Supporting the world’s most AI-native companies with a 2-person team

The biggest enterprise companies on earth rely on Gumloop for their business-critical AI automations. If these companies ever face issues or need guidance, our world-class support team is immediately available to help them out, at all hours of the day. These companies know us for the quality of our small but mighty support team. Our support team has only two people.

We do it with Gumloop.

Getting real-time user intelligence at scale

To address the customer’s concerns, a support agent needs to understand the customer’s context. What have they been doing in the product?

User data enrichment: Every time a support ticket opens, a workflow automatically queries internal databases and pushes a complete user dossier directly into Pylon.
Error detection and enrichment: When an error is detected, a message is sent to an error-monitoring Slack channel.
Deep user research: For high-value customer profiles, a user research agent takes a user’s email address, performs a comprehensive search on them using Apollo, Exa, Parallel, and Firecrawl, and posts a summary to a Slack channel.

Support operations and ticket management

Gumloop’s human support agents are supported by a team of specialized AI agents.

Diagnosing user issues: An agent (named “Gummie Support”) first determines what kind of issue it’s dealing with.
Crafting helpful responses: Another agent (named “Support Captain”) formats the investigation results into friendly customer communications.
Automatic cleanup: Two other agents automatically archive email threads and Pylon tickets after issues have been resolved.
Pre-call research: An agent automatically researches the user before a support call.
Following up with customers: A workflow identifies users to follow up with every day, ensuring that the solutions provided are effective.

Automated, continual monitoring

Platform health monitoring: An agent automatically investigates error rates and monitors platform health.
Social listening and brand monitoring: An agent monitors Reddit and Twitter for relevant mentions of Gumloop and responds to customer complaints.
Daily support summary: An agent fetches the last 24 hours of support tickets and posts a formatted summary to Slack.
Support doc maintenance: A workflow analyzes the Gumloop support docs and drafts missing content.
Conversational analysis: Another workflow categorizes and analyzes every support conversation to understand trends and user sentiment over time.

Lessons learned from using Gumloop at Gumloop

Gumloop’s multi-agent support operation grew one workflow at a time, over months of continuous iteration.

Know your models: Use different models across agents for different tasks to optimize cost and capability.
Context is king: Quickly assemble a complete customer profile for each ticket.
Be proactive: Address problems before users report them, making support faster and more efficient.

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Meeting Prep Agent

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Getting real-time user intelligence at scale

Support operations and ticket management

Automated, continual monitoring

Lessons learned from using Gumloop at Gumloop