Case study

How Raaz.app Built a Five-Agent Digital Clinic on WhatsApp Without a Developer — Peach AI

Five specialised AI agents. One patient journey. From first "Hello" to post-treatment recovery — in Hinglish, with full human oversight, deployed in 3 days.

From trypeach.ai Raaz.app helps men with PE and ED — sensitive issues that most men find hard to talk about openly. Their biggest competitor is not another startup. It is embarrassment. When a patient feels judged, they close the app and never come back. Raaz needed AI that could listen, understand local slang, and guide a patient from first contact to post-treatment recovery. A single chatbot could not do it well enough. So they built a crew.
From trypeach.ai 58%
From trypeach.ai Increase in lead-to-booking conversion

Assets

Original page visuals.

Images are carried over from the live page and framed inside the current Peach design system.

Highlights

How Raaz.app built a digital clinic on Whats — App - without a single developer

Building AI for a high-trust, nuanced vertical?

01

Trust is the barrier

41%

02

One bot cannot do everything

Patient queries handled entirely by AI

03

Language and culture matter

Better appointment show-up rate with ₹49 token

04

Scale vs empathy tradeoff

3days

05

No decision trees

To build and deploy each new agent - no developer

06

Simple guardrails

When a man is nervous about a sensitive health issue, a robotic flow feels cold. He sees a menu, feels like a ticket number, and leaves. The clinical friction is the conversion killer — not the product, not the price.

Details

Trust is the barrier

Men reaching out to Raaz are already anxious. Any moment of friction — a confusing menu, a generic question, a delayed reply — breaks the fragile trust needed to continue.

Triage, scheduling, anxiety management, payment nudging, and post-care follow-up are four completely different jobs. A single agent trained to do all of them does none of them well.

Patients communicate in Hinglish — a natural Hindi-English mix. A bot that does not understand Devanagari script, local slang, or even a rushed typo loses the patient immediately.

Hiring enough human agents for 24/7 empathetic support is expensive and impossible to scale. But pure automation without warmth is worse than no automation at all.

Instead of one bot trying to do everything, Raaz built a crew. Each agent hands off to the next when their role is done. Every agent was built in plain English — no code, no flowcharts. A junior intern manages the flows.

The Empath — First contact & triage

+58% conversion

What it does

Greets every user as "Sir." Never asks for their name straight away. Communicates in natural Hinglish using Devanagari script. Identifies whether the user has a problem, then asks about their situation — gently, without judgment.

Why it works

Removing the judgment barrier increased lead-to-booking conversion by 58% vs the previous static menu flow. Patients feel safe — not processed.

Plain English instruction used

"You are a helpful friend named Kundan. Assist men with PE and ED only. Communicate naturally in Hindi using Devanagari script. Never ask for their name immediately — keep them anonymous until they feel safe."

Appointment booking

Slot to confirm <2 min

Once the patient admits they have a problem, this agent takes over. Connects to Zoho CRM. Offers three specific 1-hour slots in AM/PM format to cut decision fatigue. Confirms without asking for re-confirmation. Tells the patient to save the doctor's number as "Raaz Incoming."

Specific slot options remove the paralysis of open-ended scheduling. The "save as Raaz Incoming" instruction means patients recognise the call and pick up. Slot to booking under 2 minutes.

FAQ & pre-appointment anxiety

Reduces cancellations

Between booking and the appointment, patients get nervous and cancel. This agent manages the anxiety window. Answers safety and privacy questions by reading from an uploaded FAQ PDF. Handles Hinglish typos — "binaries" instead of "bimari" — without breaking.

A patient in a hurry makes typos. The agent understands intent, not just spelling. Pre-appointment cancellations dropped because patients got answers fast — without waiting for a human.

How the FAQ was added

The team uploaded a PDF of their FAQs into Peach. The agent reads the file to answer questions. Adding new topics takes minutes — upload a new PDF and the agent handles it the same day.

Order & payment

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