How we research, rank and match, with the math shown

The short version

• Who's in itAdvisors we can back with real evidence: an in-depth analysis, or an AI-assisted research dossier built from public sources. 242 published so far, from the 2,388 profiles we track on Kasamba, Keen and Purple Garden.
• What sets the orderPlatform popularity: completed readings, from a snapshot of the platforms' own public figures. Advisors without that data come after, ordered by composite score.
• What the score isA 0–10 composite: 60% smoothed rating, 20% reading volume, 20% profile freshness. It's shown on every profile, broken into its parts.
• How the quiz matchesIt screens on topic and channel, adds up fit points for style, budget, orientation and experience, then shows three advisors and the reasons. It doesn't use popularity, and it never shows a made-up “match %”.
• What money changesNothing in the order or the matches. Platform links may earn us a commission, and neither the ranking code nor the quiz has an input for it.

Sources & evidence tiers

Each source is used only for what it can actually support. Where two sources disagree, the rule for which one wins is written down here rather than decided case by case.

AI-assisted research

Here's the coverage problem plainly. Our snapshot alone tracks 2,388 advisor profiles on two platforms, and the wider market runs to dozens of platforms. Firsthand, paid testing is the gold standard, and the in-depth analyses we quote are built that way, but no organisation or individual can do it for every advisor. Without another method, most advisors would have no usable guidance at all.

So for advisors without an in-depth analysis, we use AI to do what a diligent researcher would do, at a scale no researcher could. We've spent thousands of US dollars on model usage to collect what's publicly known about each advisor from across the web, and to condense it into the same structured profile you see everywhere on the site. The goal is narrow and practical: help you avoid poor fits and find an advisor who suits your situation before you pay for a reading.

What we collect for each advisor

Protocol

1. Retrieve first, then write. The model works only from documents retrieved for that specific advisor, not from what it “remembers”. Grounding generation in retrieved sources is the standard mitigation for fabricated content^{[9, 10]}.
2. Extract into a fixed schema. Specialties, communication style, pricing and offers, recurring praise, recurring complaints and red flags. These are the same fields for every advisor, so profiles can be compared.
3. Platform numbers win. Ratings, reading counts and prices are taken from the platform profile, never from third-party text. See “When sources disagree”.
4. Patterns, not anecdotes. A strength or complaint goes into a profile only when it recurs across clients or sources. One-off stories are left out.
5. Editorial check before publishing. The editorial team reviews each dossier for unsupported claims, tone and safety red flags before it goes live.
6. Refresh and correct. Dossiers carry a compilation date. They're refreshed when the evidence changes and corrected under the corrections policy.

What AI is never used for

• Writing reviews, ratings or testimonials, which US law prohibits when fake or AI-generated^[12]
• Inventing sessions, firsthand experiences or quotes
• Deciding rankings or quiz matches, which are computed by the rules on this page
• Judging whether an advisor is “accurate”

We disclose this because readers deserve to know how content was made. That's the “Who, How and Why” test Google applies to content quality^{[7, 8]}, and the transparency and accountability properties the NIST AI Risk Management Framework sets out for trustworthy AI^[11].

Listing order

Every list on the site (home, the full directory, each topic and each platform page) uses the same order:

Why popularity first, not rating

Ratings on review platforms are compressed toward the top of the scale, a pattern documented across marketplaces^{[2, 3, 4]}. It holds here too: of 2,159 rated profiles in our snapshot, 57% sit at 4.7★ or higher, so rating alone barely separates anyone. Completed readings are paid sessions that clients chose to start. Counted across 15.4 million sessions, that's the hardest public signal to fake.

The live adjustment only re-orders the page in your browser. Rank numbers always refer to the published order. If live status can't be loaded, you see the published order unchanged.

The composite score

Every advisor gets one score from 0 to 10. It orders advisors without popularity data, breaks ties, and appears on every profile with its parts shown.

Why the rating is smoothed

Without smoothing, a new profile with three 5★ sessions would outrank a veteran with 40,000 sessions at 4.9★. Here's what the real function returns for both:

Missing inputs

Worked examples from the live directory

These advisors are chosen by rule, not by hand: the most-read profile, the smallest known sample, and the first profile with no stated reading count. Freshness is measured on the build date (2026-10-02).

* Neutral value: reading count not stated on the platform profile.

Run the formula yourself

This calculator runs the same function the site uses to score advisors, with the current directory constants.

Topic tagging

The “Best for” rankings and the quiz's topic screen are built from topic tags. Tags come from a fixed keyword classifier, not from hand-picking. It reads each profile's title, verdict and “best for” note and counts keyword hits per topic. Keywords match at the start of a word, so “reconcil” also catches “reconciliation”. It keeps up to 3 topics, ordered by hits, and the first is the primary topic. Profiles with no hits at all (39 today) are filed under Spiritual guidance by default.

An advisor can carry up to 3 topics, so the column adds up to more than 242. Within a topic page, the order is the listing order above.

The match engine

The lists answer “who is most in demand?”. The quiz answers a different question: “who fits what I just told you?”. This section specifies the model completely. Anyone with the dataset could reproduce every match the site makes.

8.1Design goals

• Fit, not fame. Popularity and the composite score are left out of the model, so that a less-booked advisor who fits you can beat a famous one who doesn't.
• Fully specified and deterministic. The same answers always give the same three advisors. No learned black box, no randomness, no paid boosts.
• Honest output. Three advisors, each with the reasons they were chosen. Never a fabricated “93% match”.
• Graceful with sparse data. Missing or ambiguous attributes get neutral or reduced credit, never a penalty that would quietly exclude someone.

8.2Model

The quiz asks six questions with 8 × 4 × 5 × 4 × 3 × 3 = 5,760 possible answer combinations. Following the consider-then-choose account of consumer choice, in which people first screen options on must-haves and then trade off the rest^{[16, 17]}, the engine works in two stages.

Stage 1 is non-compensatory: an advisor who doesn't cover your topic can't make up for it with a great price. This is elimination by aspects^[15], applied only to the two answers that describe what you need. Stage 2 is compensatory: a weighted additive value model^[14] in which a weaker fit on one attribute can be offset by a stronger fit on another. On average the screen narrows 242 advisors to a consideration set of 42.

8.3Advisor attributes

Each advisor is represented by attributes derived from the profile text and platform facts by fixed rules. No attribute is hand-assigned.

27 advisors have no detectable style and 61 are balanced in orientation. Both are treated as neutral, not as negatives (see 8.4).

8.4Part-worths

Each question contributes one term s_k. The values below are the complete scoring table, nothing omitted. A response that expresses no preference (“no preference”, “a bit of both”, “a few readings”) gives every advisor the same value, so it can't tilt the result.

Two design choices deserve a reason. Style is weighted second only to topic because, in one-to-one advisory work, matching how a practitioner communicates to what a client prefers is associated with fewer drop-outs and modestly better outcomes. That's the conclusion of two meta-analyses of preference accommodation in counselling^{[22, 23]}. We borrow the principle, not the clinical claim: a psychic reading isn't therapy. First-timers are steered to free minutes, high ratings and lower prices because trial offers and track record are among the risk-reduction cues consumers rely on most when buying something unfamiliar^[24].

8.5Why the weights are set, not learned

We have no outcome data to fit weights to. We don't track who booked whom or how a reading went, and the privacy policy commits us to that. Learning weights from clicks would also teach the model popularity, which is exactly what it's meant to exclude. The weights are therefore set by editorial judgement. Decades of decision research find that such “improper” linear models, with sensible signs and rough magnitudes, predict nearly as well as statistically optimal weights and are more robust out of sample^{[20, 21]}.

A nominal weight (the most points a term can give) isn't the same as real influence. What matters is how much a term varies among the advisors still in contention. The audit measures both:

Effective weight is the average spread (max − min) a term produces inside the consideration set, as a share of the total spread across all terms, averaged over every answer combination. Channel's effective weight is 0% by construction: once the screen has kept only advisors who offer your channel, the channel term is identical for all of them. Channel does its work entirely in stage 1. Topic falls from 33% nominal to 22% effective for the same reason, and style becomes the strongest discriminator among on-topic advisors.

8.6Relaxation

A hard screen can leave nothing to recommend, a known failure mode of constraint-based recommenders^{[18, 19]}. Our rule: if no advisor matches the topic, the topic screen is dropped. If some do, only they stay. The channel screen works the same way within whatever is left. Every relaxation is shown to you on the results page. In the current dataset it happens in 0% of answer combinations.

8.7Ties

Part-worths take a small number of discrete values, so totals can tie. Ties are broken by platform rating, then by profile ID. The ID is arbitrary, but it keeps results deterministic. In 31% of answer combinations, the advisors in 3rd and 4th place have equal utility, so the tie-break decides who makes the cut. We report this rather than hide it (see section 09).

8.8Explanations, and why three

Each result shows short reasons, such as “Specializes in career & money” or “$4.99/min”. Every reason maps to a stored attribute. Explanations make recommendations easier to trust and to check^{[25, 26]}, and they let you spot a bad match yourself. We show three results because long choice sets can discourage a decision^[27]. The meta-analytic evidence on choice overload is mixed and depends on context^{[28, 29]}, so three is a judgement: enough to compare, few enough to decide.

8.9Worked example

Answers: Career & money · Voice call · $3 – $6 / min · Direct & honest · Direction & clarity · This is my first. The screen keeps 67 of 242 advisors. Here are the top five by utility, as computed by the production engine:

Two properties show up here. The model is compensatory: an advisor whose primary topic matches can lose to one who covers it as a secondary topic but fits better on price, style and orientation.

Match engine audit

The quiz has a finite answer space, so we don't sample it. At every build we run the production engine on all 5,760 answer combinations against the live dataset and publish the results. Metrics follow the recommender-systems evaluation literature, using coverage and concentration rather than accuracy alone^{[30, 6]}.

Does every question matter?

A question that never changes the outcome is decoration. We test each one by one-at-a-time sensitivity analysis^[31]: for every answer combination, change just that answer to each alternative and check whether the recommendation changes.

Exposure across platforms

Purple Garden receives 56% of recommendations against a 33% catalog share. Most of the gap is structural: video is offered only on Purple Garden, so every “Video” answer can only return Purple Garden advisors. Excluding those answers, its share falls to 41%. Keen is under-exposed (14% vs 20%), which follows from its higher median price scoring lower on budget fit. Neither effect involves commercial terms, and both come straight from the published part-worths.

Who writes what

Every element of a profile has one accountable source:

Money & independence

Links to reading platforms may be affiliate links: if you sign up through one, we may earn a commission at no cost to you. We disclose this on every page that carries such links, as the FTC's Endorsement Guides require^[13]. The safeguard is structural. These are the only inputs to the order, and none of them is commercial:

• No paid placements, “featured” slots or sponsored rankings
• No commission rate or deal in the ranking code or the quiz
• No platform or advisor sees or approves a page before it's published
• No listing removed, hidden or re-ordered for payment
• No free readings accepted in exchange for coverage
• No invented reviews, testimonials or sessions

Every “View on …” link to a platform is marked rel="sponsored nofollow". Funding is explained on the About page.

What the labels mean

Hot #N overall

The advisor's position among all 2,388 snapshot profiles by completed readings (2026-09-02). It's the platforms' numbers, not ours.

Verified N days ago

When the profile's evidence was last checked against the live platform profile. It also drives the freshness part of the score.

Online now · Busy

From our latest status scan, up to about 35 minutes old.

Composite score

The 0–10 score from section 05, with its three parts shown on each profile.

Platform rating · Readings

The platform's own figures. We display them unadjusted; only the score smooths them.

Listed price

The first per-minute price in the profile's pricing notes, usually the chat rate. Platforms change prices, so the checkout price is the one that counts.

In-depth editorial review

Link to the full Eastern Alignment analysis a profile is built on.

Limits of this method

• Nobody can measure whether a reading is accurate. Vague statements feel personally accurate to almost anyone^[33]. We measure track record, fit and data freshness, not psychic accuracy.
• We don't audit platform figures. Ratings and reading counts are what the platforms publish, and they're inflated in the way review platforms generally are^[2].
• The snapshot is a single moment. The order reflects 2026-09-02 until the next capture. Keen isn't in it; compare Keen advisors on Best on Keen.
• Popularity and volume favour incumbents. New advisors start with less evidence, the cold-start problem^[32], and popularity compounds^[5]. The quiz excludes popularity partly to counter this.
• Text-derived attributes are proxies. Topic, style and orientation describe how an advisor is written about, which isn't the same as how they read for you.
• AI research can be wrong. Language models can produce fluent errors^[10]. Grounding, schema extraction and editorial review reduce this but can't eliminate it. Report anything wrong and we'll fix it.
• Quiz weights are unvalidated against outcomes. See 8.5. Experience barely moves results, and nearly a third of cut-offs are tie-breaks. Both are candidates for the next revision, and any change will be logged below.
• Coverage is partial. Not being listed isn't a verdict.

Changelog

Every material change to the method is logged, including corrections to our own mistakes. Wording edits aren't logged.

1. October 3, 2026v1.2
   • AI-assisted research dossiers added as a second evidence tier, for advisors without an in-depth analysis. They're compiled from public sources under the published research protocol.
   • Every advisor profile now states which evidence tier it rests on.
2. October 3, 2026v1.1
   • Methodology published as its own page. Every number on it is computed from the live dataset by the same code that orders the lists and runs the quiz.
   • Match engine documented in full (model, attribute derivation, part-worths, relaxation, explanations) with an exhaustive audit over every possible answer combination.
   • Fix: the topic classifier counted the words “he” and “she” as Love & Relationships keywords, and because keywords match at the start of a word, also “help”, “her”, “heart” and similar. Both were removed. Love & Relationships fell from 234 to 183 profiles and topic tags changed on 78 profiles. Advisors with no topic keyword at all are now filed under Spiritual Guidance by default.
   • Correction: earlier pages said ranking lists apply a “platform-diversity rule” (no platform more than 2 positions in a row). No listing applied it. The claim is withdrawn.
   • Correction: earlier pages said the composite score powers the match quiz. It does not. The quiz uses its own published utility model.
   • Clarified that volume is measured in completed readings (as reported by the platform), not written reviews.
3. October 2, 2026v1.0
   • Launch. Lists ordered by platform popularity from the 2026-09-02 snapshot; composite score (60 / 20 / 20) used for advisors without popularity data and shown on every profile.
   • Live online status added: online advisors are moved to the top of a list in your browser.

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