From 58a1b57458d2e33d7a050e5dab3603e1af06f581 Mon Sep 17 00:00:00 2001 From: daniel-azoulai Date: Wed, 25 Feb 2026 09:25:16 -0800 Subject: [PATCH] Update case-study-my-askai.md --- qdrant-landing/content/blog/case-study-my-askai.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/blog/case-study-my-askai.md b/qdrant-landing/content/blog/case-study-my-askai.md index fd7fb86a1..da5248766 100644 --- a/qdrant-landing/content/blog/case-study-my-askai.md +++ b/qdrant-landing/content/blog/case-study-my-askai.md @@ -19,7 +19,7 @@ tags: - case study --- -![My AskAI overview](/blog/ase-study-my-askai/my-askai-bento-box.png) +![My AskAI overview](/blog/case-study-my-askai/my-askai-bento-box.png) [My AskAI](https://myaskai.com) built a managed platform for AI customer support agents that plug directly into existing helpdesk tools like [Intercom](https://myaskai.com/ai-agent-integration/intercom) and [Zendesk](https://myaskai.com/ai-agent-integration/zendesk-tickets). The goal was to make AI behave like a reliable coworker, not a brittle chatbot. In production, My AskAI's agents are designed to resolve a large portion of inbound support requests automatically, then hand over to a human when the agent cannot answer confidently. My AskAI positions this as [deflecting around 75 percent of support requests](https://myaskai.com/blog/my-askai-edel-optics-case-study-2026) and sustaining a resolution rate in the low to mid 70s, depending on the time window and workload mix.