
SalesHookAI
AI Orchestration Tool for Trade Businesses
Hey IH 👋
I'm Timo, solo founder of SalesHookAI (Zera). Building this for German/Europe trade businesses ( plumbers, contractors etc.) — the kind still running quotes on paper, scheduling on a whiteboard, invoices typed by hand.
Most Software on the market have an LLM wrapper only integrated: stateless chat interface bolted onto software, no real access to the system. I wanted Zera to be different, so over the last year I built:
- Agentic RAG — she pulls relevant business rules, customer preferences, and special conditions automatically at every chat start, no need to re-explain
- Persistent vector memory per customer — payment terms, delivery times, rates, VAT categories, stored permanently and recalled automatically
- Function calling across the full system — CRM, quotes, projects, Planbook, time tracking, fleet. "Schedule an appointment with Mr. Schmidt at 2pm" isn't a generated text reply — it actually changes state in the system
- Proactive monitoring — she flags a staffing gap for tomorrow or an unanswered quote sitting for 3 days, without being asked
It's built on Google Vertex AI
Where I'm at honestly: 2 paying customers so far. Bootstrapped, no team, every hour counts — which is exactly why I'm posting here instead of just quietly shipping.
Would love your take:
- If you ran a service business, what would make you trust an AI enough to let it take actions on your quotes/schedule, not just suggest them?
- Does this pitch land, or does the technical depth (RAG, vector memory) bury the actual value for a non-technical tradesperson?
I'd be happy to go into more detail about the architecture or the challenges involved in going it alone to win new clients.
About
I kept seeing tradespeople (electricians, plumbers, contractors) drowning in disconnected tools — one app for quotes, another for invoices, a spreadsheet for scheduling, WhatsApp for the team. I wanted to build one AI-fi

1 Comment
The interesting part is that Zera actually takes actions in the business system rather than stopping at chat. For a tradesperson, that distinction probably matters much more than the underlying RAG or vector-memory architecture.