Launched MVP of AI Receptionist SaaS in 21 Days, Now Serving 120+ Businesses

Overview
RelayDesk AI wanted to build a product that could replace traditional receptionists using AI voice and chat. We helped them structure, build, and launch a working MVP in 21 days and scale it into a system businesses actually rely on.
The Challenge
RelayDesk AI had a strong vision — an AI receptionist that could handle calls and chats for businesses. But in its early stage, the product lacked structure. Features existed, but they didn’t work together as a system. Early users struggled to set things up, responses weren’t always consistent, and there was no central place to track conversations or leads. Businesses still had to manually follow up, which defeated the purpose of automation. The core problem wasn’t the technology — it was turning it into a usable, reliable product.
Our Solution
Instead of adding more features, we focused on simplifying and structuring the product into a complete system. We designed a flow where businesses could upload their data — FAQs, services, pricing — which powered a RAG- based knowledge engine.This ensured the AI responded based on real business information, not generic replies. On top of this, we deployed AI voice agents to handle inbound calls and AI chat agents for websites and messaging platforms.Every interaction was connected to a centralized CRM where businesses could track conversations, leads, and bookings. The biggest shift was in usability.We simplified onboarding into a guided setup, allowing businesses to go live quickly without technical complexity.The result was not just an AI tool, but a working receptionist system businesses could depend on daily.
Process & Timeline
Day 1–3
Day 1–3
System Design & Product Structuring
Redefined the product into a unified AI receptionist system. Mapped how businesses would upload data, configure AI behavior, and manage leads.
Day 4–8
Day 4–8
Knowledge Engine Setup (RAG)
Built the data ingestion system for FAQs, services, and documents. Structured how AI retrieves and delivers accurate, business-specific responses.
Day 9–14
Day 9–14
AI Voice & Chat Deployment
Implemented AI calling agents for inbound calls and chat agents for web and messaging platforms. Tested real conversation scenarios and fallback handling.
Day 15–18
Day 15–18
CRM & Automation Layer
Developed a centralized dashboard to track calls, chats, and leads. Added automated workflows for follow-ups, tagging, and conversation history.
Day 19–21
Day 19–21
Testing & MVP Launch
Ran real-user testing, optimized responses, reduced latency, and launched the MVP with initial businesses.
Measurable Outcomes
Before
After
Client Voice
We had the idea for an AI receptionist, but it felt scattered and hard to use at first. Rekvi stepped in and within just 21 days turned it into a proper MVP. They simplified onboarding, structured the data flow, and made the whole system reliable. Our users now depend on it daily to handle calls and inquiries. Big thanks to the Rekvi team, the experience of working with them was smooth, clear, and genuinely impressive.

Lucas Bennett
Founder, RelayDesk AI
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