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deepmind.google·6d ago
AI Engineering
OpenAI Blog·Sep 29·5 min read
Improving support with every interaction at OpenAI
TL;DR
OpenAI's support team faced scaling challenges as user demand grew faster than human capacity to respond. OpenAI built AI-assisted support tools that augment human agents by automating triage, drafting responses, and suggesting solutions in real time.
✦ Why It Matters
Engineers can adopt AI-assisted workflows to scale support operations without hiring proportionally, improving both speed and consistency.
Key Takeaways
- Integrate language models into support workflows to augment agents, not replace them.
- Automate ticket classification and routing to reduce manual triage overhead.
- Generate response drafts and surface relevant documentation to accelerate agent productivity.
- Measure impact on response time, resolution rate, and cost-per-ticket to validate scaling efficiency.
Full Summary
As OpenAI's user base expanded rapidly, the support team struggled to maintain response quality and speed with traditional human-only workflows. OpenAI developed AI-powered support infrastructure that integrates language models into the support pipeline to assist human agents rather than replace them.
The system performs ticket classification and routing, generates response drafts based on common issues, and surfaces relevant documentation and previous solutions. By embedding AI assistance directly into agent workflows, OpenAI reduced mean response time and improved first-contact resolution rates.
The approach enabled the support team to handle significantly higher ticket volume without proportional growth in headcount, demonstrating how AI can augment human expertise in customer-facing operations. This model scales support quality during periods of rapid user growth by making each agent more productive and consistent.