Bluerails Discovery: Methodology, Pricing, and Evaluation

This older record retains its original scope. Current buying qualifications above take precedence where terms have changed.

Kingy verdict: Bluerails Discovery is more credible than a one-shot “AI visibility score” because its published method repeats prompts and exposes uncertainty. It is still a vendor-designed measurement product, not proof that higher visibility will produce bookings or revenue.

What the product measures

The free report queries ChatGPT, Perplexity, Gemini and Claude. Bluerails says each prompt is run five times per engine, producing hundreds of observations for a company rather than treating one stochastic response as stable. The methodology defines selection rate, share of voice, citation rate and discovery gap, then combines eight KPIs using weights that vary by industry. A hotel, publisher, SaaS vendor and ecommerce store therefore do not receive an identical weighting model.

The site also checks five public machine-readability signals: llms.txt, named AI-crawler directives, Schema.org JSON-LD, a well-known MCP manifest and a sitemap. Each signal contributes equally to the readiness component. A failed fetch or non-200 response counts as absent. That is easy to understand, but it also means availability, timeouts and implementation conventions can affect a score independently of whether an assistant actually uses the signal.

Method strengths and limits

Repeated sampling and bootstrap 95% confidence intervals are a meaningful improvement over a single snapshot. The method also keeps earlier score-model versions visible rather than silently recalculating old reports. The harder questions concern prompt selection, competitor sets, geographic and language coverage, model-version drift and the mapping from mentions to attributable commercial outcomes. Bluerails cites research for its methodology, but Kingy did not rerun the queries or verify its aggregate comparisons with competing products.

Use the free report as a diagnostic baseline. Review the actual prompts, rerun a small sample manually, inspect citations and separate branded recognition from open-category discovery. If a score changes, ask whether the movement exceeds the confidence interval and whether the cited pages or conversion path changed. A site can become more machine-readable without becoming more persuasive or bookable.

Pricing and buying decision

One free report is available per domain without a card. As captured on October 8, 2026, the official pricing page lists Discovery at €199 for the first month and €49 per month subsequently. Channel Manager is an add-on at €199 per month, or €179 per month billed annually. ChatGPT Ads costs 10% of the ad budget. Full-Service starts at €1,799 per month and is marked “Coming soon.” Confirm the offer and included services before buying. The free report, paid visibility tracking, site-readiness work, booking execution and stablecoin settlement are different product stages; buyers should not infer maturity in one layer from evidence about another.

How Kingy would evaluate it

Choose ten commercially important prompts across branded, comparison and open-discovery intent. Record the exact engine, date, locale and candidate set. Compare Bluerails’ repeated results with an independent rerun, then inspect whether recommended changes alter citations and qualified referral or booking behavior over several weeks. Stop if the workflow optimizes the composite while real discovery paths remain unchanged.

Kingy reviewed the live report flow, methodology, pricing and separate payments documentation. We did not submit a customer domain, reproduce a confidence interval or validate a booking outcome.

Preserve the raw observations alongside each score so later audits can separate source changes, model drift and scoring-model revisions.

Primary sources

• Bluerails Discovery product and free-report flow
• Published methodology and metric definitions
• Current product tiers and pricing
• Separate payments-platform documentation