We built classifaily because the hardest part of putting AI into a workflow isn't the AI - it's getting a clean, structured answer back fast enough to act on.
Automation workflows are only as smart as the decisions inside them. A Zap, an n8n flow, or a Make scenario needs to know: is this spam? Which department does this belong to? How urgent is it? Today, developers either skip that intelligence entirely or stitch together brittle prompt chains to get there.
classifaily is the missing primitive - a single API call that returns a confident, structured classification, so your workflow can branch on it immediately. No prompt engineering. No model management. No guesswork.
Nick, classifaily's founder, spent years building and running AI-powered automation workflows. The use cases ranged from lead scoring to content pipelines - but the same problem kept surfacing: how do you take a blob of unstructured text and turn it into a clean, branchable decision inside a workflow?
The friction hit hardest with NC Bourbon Insider - an allocated bourbon alert app Nick built to notify users the moment rare bottles hit store shelves. The app processed raw, unstructured data from dozens of sources: store sites, social posts, distributor emails. Every piece of content had to be classified - is this an actual allocation drop or noise? Which product? Which region? How urgent is it?
Stitching together ad-hoc prompt chains and regex filters worked - barely. But the classification layer, the part that turned messy input into a structured decision the rest of the workflow could act on, was the one piece that held up. It was reliable, fast, and composable in a way nothing else was.
That observation stuck. If the classification layer was the most durable part of every workflow Nick built, there was a good chance other builders were solving the same problem from scratch, over and over. classifaily is that layer - extracted, hardened, and offered as a single API call so no one else has to reinvent it.
"My team told me to give a quote for this section, but I'm not really a quote guy. I built classifaily to solve my problem, and I hope it solves yours as well. P.S. I wish I picked a different name."
- Nick, Founder
Allocated bourbon alert app - the real-world workflow that proved structured classification was the hardest, most valuable layer to get right.
Every response is a structured JSON object with a label, confidence score, and optional reasoning. No prose to parse. No unpredictable output formats to handle.
Built to slot into n8n, Zapier, Make, and Pipedream from day one. Webhook-ready and designed so that a non-technical user can wire it up in minutes.
We don't store your content. Classification inputs aren't logged beyond what's needed for abuse detection and are never used to train models.
We surface confidence scores and tell you when the model is uncertain. Use that signal to build fallback paths instead of blindly trusting every output.
Teams use classifaily wherever they need a fast, reliable label to route, filter, or act on content.
Score every inbound form submission before it hits your CRM or support queue. Route spam to a review bucket, real leads straight to the pipeline.
Classify the intent and urgency of incoming support tickets so they land in front of the right team immediately - no human triage required.
Score inbound leads by intent signal and route high-intent contacts to sales instantly, while nurturing the rest through automation.
Flag user-generated content that violates your policies before it's published. Works on text, images, and audio in a single unified API.
Detect intent, urgency, and sentiment in incoming emails and automatically prioritize, tag, and assign them in your inbox or help desk.
Classify survey responses, reviews, and NPS verbatims into themes and sentiment so product teams can act without manual tagging.
Free plan available. No credit card required.
