honest fit check
a clear yes, or a clear no.
we built abracadabra for a specific shape of problem. here's how to tell if your situation fits, in 30 seconds.
yes, if any of these are true.
you have an ai agent in production
a customer support bot, onboarding assistant, research tool, or any conversational ai that real users interact with.
product teams need visibility
pms, cs, or growth teams want to understand what users experience without asking engineering to pull logs every time.
you care about user outcomes, not just uptime
knowing latency is 200ms doesn't help. you need to know if users are getting what they need and whether they're happy.
you want to act on conversation data
not just store it. search it, analyze it, share specific conversations, and use it to make product decisions.
no, if you're solving for these.
you need llm engineering tools
if your primary goal is optimizing token usage, model selection, or prompt engineering from a technical perspective, tools like langsmith or helicone are built for that.
you don't have conversation data yet
abracadabra analyzes existing conversations. if you're still building your first agent, come back when you have users interacting with it.
you only need traditional product analytics
if your ai features are click-based (not conversational), standard tools like amplitude or mixpanel are the right choice.
you need a ticketing system
we're not intercom or zendesk. we analyze conversations after they happen to find patterns and improve the experience.
sounds like a fit?
tell us about your use case. we'll show you what abracadabra can surface from your data.
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