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AI for Customer Support

Repetitive Inquiry Automation

Ask a support agent what they did today and the honest answer is often: told forty people where their order is. That work is not hard, which is exactly the problem. It is uninteresting, it is identical every time, and it consumes the same attention as the ticket that actually needed a careful human. The queue does not distinguish. A person burns out on volume of the trivial while the interesting problems wait behind it.

Skynet lets you draw a line through the queue and give one side of it to an agent. The distinction that matters is not easy versus hard — it is bounded versus unbounded. Order status has a correct answer retrievable from a system you can connect. A refund dispute has a judgment call. The agent takes the first kind, on categories you name explicitly, and never quietly expands its own remit.

How it works

step 01

Find the repeat offenders

Before automating anything, have the agent count. It clusters your last few months of tickets and tells you which questions actually dominate — usually not the ones your team would have guessed.

step 02

Automate one category

Start with a single bounded topic. The agent answers it end to end, pulling live data through connectors so the answer is real rather than generic. One category, watched closely, until you believe it.

step 03

Draw the stop line

Define where the agent must hand off — anything touching money, anything angry, anything it is not certain about. Uncertainty routes to a human with a summary. The agent does not guess to keep the deflection number up.

step 04

Widen it slowly

Review what it sent. When a category is boring for a few weeks, add the next one. This stays a decision you make from evidence, not a setting someone turned all the way up on day one.

Build it from a prompt

Name the categories, name the boundary, and it runs.

Your team’s day changes shape. The volume that used to fill it goes away, and what is left is the work that needs a person — handled by someone who has the attention left to do it well.

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