The CoraEmail project pushes the boundaries of what's possible in real-time decision-making by combining AI models, vector databases, and workflow automation to deliver responses in under 60 seconds. It’s not just a technical feat—it’s a real-world application of AI that directly impacts customer experience.
Intern Journey: Tackling AI Workflows with Precision
This week, the intern team focused on implementing and optimizing the “High-Confidence Auto-Response” use case from the CoraEmail AI playbook. Their goal: enable the system to detect support-related emails, match them against historical resolved cases using OpenAI embeddings, and generate human-like responses without manual intervention.
Key Achievements:
- n8n Workflow Configuration: Interns created multi-step workflows in n8n, integrating Gmail triggers, Supabase vector queries, and OpenAI GPT-4 completions.
- Vector Similarity Matching: They implemented pgvector in Supabase, enabling the system to identify top 5 similar tickets with >0.85 similarity—critical for high-confidence responses.
- Real-Time Response Generation: Leveraging the GPT-4 API, the AI now generates stylistically accurate support emails within 45 seconds, maintaining professional tone and brand consistency.
Biggest Challenge: Confidence Scoring Accuracy
Interns initially struggled with inconsistent confidence scoring from the OpenAI model. To address this, they wrote new tests for the calculate_confidence_score() function, refined embedding similarity thresholds, and added manual review feedback from Zendesk logs.
Learning Outcome: They gained hands-on experience with AI quality assurance and understood how even minor configuration changes (like adjusting vector distance thresholds) can dramatically affect AI performance.
Impact & Future Potential
Once fully deployed, this automation will:
- Resolve 60% of support emails autonomously within 60 seconds
- Reduce human agent workload by over 50%
- Improve customer response times and satisfaction dramatically
- Continuously learn from each interaction through vector-based memory and AI feedback logging
From a business standpoint, it helps Colaberry deliver enterprise-level support without enterprise-level overhead. From a learning standpoint, interns are exposed to the full lifecycle of a production-grade AI system—from backend API integration to front-end customer impact.
This initiative aligns directly with Colaberry’s mission to empower future-ready tech professionals with real-world, high-stakes projects that go far beyond textbook exercises.
What’s Next?
Next week, the team will begin testing the “Low-Confidence Human Escalation Flow”, which ensures complex or sensitive emails are routed to the right human agents while maintaining a polished AI-generated acknowledgment. This will require additional routing logic, context summarization, and Zendesk API enhancements.
Colaberry’s Commitment to Innovation
CoraEmail AI is more than a project—it’s a proving ground for what Colaberry interns can achieve when challenged to solve problems that matter. Stay tuned as our interns continue shaping the future of AI-powered education and support systems.
Check back next week to follow their journey deeper into human-in-the-loop workflows, performance tuning, and prompt engineering!