Hiring Automation Builders.
We are initiating a structured implementation phase to evaluate execution quality, system reliability, and delivery standards ahead of a potential long-term collaboration with **Levanrohe.com**. This phase requires building a **complete, production-grade reservation automation pipeline**, starting from AI voice interactions and ending with operational notifications to restaurant staff. The objective is to validate that the system can be delivered **cleanly, deterministically, and without manual intervention**. Below is the required scope and execution logic. --- 1. Objective (Non-Negotiable) Build an end-to-end automated flow that: 1. Receives finalized reservation data from **Retell AI** 2. Processes and validates that data in real time 3. Pushes the reservation into **xMenu** in a structured, API-compliant manner 4. Notifies restaurant staff via **Zadarma SMS** 5. Operates entirely through **Make.com** as the orchestration layer This system must function reliably under real operational conditions. --- 2. Authoritative Flow Overview **Source of Truth:** Retell AI (call-ended event) **Orchestration Layer:** Make.com **Downstream Systems:** xMenu (data sink), Zadarma (notifications) High-level flow: ``` Customer Call ā Retell AI (call concludes) ā Make.com Webhook (authoritative payload) ā Data validation & normalization ā xMenu API (reservation / order write) ā Zadarma SMS (staff notification) ``` No UI-driven steps. No manual confirmations. No hidden dependencies. --- 3. Step-by-Step Technical Responsibilities 3.1 Retell ā Make.com (Webhook Ingestion) * Configure Retell to send **call.ended** events to a Make.com webhook * Payload must include: * Guest name * Phone number * Date * Time * Party size * Notes (optional) * Treat this payload as **finalized reservation intent** Make.com must reject incomplete or malformed payloads explicitly. --- 3.2 Data Validation & Normalization (Make.com) Inside Make.com: * Validate required fields