Why CustomerEagle exists
Why we built CustomerEagle, what we believe support software should do, and how that shapes the product.
Real product views · sample data
The problem we kept running into
Support teams spend most of the day answering the same handful of questions, written slightly differently every time. It isn't hard work; it's relentless work, and it crowds out the conversations where a person genuinely helps. Then the first wave of AI support tools arrived and, in one specific way, made it worse: a bot that sounds confident about a policy you never wrote costs more than no bot at all, because somebody has to clean up after it.
What we believe
AI in support should be grounded in your own knowledge base rather than in whatever a general model absorbed from the internet. It should show where an answer came from, so your customer — and your team — can check it. It should hand over to a person the moment it is out of its depth, instead of improvising. And it should be measured by a definition published before the invoice, not after.
How that shapes the product
Each of those beliefs is a decision you can point at in the product.
Answers are drawn only from content you approved, each one cited to its source.
When confidence is low, the thread goes to your team with the history summarised, instead of improvising.
Anything that writes — a refund, an order edit — is prepared and then waits for a human to approve it.
Paid plans combine a seat price with included AI resolutions. Usage above that allowance is metered, and a resolution only counts when no human replied.
Hosting is primarily in the EU, and the data processing agreement is published rather than available on request.
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