Build a Production-Ready AI Voice Agent for Customer Support, Booking & CRM Automation

We are looking for an experienced AI automation / Voice AI developer to design and build a production-ready inbound AI voice agent for a service-based business. The agent will handle incoming customer calls, understand the customer's request, answer common questions, collect relevant information, book or reschedule appointments, update customer information, and escalate calls to a human when required. This should not be a simple voice chatbot demo. We want a working end-to-end implementation with proper integrations, error handling, conversation tracking, human handoff, testing, and basic monitoring. The goal is to create an architecture that could later be extended into a real customer support operation. Example Customer Journey A customer may call and say: "My AC has stopped working. Can someone come tomorrow afternoon?" The AI agent should be able to: Understand that the customer needs a repair appointment. Ask the necessary follow-up questions. Capture the customer's name, phone number, address, and issue. Check available appointment slots. Offer suitable times. Confirm the selected appointment. Create or update the customer record. Create the appointment in the scheduling system. Send a confirmation message. Provide the customer with the appointment details. If the AI cannot confidently resolve the request, it should transfer the call to a human representative with a short summary of the conversation. Core Requirements 1. AI Voice Agent Build an inbound conversational voice agent using a suitable Voice AI platform such as: Retell AI Vapi Twilio ElevenLabs or another appropriate platform The developer may recommend the final technology based on reliability, latency, integration flexibility, and cost. The voice experience should feel natural and conversational. The agent should support interruptions and normal conversational behavior rather than following a rigid IVR-style flow. 2. Customer Intent Detection The AI agent should identify common customer intents such as: New appointment Reschedule appointment Cancel appointment Service inquiry Pricing inquiry Existing appointment status General business questions Emergency or urgent service request Request to speak with a human The architecture should make it reasonably easy to add additional intents later. 3. Knowledge Base / FAQ The agent should be able to answer common business questions based on an approved knowledge base. Example information may include: Business hours Service areas Services offered General pricing information Appointment policies Cancellation policies Emergency service information The agent should only answer based on approved information. If the requested information is unavailable or uncertain, the agent should acknowledge that and offer escalation rather than inventing an answer. 4. Appointment Booking The agent should integrate with a calendar or scheduling system. For the initial implementation, Google Calendar, Cal.com, Calendly, or a simple scheduling API can be used. The AI should be able to: Check available appointment slots Offer available times Create an appointment Reschedule an appointment Cancel an appointment Confirm appointment details The system should prevent duplicate or invalid bookings. 5. CRM / Customer Records Integrate the AI agent with a lightweight CRM or database. This can be implemented using a platform such as: HubSpot Airtable Supabase GoHighLevel PostgreSQL or another appropriate solution Customer records should include at minimum: Customer name Phone number Email if available Address Reason for calling Appointment details Call outcome Call timestamp Conversation summary Existing customers should be matched where possible rather than creating unnecessary duplicate records. 6. Workflow Automation Use n8n or another suitable workflow automation platform for backend processes. Expected workflows may include: Customer lookup Customer creation/update Appointment creation Appointment modification Appointment cancellation SMS or email confirmation Human escalation Call summary storage Error handling Post-call processing Workflows should be organized and documented clearly. 7. Human Handoff The AI agent must know when to stop attempting automation. Examples include: Customer explicitly asks for a human Repeated misunderstanding Unsupported request Sensitive or complex issue Tool/integration failure Emergency scenario Low confidence Where technically possible, the call should transfer to a human number. Before transferring, the system should create a short summary including: Customer name Reason for calling Information already collected Actions already attempted Recommended next action The customer should not have to repeat the entire conversation. Reliability Requirements This project must include basic production controls. Tool Failure Handling If a backend API or workflow fails, the agent should not claim that the action succeeded. For example, if appointment creation fails, the agent must not tell the customer: "Your appointment has been booked." Instead, it should acknowledge the problem and either retry safely or escalate. Timeout Handling External integrations should have appropriate timeout handling. The conversation should not remain stuck indefinitely while waiting for a tool. Retry Logic Implement safe retry behavior where appropriate. Retries should avoid: Duplicate appointments Duplicate customer records Duplicate notifications Repeating irreversible actions Conversation State Important customer information collected earlier in the call should remain available throughout the conversation. The system should properly track the current task and avoid repeatedly asking the same questions. Observability & Call Logging Each call should produce a structured record. At minimum, capture: Call ID Phone number Start time Call duration Detected intent Final outcome Tools called Tool success/failure Escalation status Appointment created or modified Transcript Conversation summary Approximate AI/API cost if available A simple dashboard is preferred but not mandatory. A database table, Airtable view, or other structured interface is acceptable for the initial version. Testing Requirements The finished system should be tested against multiple customer scenarios. We expect approximately 30-50 test conversations covering situations such as: Normal scenarios New customer booking an appointment Existing customer booking another appointment Rescheduling Cancellation Asking business hours Asking about services Asking pricing questions Conversation variations Customer changes their mind Customer interrupts the agent Customer provides incomplete information Customer gives information in a different order Customer asks multiple questions Failure scenarios Calendar unavailable CRM unavailable Workflow timeout Invalid customer information No appointment slots available Unsupported request Customer repeatedly misunderstands the agent Safety / hallucination scenarios The agent should not: Fabricate appointment availability Claim an appointment was booked when it was not Invent pricing Invent policies Invent customer records Results should be documented. Expected Architecture The exact implementation can be proposed by the developer, but the expected architecture is approximately: Phone Number ↓ Voice AI Platform ↓ LLM / Conversation Logic ↓ Tool / Function Calls ↓ n8n Workflows ↓ CRM / Customer Database ↓ Calendar / Scheduling ↓ SMS / Email Confirmation ↓ Human Escalation Deliverables The project should include: Fully working inbound AI voice agent Phone number configured for testing Voice AI configuration AI system prompt / conversation instructions Customer intent handling Knowledge base integration Appointment booking integration CRM/customer database integration n8n workflows Human escalation flow Call logging Conversation transcripts Conversation summaries Basic failure handling Retry logic where appropriate Testing scenarios Test results Architecture diagram Setup documentation Short deployment / operations guide All project configurations, prompts, workflows, and relevant source code should be delivered at the end of the engagement. Technology Preferences We are open to recommendations. Possible stack: Retell AI or Vapi OpenAI / Anthropic / Gemini n8n Twilio Supabase HubSpot Google Calendar / Cal.com Resend / Twilio SMS Please explain any major technology choices you recommend. We prefer commonly used platforms rather than unnecessarily complex custom infrastructure. Budget Fixed Price: $1,000 USD This is an initial proof-of-concept / production-oriented MVP. If the implementation is successful, there may be follow-up work to expand the system with: Additional customer intents Outbound calling Multiple locations Multiple agents Additional CRM integrations Contact center integration WhatsApp Web chat Analytics QA automation Advanced monitoring Production deployment Suggested Milestones Milestone 1: Architecture & Voice Agent Foundation $200 Deliverables: Solution architecture Technology selection Voice agent configured Phone number connected Basic inbound conversation working Initial business FAQ Milestone 2: Booking, CRM & Automation $350 Deliverables: Appointment availability lookup Appointment booking Rescheduling/cancellation CRM/customer database integration n8n workflows Customer confirmation Milestone 3: Human Handoff & Reliability $250 Deliverables: Human escalation Conversation summary Error handling Tool failure handling Timeout handling Retry controls Structured call logging Milestone 4: Testing, Documentation & Handover $200 Deliverables: 30-50 test conversations Test results Final architecture diagram Deployment documentation Operations guide Source/configuration handover Final demonstration Timeline Expected duration: 2-4 weeks We care more about a reliable implementation than rushing the project. Ideal Freelancer We are looking for someone who has experience with several of the following: Voice AI Conversational AI AI agents LLM integrations Retell AI Vapi Twilio n8n API integrations CRM integrations Customer support automation Contact center technology Production AI systems AI observability Prompt engineering Agent testing / evaluations Experience building AI systems that interact with real business systems is more important than simply building chatbot demos. Proposal Requirements Please include the following in your proposal: Briefly describe a similar AI voice or automation project you have worked on. Which Voice AI platform would you use for this project and why? How would you prevent the AI from telling a customer an appointment was booked if the booking API actually failed? How would you implement human escalation? What would you use for workflow automation? What would you use for storing customer and call information? What do you believe is the biggest technical risk in this project? Please provide an approximate delivery timeline. Please do not send a generic proposal. We are particularly interested in developers who understand the difference between building a working AI demo and operating a reliable AI system in production.