Kustomer : Predictive AI for CRM-Driven Customer Support Automation

General AI SaaS Platforms

Kustomer (USA) – AI-Powered CRM for Predictive Support Automation

Kustomer partnered with us to supercharge their CRM platform with predictive AI that streamlines customer support. Our solution includes smart ticket tagging, priority scoring, and suggested replies based on historical data and customer tone. It provides agents with customer sentiment insights and auto-populates fields to reduce friction in workflows. The system learns from interactions over time, improving routing accuracy and reducing resolution time. With seamless integration across email, chat, and social channels, Kustomer’s clients now deliver smarter, faster, and more proactive service.

Project Overview

Client: Kustomer (U.S.-based CRM platform used by brands like Glossier, Ring, and UNTUCKit)
Challenge: Manual ticket triage, inconsistent response tone, and delays in customer resolution
Goal: Build predictive AI features to:
- Auto-tag and score incoming support tickets based on urgency and sentiment
- Suggest dynamic, context-aware replies to agents in real time
- Streamline workflows across support channels (email, chat, social)
Team: 8 (3 ML Engineers, 2 NLP Experts, 2 Backend Devs, 1 QA Engineer)
Duration: 5 months (Design phase → NLP modeling → Multi-channel deployment)

“GenX empowered our CRM with next-gen smart features.. Our support teams are now proactive, faster, and closer to our customers than ever.”

Head of Product, Kustomer

The Challenge

Critical Pain Points:

Agents spent too much time triaging tickets and copy-pasting responses
Lack of tone detection led to tone-deaf replies and escalations
Support systems operated in silos with no unified customer sentiment context

Technical Hurdles:

Training models on noisy and unstructured support logs across verticals
Accurately interpreting sentiment and intent from short-form or emotionally charged text
Building channel-agnostic automation without sacrificing brand tone or compliance

Tech Stack

Component	Technologies
AI & NLP Models	BERT, GPT-4, XGBoost, LightGBM
Data Processing	AWS SageMaker, Kafka, S3, PostgreSQL
Sentiment & Intent Classification	HuggingFace Transformers, spaCy, VADER
CRM Integration Layer	GraphQL, REST APIs, Webhooks, WebSockets
Agent UI Tools	React, Redux, Socket.IO
Monitoring & Compliance	Datadog, Sentry, GDPR-compliant logging modules

Key Innovations

Smart tagging, sentiment analysis, and reply suggestions accelerated support workflows. The AI routed tickets and pre-filled data based on historical context. Agents delivered faster, more proactive service with less effort.

Predictive Ticket Prioritization

Automatically flagged high-risk interactions based on tone and urgency

Result: 38% drop in missed escalations and SLA violations

Contextual Suggested Replies

Personalized, pre-approved answers generated live for each ticket

Result: 2.6x faster response time for common support categories

Unified Sentiment Intelligence Dashboard

Offered support leaders a real-time view of customer mood and intent

Result: 31% improvement in CSAT scores through proactive coaching

Our AI/ML Architecture

Core Models

Smart Triage Engine:
- Classifies tickets using LightGBM + BERT-based sentiment scoring
- Assigns priority level, intent category, and escalation likelihood
Suggested Reply Generator:
- GPT-powered engine trained on approved response templates
- Dynamically adjusts tone, length, and language per user mood and issue type
Agent Assist & Auto-Fill Module:
- Auto-populates CRM fields with entity extraction and smart autofill
- Learns frequently used workflows to reduce keystrokes and clicks