AI Tool Profile
Bluerails Discovery: Methodology, Pricing, and Evaluation
Bluerails Discovery measures how four AI assistants mention, cite and shortlist a business using repeated queries, confidence intervals and industry-specific visibility weights.
Verification & Sources
- Evidence state
- Recheck due
- Source links
- 4
- Freshness
- Needs recheck: checked July 27, 2026
- Last updated
- July 28, 2026
What this evidence state means
- Definition
- The claim was previously checked, but its review window expired or a material change may have invalidated it.
- Required provenance
- The prior evidence and check date are retained, together with the expiry or change signal that triggered recheck.
- Owner
- Kingy freshness queue owner and assigned editorial reviewer
- Freshness rule
- This is already outside its freshness rule. It must not be presented as current until reviewed against current evidence.
- Disputes and corrections
- Use “Suggest a correction” on the record. Kingy editorial reviews the cited evidence, records material corrections, and changes or removes the state when it is not supported.
Key source checks
Suggest a correction
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. The current company page lists Discovery at €119 monthly or €99 per month billed annually, Discovery+ at €299 or €249 annually, and Action at €1,999 or €1,799 annually. Settlement is marked coming soon. 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
The Kingy Brief
Follow The Kingy Brief.
One consequential launch, one pricing, limit, or shutdown change, one hands-on test, one exact prompt or Test Pack, and one try / watch / skip verdict.
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Tool Links
Launch History
Bluerails Discovery
Bluerails launched Discovery, a free per-domain report that repeats prompts across ChatGPT, Perplexity, Gemini and Claude and presents AI-visibility metrics with uncertainty ranges.
- Launch readiness
- 7.3 / 10
- Demo evidence
- Not scored yet
- Creator-story fit
- Not scored yet
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-07-27.
- Confidence and missing data
- Confidence depends on source completeness. “Not scored yet” means no reviewed value; “Needs review” means the value or score set failed validation.
- Freshness
- Recalculate after a material launch, source, demo, pricing, audience, or traction change and during the record freshness review.
- Disputes
- Use “Suggest a correction” on the record and cite the relevant evidence. Commercial relationships cannot buy or alter a score.
Repeated sampling and published confidence intervals make Discovery more useful than a one-answer snapshot. The remaining risk is outcome validity: Kingy did not reproduce…