All Case Studies
Field-Services Platform · US Home Services · Field Operations AI Voice + Chat + SMS

AI Scheduling Assistant

An omni-channel AI assistant built for a US field-services platform, deployed across Voice, Web Chat, and SMS — replacing manual field-service scheduling with intelligent, weather-aware automation. Running in production today, with every interaction audit-logged.

$80-120K
Annual Savings
3 min
Avg Call Duration
vs 8 min manual
0 min
Wait Time
vs 15 min manual
40%
Call Volume Reduction
in human calls
99%
Availability
vs 85% manual
// the_challenge

500+ calls a day.
15-minute wait times.
Everything manual.

500+

Daily Calls

Overwhelming volume creating bottlenecks across the scheduling team.

15 min

Average Wait Time

Customer frustration leading to abandoned calls and lost revenue.

15-20%

No-Show Rate

Poor communication and manual errors causing missed appointments.

$40K+

Per Agent Per Year

Scaling with human agents was economically unsustainable.

⚠

Weather & Double-Booking Errors

Outdoor jobs booked on rain days. Double-bookings from manual calendar management. No systematic way to prevent either.

// the_solution

What We Built

01

Omni-Channel AI Assistant

Unified assistant deployed across Voice, Web Chat, and SMS channels, allowing customers to book anytime, anywhere — with one consistent experience.

Claude Sonnet 3.5 DSPy Optimization
02

High-Performance Voice

Voice-optimized flow delivering natural conversation with sub-2.5s latency, replacing raw data reading with human-like speech patterns.

Voice AI Sub-2.5s Latency
03

Smart Logic

Weather-aware scheduling checks forecasts before booking outdoor jobs. Mandatory "Review & Confirm" loop ensures zero double-bookings.

Weather API Confirm Loop
architecture — ai-scheduling-assistant
Channels
Voice (Phone)
Web Chat
SMS
AI Core
Claude Sonnet 3.5
DSPy Optimizer
NLU Pipeline
Intelligence
Weather API
Calendar Engine
Conflict Detection
Integration
CRM Sync
Notification Engine
Audit Logging
Voice Stack
TTS Engine
ASR Processing
Latency Optimizer
// roi_breakdown

The Math

Before: The Cost of Manual

✕ 500+ daily calls x 8 min average = 67 agent-hours/day
✕ 15 min average wait time = abandoned calls + lost bookings
✕ 15-20% no-show rate = wasted technician time
✕ $40K+ per agent/year, and you need more every quarter

After: The AI Dividend

3 min avg call (AI) vs 8 min (human) = 62% faster
0 min wait time = zero abandoned calls
Weather-aware + confirm loop = near-zero no-shows
$80-120K annual savings = 2-3 agent salaries eliminated
// how_we_delivered

Delivered Through Catalyst Studio

This project used our 4-6 week Proof of Tech methodology. Week 1: stakeholder alignment and use case definition. Week 2: architecture and model selection. Weeks 2-3: rapid build with iterative demos. Week 4: Go/No-Go gate — it was a Go.

Learn about our Catalyst Studio
// related_platform

Voice, chat and SMS agents, packaged as a platform

Omni-channel agents with escalation, audit logging and enterprise system integrations are what the Agent Platform provides for any workflow, not just scheduling.

Explore the Agent Platform All platforms

Have a similar challenge?

We can build a proof of concept for your scheduling, customer service, or voice automation use case in 4-6 weeks.

Book a 4-6 Week Sprint