handles customer questions accurately, costs roughly $1 per 1,000 messages, and the whole thing takes about 10 minutes to build from scratch. For the advanced folks: what do you dislike about this approach? I know the obvious ceiling is context window size - that's why the knowledge base must stay under 20 pages/14,000 tokens. But are there other failure modes I'm not thinking about? Would love a real critique. For the beginners: If you've been wanting to build your first chatbot but the RAG tutorials felt overwhelming, this might be a good starting point. I put together a step-by-step video walkthrough covering everything from scratch: setting up OpenRouter and Redis, configuring credentials, building the workflow, etc. No coding required, and the workflow is included, though my recommendation is to build this simple chatbot yourself. https://www.youtube.com/watch?v=ZLTZjXvXMrc

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