Kate Ai

Kate: Multi-modal AI agent for sales & marketing. Chat, voice, SMS, RCS across all channels. Responds to website visitors, qualifies leads, automates follow-ups, manages pipeline—24/7.

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Inspiration

What it does

Key Innovation: Agentic Orchestration

Kate isn't one AI—she's five specialized agents working together:

  1. Lead Response Agent: First contact, qualification
  2. Calendar Intelligence Agent: Availability optimization (learns when meetings have highest show rates)
  3. Intent Classification Agent: Routes conversations based on prospect needs
  4. Follow-up Cadence Agent: Multi-touch sequences across channels
  5. Compliance Agent: Logs everything for regulatory audit trails

All agents share state through Snowflake's data platform, ensuring consistency and enabling network effects—every conversation makes Kate smarter for all users.

Challenges we ran into

1. The "Busy Right Now" Problem is Harder Than It Looks

Early version: Prospect says "call me this afternoon"

  • Kate: "I've scheduled you for 3 PM today"
  • Prospect: "No, I meant I'M calling YOU"

Solution: Built intent disambiguation using Gemini's multi-turn conversation capability. Now Kate asks clarifying questions naturally before committing to actions.

2. Calendar Booking Created False Positives

People would say "maybe Tuesday" and Kate would book it as confirmed.

Solution: Implemented confidence scoring:

  • "Tuesday at 2PM works" = 95% confidence → Auto-book
  • "Maybe Tuesday?" = 40% confidence → Ask for confirmation
  • "I'll let you know" = 10% confidence → Schedule follow-up, don't book

3. Multi-Channel State Management

Prospect starts on website chat, continues via SMS, then calls. Kate needed to remember context across all three.

Solution: Snowflake conversation tables with conversation_id that persists across channels. When prospect switches channels, Kate says "Hi again! We were just discussing your retirement planning..."

4. Voice Latency Kills Conversations

Initial voice responses took 3-5 seconds. Prospects would hang up.

Solution:

  • Stream responses using Gemini's streaming API (first words in 800ms)
  • Pre-load common responses for instant playback
  • Use shorter, more conversational sentences (reduces generation time)

5. Compliance Requirements Almost Stopped Us

Financial services requires immutable audit logs of all client communications. Our initial MongoDB setup couldn't prove immutability.

Solution: Snowflake's time-travel feature gives us cryptographically verifiable audit trails. Regulators can query:

SELECT * FROM conversations 
WHERE advisor_id = 'X' 
AT (TIMESTAMP => '2025-12-31 23:59:59');

## How I built it

## Challenges I ran into

## Accomplishments that I'm proud of

## What I learned

## What's next for Kate Ai

Built With

  • actions
  • ai
  • and
  • antigravity
  • api
  • appointment
  • booking
  • calendar
  • central
  • chat
  • ci/cd
  • cloud
  • connector
  • containerization
  • conversation
  • core-stack:-python-3.11-with-fastapi-for-api-orchestration-google-gemini-2.0-flash-for-multi-modal-ai-(chat
  • cortex
  • credential
  • data
  • development
  • docker
  • embeddings
  • for
  • gemini
  • github
  • google
  • ide)
  • infrastructure:
  • intent-classification)-snowflake-ai-data-cloud-(cortex-for-embeddings
  • iowa
  • management
  • manager
  • matching
  • ml:
  • orm
  • platform
  • prototyping
  • pydantic
  • python
  • rapid
  • rcs
  • real-time
  • redis
  • region)
  • search
  • secret
  • semantic
  • session
  • similarity
  • snowflake
  • snowpark-for-in-database-compute
  • sqlalchemy
  • state
  • storage
  • streaming
  • time-travel-for-compliance)-multi-channel-communication:-twilio-api-(sms
  • tools:
  • us
  • validation
  • vector
  • voice
  • voice)
  • websockets
  • with
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