For this week's AI for GTM newsletter, we sat down with Moheet Subudhi, Customer Success Manager at Mailmodo, who built a customer support AI agent named Ishaan to optimize ticket handling. |
|
How to build an AI agent for customer support |
|
Customer support is one of the most misunderstood functions. "Answering the customer's question" is what people assume is the entire job, but it's actually the output from several layers of research and back-and-forths with different teams. The CS team at Mailmodo was both struggling with ticket volume and curious about what AI could actually do for him. So instead of just asking for more headcount, Moheet, our customer success manager at Mailmodo, built an agent that does the collecting and triaging of queries and hands a ready-to-review draft to a human. He named it Ishaan. In this edition, we'll break down exactly how he built it so you can set up something similar for your team. |
|
The problem we were solving |
|
Our CS team was spending a significant chunk of time on tickets that all followed the exact same pattern: - A customer asks a question
- A CSM opens Chargebee to check their plan
- Then opens Vitally to check their health score and credit usage
- Then searches the help docs for the right article
- Only then writes a reply
At scale, five steps every time will both slow down replies and make them inconsistent. |
|
The tools behind the AI agent |
|
The tool stack for this AI agent involved five existing software tools and one orchestration tool tying them together: - Chargebee provides the customer's plan, billing status, and MRR, so it has that context before it does anything else.
- Vitally adds account health score and email credit usage on top of that
- A vector search index, built from the help center, that lets it find the right article by meaning, not by matching keywords.
- Atlas is where the ticket lives and where the approved reply eventually gets posted,
- Slack is where the draft shows up first, for a CSM to check before it goes anywhere.
Tying it together is Hermes, the orchestration layer that decides which tool to call and when, built on MCP (Model Context Protocol), the standard that connects Ishaan to each of these systems without custom integration code for every single one. |
|
Here's what the build actually looks like 1. Read the ticket Ishaan starts with the customer's question exactly as they wrote it, straight from Atlas. Nothing gets shortened or rephrased at this point; it works from what the customer actually typed. 2. Pull the account context Next, it gathers the customer's plan, MRR, credit usage, renewal date, and health score from Chargebee and Vitally, along with any past tickets from that same account. 3. Find the right help article, and note where it came from Ishaan searches the help center for articles that match what the customer actually means, not just the words they used. A customer might describe a problem in completely different language than the docs do, so matching on exact wording alone would miss the article that actually answers it. Every article it pulls gets attached to the draft, so a CSM can check the source in one click instead of trusting it blind. 4. Sort the ticket by type and urgency Every ticket gets sorted into a category (a how-to, a billing issue, a feature request, or something that needs a human) and a rough sense of urgency. This is also where the line gets drawn on what it's allowed to touch. If something feels sensitive, like a refund or an angry customer, it flags it and hands it off. 5. Check its own draft before sending it forward Before the reply goes anywhere, Ishaan runs an initial check on its own draft, comparing it against the account data and the source article it pulled. This catches cases where the two don't actually line up, a mismatched plan detail, or a claim the source article doesn't actually support. 6. Post it to Slack for review The draft lands in Slack, where a CSM reads it, edits it if needed, and approves it before sending it to the customer. |
|
AI news: What's happening right now? | - GenOptima launched a platform for Generative Engine Optimization (GEO), tracking how often brands get recommended inside AI conversations across ChatGPT, Gemini, and Claude instead of static keyword rankings. GTM budgets built around SEO now have a new target: getting cited inside AI answers, not just ranking on a page.
- Brunner acquired AdSkate, an AI-powered creative analytics platform that identifies which creative elements drive engagement and ROI. Creative testing moves from reporting what happened to explaining why, giving GTM teams a way to make creative calls measurable before spend goes out.
- CrunchJunkie launched a platform tracking brand visibility across eight AI assistants and linking it to real conversions via GA4. It turns "we're mentioned in ChatGPT" into an actual pipeline number GTM teams can report on.
|
|
|
| That's all, folks! I'll see you soon with more tips and ideas. If there is something we can do better for you, please let me know by replying to this email. |
|
Until next time, Aquib CEO, Mailmodo |
|
|
|