Gladly Pricing Overview

Managing customer tickets is still straightforward. I love that we can write internal notes, tag teammates for specific tasks, and seamlessly assign tickets to the right team members. Plus, the ability to tag myself for breaks or when I’m away from the screen makes workflow management even smoother. Overall, Gladly has made handling customer interactions much more efficient! Review collected by and hosted on G2.com.

If there’s one thing I find challenging about Gladly, it’s that when the inbox is full, there can sometimes be a lag in fully loading and displaying all tickets. This can slow down workflow slightly, especially when trying to navigate through multiple conversations efficiently. That said, overall, I still find Gladly to be a user-friendly and effective platform! Review collected by and hosted on G2.com.

As a Machine Learning Consultant, I have come across many organizations that face challenges due to "Ticket" based systems with data in silos. The best part I like about them is their “People-centered” model. You do not have a ticket number; you just have a lifetime conversation thread about that customer. This is very much helpful from a data perspective because the context window of the AI is always clear. The Gladly Sidekick (AI) is extremely powerful. This is not a simple chatbot, it employs the power of Generative AI and RAG (Retrieval-Augmented Generation) to read our "Guides" and provide very human-like responses. You have a great ML logic for your feature where "People Match"—it routes the customer to only the best agent based on their query and loyalty status, not just who’s free. The integration with Shopify and other backends is also great, plus the AI can actually handle tasks like refund processing or package tracking rather than only "deflect" the user. Review collected by and hosted on G2.com.

One thing that I do not like is the "Rules" engine can be a little hard to configure if you have more advanced workflows. If you are trying to do very advanced branching, the UI feels somewhat limited relative to a full-fledged flow builder. Finally, while the reporting dashboard provides basic KPIs, if I am looking for deep machine level analysis or sentiment trend mapping, I tend to export out via REST API and consume in my Python notebooks. The session timeout also needs to be a little less aggressive; it occasionally logs the agents out before they finish their long, most of the time even on mid case. Review collected by and hosted on G2.com.