Three proposals read the same, the prices do not match

You have three AI visibility proposals. All of them say they improve your brand’s presence in AI search, all of them include a dashboard screenshot, all of them mention ChatGPT and Perplexity. The prices are far apart.

Comparing them gets you nowhere, because you are comparing things that are not alike. One sells a dashboard. One sells a block of code. One sells someone doing the work for you. Three different cost structures, so of course the prices do not line up.

Sorting has to come before pricing. And sorting takes three questions.

Three questions that cut the whole market

One: who runs the measurement? Does this provider have their own measurement system, or do they buy the same third-party tool you could buy — or check by hand a few times a month?

Two: who makes the changes? Once the report exists, do they send people to change your site and content, or do they hand you a to-do list?

Three: when the rules change, who notices? Engine ranking logic, crawler conventions, the shape of content worth citing — all of it shifts every quarter, and what worked last year may not hold this year. So the question is what this provider relies on to know the rules moved: waiting for a tool vendor to push an update, a consultant catching whatever crosses their desk, or testing for it themselves?

The first two decide what you buy. The third decides how long it holds. Cross all three and Taiwan’s AI visibility market falls into roughly five types:

Type Who measures Who executes Who notices rule changes
Pure monitoring tool In-house Nobody The tool updates its own product, not your site
SEO agency add-on Inherited ranking metrics Someone does Tracks the SEO body of knowledge; new GEO conventions may not be on the radar
Automated schema generator None Scriptable items only Follows schema spec revisions, and only that slice
Consultancy-only Bought or manual Someone does Rests on individual consultants keeping up; depth varies by person
Platform plus execution Own platform Someone does Tests for it — rule shifts surface through in-house backtesting

That last column is the one position on this table with nobody standing alongside, and also the one most often skipped: most buyers compare only the first two, because the first two appear in the proposal and the third does not.

Each type is expanded below: what it actually sells, when it is the right call, and what it structurally cannot do.

Type 1: Pure monitoring tools (measurement yes, fixing no)

They run a set of queries against major AI engines on a schedule, record whether your brand comes up and where the frequency is heading, and plot the trend.

When it fits: you have already committed to the long game and need a line you can watch weekly; or you have people in-house who can read the report and find time to act on it.

What it cannot do: it will not tell you why the line dropped, and it will not fix it. Monitoring answers “where things stand”, not “what to do about it”. The tool layer breaks down further, and we have a separate piece on picking one: How to choose an AI visibility tool.

Type 2: GEO as an SEO agency add-on (execution yes, measurement inherited)

You already run SEO, and the agency adds a GEO line to the existing contract. Someone does execute — that is this type’s real advantage, since they already have their hands on your site.

When it fits: the SEO contract is live, you want to start somewhere, and you would rather not manage two vendors.

What it cannot do: measurement usually carries over the search-ranking metrics. Rankings and AI citations are two different things, and using keyword movement to prove AI visibility improved leaves a step of the argument unfilled. Ask directly: which number in this monthly report measures AI citation itself?

Type 3: Automated schema generators (no measurement, narrow execution)

They fill in your structured data, producing JSON-LD so machines can read what your pages are about. This genuinely helps, and it is one of the few parts that can be handed entirely to software.

When it fits: your structured data is empty and you want that gap closed cheaply and quickly.

What it cannot do: its reach is the scriptable slice. What content your site should carry, how your expertise gets written into passages worth quoting, whether anyone outside is discussing you — a generator produces none of it. You buy one, the score moves a little, and then it stops.

Type 4: Consultancy-only execution (execution yes, measurement bought or manual)

People change your site, your content, and your off-site signals. The value here is the people: judgment, industry knowledge, knowing which gap to close first.

When it fits: you want someone accountable for the outcome, a single person to ask questions, and judgment specific to your industry.

What it cannot do: the measurement end is usually a purchased tool or manual spot-checking. The trouble with manual spot-checks is not that they are slow — it is that they cannot be rerun. Ask the same question three times and AI may answer three ways, so a single check leaves you unable to tell a pattern from a coincidence. Ask for a method you could rerun yourself and reach the same conclusion; a lot of proposals get vague at that question.

Type 5: Platform plus execution (own the measurement, do the work)

An in-house measurement platform combined with doing the work. Measurement and changes live under one roof, so “what we changed” and “whether the number moved” can be lined up against each other.

When it fits: you have no time to watch AI engines, you want someone accountable, and you want to know whether each adjustment actually did anything.

What it cannot do: it carries the highest cost structure of the five, because the measurement system has to be maintained. If you only want a look at where you stand today, this is heavy machinery for a light job.

Strengths, weaknesses and risks, laid out

The five sections above cover what each type sells. What actually drives the decision is the other two columns: what you might gain by picking it, and what you might walk into.

How to read the table: strengths and weaknesses describe the capability boundary of the provider type; opportunities and threats describe what happens to you once you pick it. The threat column is each type’s most typical failure mode, and it is the one worth reading before you sign.

Type Strengths Weaknesses Opportunities Threats
Pure monitoring tool Many engines, many queries, a long-run trend you can check weekly Describes the present only; no cause, no fix With execution capacity in-house, it moves “what should we fix” from opinion to data You buy the line but nobody picks up the work; a year later you own a chart that got worse
SEO agency add-on Their hands are already on your site, so execution lands fastest Measurement inherits ranking metrics, leaving a gap to AI citation SEO and GEO share a foundation, so one set of fixes serves both A polished monthly report with no number measuring AI citation directly — improvement stays unproven
Automated schema generator One of the few parts software can own outright; small input, quick effect Limited to scriptable items; cannot touch content or off-site When structured data is empty, this is the highest-return first step Mistaking “the score moved” for “AI started citing me”, then stopping there
Consultancy-only Someone is accountable, and judgment can fit your industry Measurement is bought or manual, and spot-checks cannot be rerun Where the industry is unusual and judgment must be bespoke, people are worth most here Results rest on checks nobody can reproduce; change the person and the thread breaks
Platform plus execution Measurement and execution under one roof, so changes and numbers line up Highest cost structure; the measurement system needs maintaining Over a long engagement, every adjustment can be verified after the fact If you only want a look at where you stand, this is heavy machinery for a light job

No star ratings, no monthly fees, no pick-of-the-bunch. Prices shift every quarter and a number written down today misleads a few months out; and which type is best depends on where you are. Sort first and the shortlist narrows on its own.

Why sorting beats haggling

The same monthly budget buys wildly different things across these types. Spend it on a monitoring tool and you get a trend line plus an execution gap you still have to staff. Spend it on consultancy and you get people doing the work but possibly no scorecard you can verify. Spend it on platform plus execution and you get both, at a higher unit price.

When three proposals quote differently, that is usually a difference in what is being sold rather than a difference in markup. Sort them first, and you can tell whether the cheap one is genuinely cheap or simply missing the part you assumed was included.

As for whether to just do it in-house — that is a different axis. This piece covers who you can hire. To compare doing it yourself, buying a tool, hiring an agency, or handing it to a managed operator, the Solutions page has a comparison table.

The column nobody is comparing

Public provider comparisons already exist in this market. They compare features, monthly fees, target clients, methodology. Not one of them lists “who notices when the rules change” as a criterion — research capability appears on none of those tables, which is why it never occurs to you to ask about it either.

That is why the last column of the table above has exactly one position that can answer “tests for it”. In the Traditional Chinese market, keeping research and execution under one roof is, at present, a set of one. That claim is falsifiable: find a second provider who can answer that question and the column is no longer empty.

Worth adding: multiple public reviews report that international tools have limited support for Chinese semantics. If your customers query AI in Chinese, that goes straight to whether the measurement is accurate — whether the prompts match how people here actually phrase things, whether tokenization has been tuned for Chinese, whether the region can be pinned to zh-TW. Those three questions work on any provider claiming measurement capability.

Last reviewed: August 2026. This market moves quickly; this page gets re-checked quarterly.

Where we sit, plainly

geoweb.tw is a type five. The 12-dimension GEO health scoring and the AI citation backtesting are our own platform, and the team also carries out the on-site fixes and content. That is our position, stated here so you know who drew this map.

Equally worth saying: several situations do not call for a type five at all.

Structured data empty and everything else reasonably sound — close that gap and stop, which is type three work at a fraction of the cost. Engineering and content people already in-house, just missing a line to watch — you want type one. SEO contract still running and you only want to test the water — the type two add-on is the least friction.

This is not an argument for buying type five. Sorting is useful because once you know your own situation, one of the first four types may already be enough.

Two things worth doing this week

Put those three questions to the proposals on your desk. Who runs the measurement, who makes the changes, who notices when the rules move. Answer all three and each proposal files itself; wherever the answer goes vague, that is the edge of what they do. There is also a messier situation — not a sorting problem, but a proposal with fabricated parts in it. We covered that separately: The emperor’s new clothes.

Get your own baseline first. Whichever type you end up hiring, you need a number from before you hired anyone, or every report you receive afterwards is something you can only take on faith. For the site-health layer, run a free analysis at geoweb.tw — thirty seconds. If the gaps look wide and you are unsure which part to close first, send the report to [email protected] and we will come back with a specific read — including the parts you should handle yourself rather than hire out.

Further reading