A fine-tuned model rates SaaS hero taglines 0–100 — entirely client-side. No server, no API key, no data leaves your tab.

Most SaaS taglines are forgettable category labels — "the all-in-one platform for modern teams." I wanted something that could tell a sharp, ownable hero line from buzzword filler. This page is that model, running on your CPU in the browser.

What even is a tagline?

The one line under a company's name — the hero line at the top of a landing page. A good one is a tight, ownable promise you couldn't paste on a competitor's site ("Wake up an expert."). A bad one just names the category or stacks buzzwords ("an all-in-one platform for teams"). The model scores exactly that difference — try it below.

How the rating works

It's intfloat/e5-large-v2 fine-tuned end-to-end with a pairwise ranking loss on a few hundred rubric-rated SaaS taglines plus hand-picked hard negatives (category labels, buzzword soup, body-text fragments, prompt/role leakage). Held-out pairwise accuracy ≈ 0.90 — fine-tuning the whole embedding body, not just a frozen linear head, is what broke past the ~0.80 ceiling. It's exported to int8 ONNX (~336 MB) and run by transformers.js on the WASM backend. You type a line → it's embedded (mean-pooled, L2-normalized) → a one-row linear head turns the 1024-dim vector into a score → mapped to a percentile against the training set. Downloaded once, then fully offline.

The brainstorm half (optional)

Rating is the easy part. Generating great taglines is hard — and the real lesson was that you can't cheaply train a small model to write great ones: great taglines are a taste / creative-leap problem, not a capacity problem. So instead of a generator, the optional brainstorm connects your own OpenRouter key via PKCE OAuth (100% client-side — no app registration, no backend) and lets a reasoning model brainstorm. Paste a URL and OpenRouter's agentic web tools fetch and read the page server-side. As the model thinks, candidate lines are mined from the stream and rated live by the local classifier. Cloud brainstorm, local curation — no backend on either side.

Why this shape

The classifier can't manufacture a great line, but it reliably clears the floor: it buries imperative formulas ("Stop X. Start Y."), bare category labels, and literal feature-lists, and floats the punchy, differentiated ones. A good-enough critic plus a strong brainstormer beats trying to train a great generator. Model and data are open on Hugging Face: standd/tagline-quality-e5-ranker.

Want to see the space it learned? Explore 100 real SaaS taglines mapped out — color = rating, shape = industry. They fall into three clean quality tiers regardless of vertical: the model reorganized its whole embedding space by how good a tagline is, not what the company does.