Why Chatching
Most AI assistants are built as experiments. We help you build them as engines.
Why AI assistants fail after launch
The excitement of the first demo often masks structural weaknesses. Assistants fail when they aren't integrated into core workflows, when their performance is unpredictable, and when teams lack the data to know why a user stopped using them.
"A chatbot is a feature. An assistant is a service. Most companies ship features and wonder why they don't get service-level retention."
Why generic chat analytics fall short
Standard tools show you what users said. They don't show you what users meant or if the assistant actually helped. You need product-aware analytics that connect the chat thread to the database state and the user's eventual outcome.
Generic tools measure engagement. We measure fulfillment.
Why assistants must be treated as product surfaces
An AI assistant isn't a separate layer on top of your app; it's a primary way users interact with your value proposition. It requires the same rigor in design, testing, and measurement as your core dashboard or API.
Treat your assistant with the same engineering discipline as your core infrastructure.
Our Principles
Measure behavior, not vibes
Stop relying on sentiment analysis. Track what users actually do after interacting with your AI.
Optimize for outcomes, not tokens
Efficiency matters, but impact matters more. We prioritize the result over the raw processing cost.
Ship safely, improve continuously
Robust eval sets and regression testing allow you to iterate on your assistant without fear.
Ready to turn your assistant into a growth engine?
Let's talk about how we can help you build an AI assistant that drives real business outcomes.
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