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

21 Days
MVP Launch Time
120+
Businesses Onboarded
78%
Reduction in Missed Calls
RelayDesk AI

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

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

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

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

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

Testing & MVP Launch

Ran real-user testing, optimized responses, reduced latency, and launched the MVP with initial businesses.

Measurable Outcomes

Before

Product existed as disconnected features
Difficult onboarding for new users
No centralized system to track leads or conversations
Businesses still relied on manual follow-ups

After

AI receptionist system used by 120+ businesses
78% reduction in missed or unanswered calls
All interactions tracked inside a unified CRM
Businesses operating without full-time reception staff

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.
RelayDesk AI

Lucas Bennett

Founder, RelayDesk AI

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