Case Study
We turned our founder’s WhatsApp into an interface for running AI Garage
How we built Shushi — a personal AI agent that works inside our founder’s existing WhatsApp conversations to understand requests, check context, use business tools, ask for approval, and take action.
Built for
AI Garage — internal dogfooding
Status
Live · used by AI Garage’s founder
Interface
Shushi
AI Agent · WhatsApp
Shushi, can you book an intro meet with Vedant for coming Monday at 2 pm for 1 hr?
v*****@gmail.com
10:42 AM
Sure! I’ve set up the details for your meeting with Vedant:
Intro meet with Vedant
Monday · 2:00–3:00 PM IST
v*****@gmail.com
Company calendar
Shall I go ahead and book this for you?
10:42 AM
yes pls
10:42 AM
Done! I’ve booked the intro meet with Vedant for tomorrow at 2:00 PM IST. Invitations have been sent.
10:42 AM
16 seconds from request → booked.
The State Before Shushi
The problem wasn’t Google Calendar. The meeting request was already happening inside WhatsApp. But completing it meant leaving the conversation, opening Calendar, checking multiple calendars, finding conflicts, creating an event, copying details, adding attendees, configuring the meeting, sending the invite, and returning to WhatsApp.
The same fragmentation appears across dozens of founder workflows. So instead of building another dashboard, we asked:
What if WhatsApp itself could become the interface to AI Garage’s operations?
The Challenge
A useful WhatsApp agent couldn’t simply translate messages into API calls. It needed to understand incomplete requests, remember what was being discussed, distinguish between conversations, search multiple calendars, respect Shubham’s preferences, detect conflicts, ask for missing information, and know when not to act.
Most importantly, consequential actions needed to remain under human control.
The goal: make operating AI Garage conversational without making it unpredictable.
Real Usage
Since we started recording Shushi’s activity:
196
Messages processed
96
Shushi responses
23
Active usage days
23
Completed calendar actions
~1+ hr
Direct calendar administration automated
Conservative estimate of 3 minutes of manual calendar administration per completed action. Excludes availability checks, context switching, memory retrieval, and other Shushi workflows.
One Conversation. Multiple Systems.
A request as simple as:
“Shushi, book calendar with Ronit for tomorrow 2pm”
can trigger an entire workflow.
01
Understand
Extract the person, date, time, and intent from natural language.
02
Check
Search relevant calendars and operating rules.
03
Clarify
Ask for duration, email, or calendar when required.
04
Remember
Carry those details across subsequent WhatsApp messages.
05
Confirm
Summarise the proposed action before doing anything consequential.
06
Act
Create or update the event and send invitations.
07
Continue
Keep the resulting action available as conversational context.
It Doesn’t Just Follow Commands
Shushi understands the operational rules around every action.
It knows operating preferences
When a Sunday meeting was requested for 1 PM, Shushi found an existing Fellowship session and knew Shubham’s preferred Sunday booking window started at 2 PM. Instead of blindly booking, it proposed the nearest appropriate slot.
It knows when information is missing
If a meeting request doesn’t contain a duration or attendee email, Shushi asks rather than inventing one.
Preferences aren’t hard restrictions
If Shubham explicitly wants a meeting outside his normal booking window, he can override the preference conversationally.
It works across identities
Shushi can reason across Shubham’s Personal, Company, and AI Garage calendars and use the appropriate one for the task.
Review Over Autonomy
We deliberately didn’t design Shushi to act autonomously whenever it thinks it understands something. Before consequential actions, it shows what it intends to do and waits for confirmation.
That also creates a recovery path when AI gets something wrong.
Real conversation — error correction
Meeting seems to have been scheduled till 8 pm?
I’ve prepared an update to change the end time to 2:30 PM IST… Shall I go ahead and fix this for you?
Yes pls.
The goal isn’t zero mistakes. It’s controlled, inspectable automation.
Architecture
The complexity stays behind WhatsApp.
Under the hood
WhatsApp / Baileys
The conversational interface and message transport.
n8n
Self-hosted orchestration for deterministic workflows and tool execution.
Gemini
Natural-language understanding and reasoning.
Supabase
Conversation state, pending actions, and operational context.
Google Calendar
Availability, event creation, and event updates.
Custom APIs
The layer that lets Shushi progressively reach more AI Garage systems.
What Makes It More Than a WhatsApp Bot
A chatbot returns an answer. Shushi can complete an operation.
That distinction changes WhatsApp from a messaging surface into an operating interface.
Beyond Calendar
Calendar was the first production workflow, not the end state. Shushi is already capable of retrieving information from Shubham’s broader context.
Real conversation — AI Garage WhatsApp group
Shushi, what was my review of profound profile?
Shushi retrieved his previous observations and returned the relevant context directly into the group conversation.
The interface hadn’t changed. The capability underneath it had.
Why we built it for ourselves first
Shushi wasn’t built as a portfolio demo. Shubham actually uses it while running AI Garage. Every awkward clarification, failed action, missing integration, and edge case becomes something we experience ourselves.
Dogfood the automation before recommending the pattern to someone else.
Results
23 completed calendar actions executed through the agent.
Multiple calendars → one interface Company, AI Garage, and personal context can be reasoned about from WhatsApp.
Human approval built in Consequential actions wait for explicit confirmation.
No new interface to learn The agent lives inside a tool already used every day.
Request → booked in ~16 seconds A real meeting request has gone from WhatsApp message to confirmed calendar booking.
Self-hosted orchestration The automation layer runs on infrastructure controlled by AI Garage.
Shushi Is the First Agent, Not the Last
The interesting part isn’t building a calendar assistant called Shushi. It’s the underlying pattern:
Person + existing interface + company context + business rules + tools + human approval
Change those components and the same architecture can support very different jobs. We’re already exploring this pattern with assistants such as Loop for AI Garage’s community operations, and extending Shushi itself to interact with browsers — starting with the ability to fill any form shared via WhatsApp using the user’s existing context, with human approval before submission.
For another business, the interface could be WhatsApp, Slack, email, or something internal. The workflows could be sales, hiring, support, finance, or operations.
What would your Shushi do?
Think about the work that already begins in your team’s WhatsApp messages, emails, spreadsheets, and internal conversations. What happens next manually? That’s where we start.