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2 minute read
In the era of automation and AI-driven operations, the ability to make real-time decisions is no longer a luxury—it's a necessity.
Why This Project Matters
At Colaberry, we challenge our interns to build systems that replicate the responsiveness and reliability expected in top-tier AI support platforms. This week’s spotlight is on our CoraEmail AI initiative, which automates support ticket resolution through intelligent email processing.
Featured Alumnus Testimonial: Ready for Real-Time
"The Colaberry internship truly prepares you for the field. When I started my job as a Qlik Developer, they threw me around the fire right away. But honestly, the experience was very similar to the internship, which is right, it's what the internship really teaches you what you will experience in the real world scenario.
That intensive training ensures that even when dealing with complex, real-world data solutions—like managing projects with daily stand-ups and coupling meetings throughout the day—I was able to cope and actually accomplish what I needed to do in a job. It’s all the stuff that I'm able to look at and try to figure out; it's stuff I've used before. The entire process is designed to mirror the real work environment."
— Josiah, Qlik Developer
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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
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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
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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!
