EMMA
SaaS / HR Tech — Canada
Client Profile
Industry
SaaS / HR Technology (Canada)
Size
Fast-growing B2B SaaS company with 850+ enterprise clients,
125,000+ end users
Business Mode
Subscription-based HR platform ($3.2M ARR) with
3,500+ articles knowledge base
Challenge
2,400+ monthly tickets, 48-72 hour response times, 12%
annual churn ($384K loss)
Business Problem
→Support tickets averaged 2,400 monthly with 52-hour median response
time (exceeding 24-hour SLA)
→ Knowledge base had 3,500+ articles but 78% of users couldn’t find
answers
→ Support agents spent 65% of time (1,560 hours monthly) answering
same 35 questions
→ CSAT declined from 8.1 to 6.2 over 12 months
→ Enterprise clients threatened non-renewal due to support quality
→ Support team burnout rate 40% annually, $180K recruitment/training
costs
→ Unable to offer 24/7 support due to $420K annual staffing costs
Objective
Transform support from cost center into competitive advantage:
Deflect 60-70% of support tickets through intelligent self-service
Reduce response time from 52 hours to under 5 minutes
Increase support team capacity 3-5x without proportional hiring
Improve customer satisfaction from 6.2 to 8.5+
Enable true 24/7 support without overnight staffing costs
Reduce churn by 50% through improved support experience
Automation Details
Intelligent Retrieval
Hybrid search (semantic + keyword) achieves 94%
relevance on first retrieval
Context Window Management
512-token chunks, 50-token overlap,
retrieving top-10 chunks per query
Multi-Turn Conversations
Buffer memory maintains context across
15+ message exchanges
Confidence Scoring
Ensemble approach determines when to escalate
(threshold: 0.75)
Agent Co-Pilot Mode
AI suggests 3 potential answers with sources,
reducing agent research time by 70%
Continuous Learning Pipeline
Nightly batch processing incorporates
new resolved tickets (40-60 daily)
Quality Assurance
Random sampling of 5% of AI responses for human
review with feedback loop
Personalization Engine
User-specific context influences answer
relevance and examples
Proactive Support
Monitors user behavior patterns, offers contextual
help (reduces ticket creation by 18%)
Multi-Channel Deployment
Same AI engine powers web chat, Slack
bot, Teams bot, email responses
Solution Overview
→ Deployed enterprise-grade RAG-powered AI knowledge assistant
→ Conversational AI chatbot trained on 3,500+ articles, 2 years support
history (28,000+ tickets)
→ Advanced NLU with context awareness across multiple conversation
turns
→ Intelligent answer generation with source citations and confidence
scoring
→ Agent co-pilot providing auto-suggested responses with relevant
documentation
→ Continuous learning system updating from new tickets, resolutions, and
user feedback
→ Multi-language support (English, French, Spanish, German)
→ Seamless escalation to human agents with full context transfer
→ Analytics dashboard tracking resolution rates, user satisfaction, and
knowledge gaps
Technology Stack
LLM
OpenAI GPT-4-Turbo (128K context), Claude 3.5 Sonnet for
complex queries
RAG Framework
LangChain with custom retrieval chains and ReAct
agents
Vector Database
Pinecone (Serverless) storing 2.8M+ vector
embeddings with hybrid search
Embeddings
OpenAI text-embedding-3-large (3,072 dimensions)
Backend
Python 3.11, FastAPI with async processing, Celery for
background tasks
Frontend
React with real-time WebSocket connections, mobile-
responsive
Integration
Zendesk REST API, Intercom, Slack bot, Microsoft Teams
bot
Infrastructure
AWS (ECS Fargate, RDS Aurora, ElastiCache), Kubernetes
Monitoring
DataDog for performance, Sentry for error tracking
Outcome & Impact
→ Ticket Deflection: 72% of queries resolved by AI without human
intervention (1,728 of 2,400 monthly)
→ Response Speed: Average first response time reduced from 52 hours to
2.3 minutes (99.9% faster)
→ Team Productivity: Support team capacity increased 4.2x – now
handling 2,400 monthly tickets with same 8-person team
→ Cost Savings: $520K annual savings from avoided hiring minus $180K
system costs = $340K net savings
→ Customer Satisfaction
: CSAT improved from 6.2 to 9.1 (47%
improvement), NPS increased from 28 to 64
→ 24/7 Support: Now offering round-the-clock support without
overnight staff (saving $420K annually)
→ Churn Reduction: Customer churn reduced from 12% to 5.5% annually,
saving $256K in lost revenue
→ Agent Satisfaction: Support agent burnout reduced from 40% to 8%,
engagement scores increased 65%
→ Resolution Quality: First-contact resolution rate improved from 45% to
78%
→ Enterprise Retention: All enterprise accounts threatening non-renewal
signed multi-year extensions
Why This Matters for Similar Clients
SaaS Companies
Any SaaS with 1,000+ support tickets monthly can
deflect 60-75% through intelligent automation, reducing support costs by
40-60%
E-Commerce
Online retailers with high ticket volumes (5,000+
monthly) can automate order tracking, returns, and common questions
Financial Services
Banks and fintechs can provide 24/7 support for
account questions while maintaining regulatory compliance
Healthcare
Patient portals can answer 70%+ of common questions
about appointments, prescriptions, billing
Telecommunications
Telecom providers handling 10,000+ monthly
support interactions can automate troubleshooting
→ Education: Universities and online learning platforms can provide
instant answers to 50,000+ students
ROI Framework
Companies with 1,000+ monthly tickets typically see
6-9 month payback, with 3-year ROI of 400-700%. Larger implementations
(10,000+ monthly tickets) can save $1-3M annually
Ticket Deflection Rate
Average Response Time
Churn Reduced