Issue No. 14 · Vol. I

Changelog

What we shipped, when, and why. Editorial notes on every release — including the small ones, the embarrassing rollbacks, and the times we changed our minds about pricing.

Filter §
12 entries · Last 6 months
Pricing v0.14.0

We killed the $4.99 report. The new floor is $49.

After a CEO-mode review and a hard look at HubSpot's free AEO grader, we accepted that $4.99 was anchoring us as a cheap impulse buy. The new $49 tier is the qualified-lead generator we needed — and the email at checkout finally has somewhere to go.

The old $4.99 unlock was a hangover from the public-beta era. It got people in the door, but it framed the product as the cheap version of HubSpot — which is free. We were anchoring against the wrong reference.

What changed

  • Killed the $4.99 Stripe product. Existing $4.99 buyers grandfathered for life.
  • New $49 AI Readiness Report — full consensus matrix, PDF, OG share card, competitor consensus, severity ranking.
  • Free scan now shows enough to create urgency without satisfying it: AI Readiness Score, SEO-for-AI checklist, blurred consensus preview.
  • $199 Full Accuracy Report adds per-claim accuracy scoring against verified site data and a correction playbook.
"$49 isn't really about the $49. It's about the email and the trust the email represents — that converts to $399/mo through the drip, no sales call."

The full pricing matrix is documented in /pricing.html if you want to see how the tiers ladder.

Monitoring Product v0.13.0

Monitor and Monitor Pro are live. Re-scans now run on a schedule.

The first subscription tier shipped. Six flat-price Stripe SKUs, an hourly cron, a DriftDetectionWorkflow, and email alerts on every score shift of 5 or more.

Monitor ($399/mo) and Monitor Pro ($599/mo) both went live this morning. The infrastructure was the harder part: we moved scans into Cloudflare Workflows, added per-subscription nextScanAt bookkeeping, and built a separate drift-detection workflow that compares the latest scan to the prior one.

  • Monthly re-scans (Monitor) and weekly re-scans (Pro), same brand each time.
  • Email alerts when accuracy score moves by ≥ 5 points or a previously verified claim flips.
  • Trend dashboard plots scores over time, per model and overall.
  • AI Readiness Certification Badge — cached at CDN edge with 1-hour TTL.
misquoted.ai/dashboard/trend
Screenshot · Trend dashboard
Accuracy score, last 12 weeks
Per-model trend lines with annotation markers on model-release events.
Product Design v0.12.0

Consensus scoring report — the editorial redesign.

Reports now read like an investigation, not a dashboard. Grouped claim cards, color-coded verdicts, an executive summary up top, and a model-by-model breakdown that finally fits on one page.

The old report was a flat table. Everyone scrolled to the bottom for the score and ignored the rest. The new layout puts the score in a hero, surfaces the three most-disagreed-on claims first, and groups everything else by topic.

  • Lead story: composite score + the one sentence that says why.
  • Three-up: most-inaccurate, most-disagreed-on, most-recently-flipped.
  • Grouped claim cards by dimension (positioning, pricing, founder bio, product list).
  • Per-model reliability sidebar — at a glance, which model knows you best.
62
Sample · Hero score block
AI Readiness Score · 62 / 100
"Mixed accuracy. Models agree on positioning, disagree on pricing and founder bio."
Product v0.11.0

AI Readiness Check — the free scan, now actually useful.

The free tier got a real product. SEO-for-AI checklist, Google AI Overview presence check, and copy-paste code for the two files you'll wish you'd had: llms.txt and JSON-LD.

The free scan needed to deliver enough value to earn trust without satisfying the curiosity that drives the $49 purchase. We landed on five things: a composite readiness score, a pass/fail checklist, an AI Overview check, two copy-paste quick wins, and a blurred preview of the consensus matrix.

llms.txt · auto-generatedcopy →
# llms.txt — generated by misquoted.ai # Audited 2026-04-08, ChatGPT/Gemini/Perplexity User-agent: * Allow: / Sitemap: https://example.com/sitemap.xml ## About Example Co. is a B2B analytics platform founded 2019 in Brooklyn, NY. CEO: Sarah Chen.

Everyone gets the file. The conversion play is the score below it.

Product v0.10.0

Browser preview screenshots on every scan.

Reports now ship with a real browser screenshot of your site as the scanner saw it — so you know exactly what AI models had to work with.

One of the most common questions in support was "why does the AI think we're a B2C company?" Almost always, the answer was hidden in the rendered DOM — a stale meta tag, a JavaScript-rendered hero the scanner missed, or a robots block.

Every scan now captures the page exactly as the headless browser rendered it: the full DOM, a hero-viewport screenshot, and a full-page screenshot. They show up inline in the report and you can download them.

misquoted.ai/r/abc-123/snapshot
Screenshot · Browser snapshot view
Captured exactly as Perplexity saw it
Hero viewport + full-page render, downloadable PNG.
Bug fix v0.9.3

Four QA findings, four fixes.

Markdown rendering on report pages, a React hydration mismatch on the scan stepper, an aria-label that read "Run scan" instead of "Run free scan," and copy that said "4 models queried" when we only query 3.

  • Markdown — claim descriptions now render proper line breaks and emphasis in the consensus matrix.
  • Hydration — the scan progress stepper no longer flashes a server/client mismatch on slow connections.
  • Accessibility — corrected the aria-label on the homepage scan form.
  • Copy — landing page now says "3 models queried" (ChatGPT, Gemini, Perplexity). Claude is on the roadmap, not in production.

None of these were urgent. All of them are the kind of thing that signals "we don't pay attention" if left untouched — so we touched them.

Product v0.9.0

Web-search tool calls are now tracked per model.

When a model decides to look something up live instead of relying on training data, we record that — and show it next to the answer in the consensus matrix.

ChatGPT, Gemini, and Perplexity each have web-search tool access. They use it inconsistently. Sometimes the disagreement we're surfacing isn't a training-data difference — it's that one model searched live and got a fresh answer while another didn't.

The consensus matrix now annotates each answer with a small mono badge: web ↗ if the model called search, blank if it answered from training. This changes how you read disagreement: a "training-vs-search" split is different from a "the models disagree about reality" split.

API v0.8.2

Public badge API — embed your AI Readiness score.

A signed, CDN-cached endpoint that returns an SVG badge for your current AI Readiness Score. Drop it on your status page, your homepage, your investor deck.

Subscribers can now embed their live score as a badge — Monitor tier and above. The endpoint is cached at the CDN edge with a 1-hour TTL, so it costs us nothing per request and stays current without polling.

GET /api/v1/badge/:brand.svg200 OK
# Embed: <img src="https://misquoted.ai/badge/example-co.svg" alt="AI Readiness Score" /> # Response (SVG with score, color, last-scanned date): cache-control: public, max-age=3600 content-type: image/svg+xml
Pricing v0.8.0

Multi-brand bundles, priced flat.

Holdcos and agencies kept asking for "Marvel + Pixar + ESPN in one subscription." The 3-brand and 10-brand bundles answer that question with flat Stripe pricing and in-app brand assignment.

  • 3 brands — $899/mo (Monitor) or $1,399/mo (Pro). ~25% per-brand savings.
  • 10 brands — $1,999/mo or $2,999/mo. ~50% per-brand savings.
  • 20+ brands — custom, contact sales until the demand pattern stabilizes.

Each bundle is a single Stripe subscription. Brand assignment is managed in-app, not in Stripe — so swapping a brand mid-cycle doesn't require a billing change.

Monitoring v0.7.0

Continuous site crawling, on the same cron as the model scans.

The ground-truth baseline for the $199 report and for Monitor accuracy scoring needs to stay current. We now re-crawl alongside every scheduled re-scan, so accuracy is measured against today's site, not last quarter's.

Drift can go either direction: the AI gets it wrong, or the site changed and the AI is still right about the old version. We now distinguish those cases by re-crawling on the same schedule as the model queries. The $199 report uses a 14-day freshness window; Monitor and Pro keep a rolling baseline.

Bug fix v0.6.4

Fixed: scan batches occasionally orphaned at "running" forever.

A PlanetScale connection-pool timeout was silently failing on long-running queries, leaving scan batches stuck in running with no error. They now fail loud and retry.

Symptom: scan starts, three model calls succeed, the batch sits at "Scoring consensus" forever. Root cause: the consensus aggregation query took longer than the pool's idle timeout. The connection dropped, the query silently returned null, and the workflow had no fallback.

Now: explicit query timeouts, structured error logging, automatic retry on the workflow step, and the batch shows "Failed — retrying" instead of pretending to work.

Product v0.5.0

misquoted.ai — public beta.

First public release. Three models (ChatGPT, Gemini, Perplexity), one consensus score, one report, one $4.99 unlock. The product had a long way to go. The premise didn't.

The first version of misquoted asked a simple question: when three different AI models answer the same question about your brand, do they agree? It turned out the answer was usually "no" — and that the disagreement was where the interesting story lived.

"Competitors measure visibility. We measure accuracy and consensus. Different product."

Everything since has been about turning that one observation into a product people will pay for. Some of those bets worked, some didn't, and this changelog tries to be honest about both.