The problem
What was broken before AI
Galileo’s enterprise customers ask detailed implementation questions that go beyond public documentation. They need to understand how services interact, how deployment choices cascade, and how the product behaves in their specific environment. Without AI, Al had to manually search docs, ask engineers, and piece together answers from scattered systems. That slowed down response time and made customer support harder to scale.
What changed
What the use case made possible
Al created a workflow where Claude Code can search across Galileo’s 15 repositories, Confluence docs, and customer-specific notes to generate a tailored answer or deployment plan. For reactive Slack support, Pylon helps turn a solved customer thread into a reusable article. The workflow turns support from a one-off answer into a knowledge loop: answer the customer, review the result, then capture the knowledge for the next customer.
Why this matters
Why this use case is worth studying
Al’s workflow shows how much customer support depends on context that never fully makes it into the docs. The real answer might be in the codebase, an internal note, a past Slack thread, or a detail about how one customer is set up. By giving AI access to those sources, Al can get to a better first answer faster — and when a question is solved, it becomes something the next customer can benefit from too.
Use this when
When this pattern applies
Use this pattern when customers keep asking questions that public docs cannot answer on their own. It works especially well for technical products where the real answer may live across code, deployment notes, past support threads, customer constraints, and implementation details that only a few people on the team fully understand.
