Client Problem
ProjectHub, a fast-growing SaaS platform for project management, serves over 10,000 small to medium-sized businesses. With rapid expansion came an overwhelming volume of support requests—averaging over 500 inquiries a day. Their support team of 8 couldn’t keep up, leading to long response times, frustrated customers, and rising operational strain.
The Challenge
ProjectHub faced several mounting challenges in its customer support function:
First response times ballooned to over 12 hours
40% of agents’ time was spent answering the same recurring questions
Customer satisfaction scores dropped sharply from 4.6/5 to 3.7/5
Hiring more agents to scale was financially unviable
No after-hours support coverage, leaving customers in the dark overnight
The Oleo Solution
To address these issues, Oleo engineered and deployed a comprehensive, AI-powered support automation solution tailored specifically for ProjectHub.
1. Smart Knowledge Base Deployment
Oleo built a vectorized knowledge engine using ProjectHub’s internal resources, including documentation, past tickets, and FAQs. This enabled:
Semantic search that could understand query intent, not just keywords
Instant retrieval of previously solved cases and help articles
Continuous learning from new interactions to enhance future responses
2. Multi-Channel AI Assistant
We integrated a GPT-based AI assistant across all major support channels:
In-app chat with awareness of user context (e.g., account details)
Email bot for triaging and auto-responding to common tickets
Interactive help center with real-time suggestions
Searchable customer portal with AI-enhanced accuracy
3. AI-Augmented Agent Tools
For more complex inquiries, we didn’t replace agents—we empowered them. Oleo provided:
Suggested response drafts based on the full thread context
Summaries of lengthy conversations to save review time
Relevant documentation recommendations
Sentiment analysis to flag and prioritize urgent tickets
4. End-to-End Workflow Automation
To streamline operations, we automated critical support flows:
Auto-tagging and routing of incoming tickets
Proactive alerts for known issues before users report them
Automated follow-ups for unresolved tickets
Live CSAT monitoring with real-time alerts for negative trends
Implementation Timeline
The rollout was done in structured phases over 10 weeks:
Discovery (1 week): Audit of existing support processes and integration mapping
Knowledge Optimization (2 weeks): Structuring and embedding all documentation
AI Assistant Build (2 weeks): Fine-tuning GPT models for ProjectHub’s use cases
Channel Rollout (1 week): Deployment across chat, email, and portal
Team Enablement (1 week): Training agents and tuning the AI in real time
The Results
Just three months post-launch, ProjectHub saw powerful results:
🔧 Performance Gains
73% of inquiries resolved without human input
Response time dropped from 12 hours to 4 minutes on average
Resolution time for repeat issues slashed by 86%
24/7 support coverage introduced with consistent quality
Support capacity jumped 280% with zero headcount increase
😊 Customer Experience Boost
Satisfaction scores climbed from 3.7 to 4.8 out of 5
Self-service success improved by 165%
Product usage rose 22% due to better user support
Churn caused by poor support dropped by 34%
💼 Business ROI
$380,000 in annual staffing cost savings
Agents now focus solely on high-value, complex issues
215% increase in overall team productivity
$920,000+ in revenue protected through churn reduction
Total ROI of 430% in year one
Why It Worked
Several key drivers made this implementation a standout success:
Contextual AI: The assistant had deep knowledge of ProjectHub’s product and users
Human-AI synergy: AI amplified agent productivity rather than replacing them
Continuous learning: The system improved from every interaction
Unified experience: Customers got consistent, high-quality help across all channels
Data-driven iteration: Regular reviews refined the system over time
Final Thoughts
ProjectHub’s partnership with Oleo demonstrates what’s possible when modern AI tools are applied thoughtfully. By combining intelligent automation with human expertise, they delivered exceptional support, improved customer experience, and significantly reduced costs.
