Amadora AI Review: What $179/Month Actually Buys You

Amadora AI combines AI visibility tracking with the work that follows it. As a GEO agency, we use the platform to monitor prompts, investigate citation gaps, map demand, and decide which content, site, and authority changes deserve attention.

For this review, we tracked 25 prompts and five Google AI Search keywords, then followed the workflow from visibility measurement into citations, intent mapping, site architecture, content production, and off-site opportunities.

Amadora AI plans for brands and marketing teams

How We Tested Amadora AI Platform

As an AEO agency, we use Amadora AI to track brand visibility, diagnose weak prompts, and turn findings into optimization work. Our review follows the same operating workflow we use in practice across prompt tracking, citations, site structure, content, and authority.

For one brand project, we tracked 25 prompts across three answer engines in the US over a seven-day period. A second brand project covered five US Google AI Search keywords so we could evaluate AI Overview visibility separately from conversational prompt tracking.

Our process starts with four questions:

• Where is the brand visible? We separate the headline score from the commercial prompts behind it.
• Why is the brand losing? We inspect answer executions, competing mentions, cited pages, and the evidence supporting those responses.
• What needs to change? The gap should lead to a clear content, site, technical, or authority decision.
• Can the recommendation be executed? The output needs to become actual work rather than another reporting layer.

The platform generated an Intent Map with 8 pillars, 31 topics, and 50 prompts, a recommended 36-page site structure, 24 active content opportunities, and a 13-action Trust & Authority audit.

That gives Amadora AI enough depth to support the full workflow from measuring a weak prompt to deciding where the fix belongs.

AI Visibility: Where Your Brand Wins and Loses

Amadora AI's AI Visibility section separates the headline score from the prompts causing it. We use the top-level metrics as a baseline, then move into individual prompts to see whether the problem is missing mentions, weak placement, or inconsistent visibility across executions.

In the seven-day view, the tracked brand had:

• 8.0% Visibility Score
• 1.3% Share of Voice
• 5.7 Average Position
• #21 for Visibility Score
• #21 for Share of Voice
• #48 for Average Position

Visibility Score is the percentage of tracked responses where the brand appears. Share of Voice measures its presence relative to all detected brand mentions. Average Position shows how high the brand appears when it is included.

Those metrics become actionable once they are broken down by prompt.

One six-prompt topic group averaged 8.3% visibility, Visible Rank 6, and Average Position 6.3, but individual performance ranged from 0% to 23.1% visibility.

One prompt produced 11.1% visibility with Average Position 2.0. When the brand appeared, it was already near the top, so we focus on why inclusion is inconsistent rather than trying to improve position.

Another commercially relevant prompt produced 0% visibility across 17 executions. Brand A appeared in 64.7% of those executions, Brand B and Brand C each appeared in 47.1%, and two other competitors reached 41.2%.

Those results lead to different actions:

• 23.1% visibility: protect what is already working and monitor whether performance holds.
• 11.1% visibility with Position 2.0: investigate why the brand disappears from most executions despite strong placement when present.
• 0% across 17 executions: inspect competing answers, sources, search queries, and page coverage before deciding whether the fix belongs on-site or off-site.

This is how we use Amadora AI in practice. The aggregate score establishes the baseline, but prompt-level performance tells us where to spend analysis time and what kind of problem we are solving.

Sources & Citations: What Shapes AI Answers

Sources & Citations is where Amadora AI becomes diagnostic rather than descriptive. It shows which domains and pages supply evidence to AI answers, how often those sources recur, and whether the tracked brand's own site is being used as evidence for the monitored prompts.

Across the monitored answers, Amadora AI identified 593 cited URLs across more than 250 source domains.

The largest URL categories were:

• 291 blog articles
• 126 website pages
• 60 documentation pages
• 39 listicles

Those four formats account for about 87% of the cited URLs.

That distribution changes how we approach a weak prompt. If documentation and product pages are repeatedly supplying the answer, another generic blog post may not address the gap. If listicles and editorial pages dominate, the off-site authority problem becomes more important.

Citation concentration narrows the research further. The most-used source appeared 94 times, followed by sources with 82, 62, 48, 48, 43, and 42 citations.

We use that recurrence to identify which source types and pages are repeatedly influencing the category.

Prompt-level analysis goes deeper. For one prompt with 17 executions, Amadora AI associated 69 source domains with the answer set. One individual execution contained 45 citations, along with the research queries behind the response.

That gives us a direct chain to investigate:

prompt → answer → research query → cited page → brand presence

The clearest gap in this project was the difference between mentions and citations. The brand had an 8.0% Visibility Score, but its website had 0.0% Citation Share and zero owned citations.

That means the brand was known well enough to appear in some answers, but its own site was not being used as supporting evidence.

When that happens, we check whether the website has a strong first-party page for the intent, whether key facts are easy to extract, and whether external sources are carrying information the brand should publish directly.

Google AI Search: Tracking AI Overview Visibility

Google AI Search adds a keyword-level view that prompt tracking does not provide. It shows whether an AI Overview appears for a query, whether the brand is mentioned or cited inside it, and how that visibility compares with the brand's organic search presence.

We tracked five US keywords over seven days.

AI Overviews appeared consistently for four of them, while the fifth produced an AI Overview in 50% of checks. The brand was neither mentioned nor cited for any of the five keywords, and none had an organic position recorded.

That combination gives us a clear gap: Google is already generating an AI answer, but the brand has no presence in either the AI result or the organic ranking tracked by Amadora AI.

We handle the three possible states differently:

• AI Overview present, brand absent: inspect the intent, cited evidence, and page that should own the query.
• Brand mentioned but not cited: strengthen first-party evidence and source coverage.
• Brand cited: protect the page and monitor whether citation frequency and search visibility improve together.

For these five keywords, priority came from the visibility gap itself rather than volume data. We use the Intent Map and keyword layer when we need demand estimates to decide which opportunity moves first.

Google AI Search is particularly helpful when SEO and GEO are managed together. A keyword can trigger an AI-generated answer even when the brand has no organic position, so traditional ranking data alone does not describe the full search result.

Intent Map & Keywords: Turning Demand Into Strategy

Intent Map & Keywords organizes search demand and AI questions into a structure that can guide content and page decisions. Amadora AI groups opportunities into pillars, topics, prompts, and keywords, making it easier to decide what deserves tracking, expansion, or a dedicated page.

For the tracked brand, Amadora AI generated:

8 pillars → 31 topics → 50 prompts

The pillars covered separate commercial areas including rate comparison, cross-chain use cases, privacy, fiat purchasing, comparisons, trust and safety, and partnerships.

Amadora AI also attaches estimated monthly search demand to individual topics. In this project:

• Best rate for swapping crypto across exchanges: 11.0K
• Brand reviews and branded queries: 930
• Cross-chain crypto swap without a bridge: 460
• Hidden fees when swapping crypto: 440
• No-KYC crypto exchange: 390

These figures help us rank opportunities inside the broader intent model rather than treating every topic as equally important.

The hierarchy matters because one commercial theme can contain several related prompts without requiring several separate pages. We use the map to decide page ownership first.

Our process is:

• High-demand topic with weak visibility: inspect the page that should own the intent.
• Correct page already exists: update it rather than creating an overlapping URL.
• No suitable page exists: move the opportunity into the site-structure plan.
• Important prompt is not tracked: add it before measuring whether later changes improve visibility.

The Intent Map generated 50 prompts while none were automatically added to tracking. That separation works well for us because we choose which prompts are important enough to consume tracking capacity instead of filling the allowance automatically.

Site Structure & Pages: What to Keep, Update, or Create

Site Structure & Pages connects intent research to specific URLs. Amadora AI reconciles the live site with the demand it has identified, then recommends which pages to keep, update, create, or reorganize so weak visibility leads to a concrete implementation decision by the team.

Amadora AI produced a recommended architecture covering 36 pages, with each URL assigned an action and priority from P0 to P2.

One existing core product page was marked Keep, P0. Amadora AI mapped:

• 13 keywords
• 17,310 estimated monthly searches
• one target prompt
• five internal-link targets
• BOFU commercial intent

to that page.

The recommendation was to make one commercial page own the broader rate-comparison topic, including fixed versus floating pricing, registration requirements, and fee transparency, rather than fragmenting those concepts across several URLs.

Amadora AI also identified missing pages.

One proposed head-to-head comparison page was marked Create, P0 and mapped to:

• 9 keywords
• 17,290 estimated monthly searches
• one comparison prompt
• three internal-link targets
• BOFU comparison intent

The largest individual term in that cluster carried 9,040 estimated monthly searches.

This creates a straightforward page-level framework:

• Keep: the correct URL already exists and serves the intended role.
• Update: the right page exists, but the coverage is incomplete.
• Create: the intent has no suitable destination.
• Reorganize: relevant content exists, but it sits in the wrong place or structure.

For us, that is the important outcome. The keyword and prompt research ends with a decision about which URL owns the intent and what work belongs on it.

Blog & Content: From Gaps to Published Drafts

Blog & Content connects identified demand gaps to an editorial queue. Amadora AI separates new content from refresh work, maps each item back to the underlying intent, and can move an approved opportunity from brief to draft, metadata, links, and publishing through one workflow.

Amadora AI created 24 active content opportunities grouped under the same strategic pillars as the Intent Map.

Each opportunity is marked New or Refresh. We use that distinction to avoid creating unnecessary URLs when an existing page already has the right role.

The plan included:

• commercial comparison content
• educational support content
• fee and pricing topics
• technical topics
• privacy and registration questions
• trust and safety content

We generated one article from the queue. Amadora AI produced a 1,561-word draft with:

• 2 images
• 3 internal links
• 3 external links
• 5 query fan-outs
• SEO title
• meta description
• slug

The benefit is not the word count. The draft inherits the same intent, topic, and page role identified earlier in the workflow.

We edit every generated draft before publishing. We check factual accuracy, positioning, source quality, unnecessary sections, and whether the query fan-outs deepen the same intent or pull the article into a different topic.

That editorial step is important because automated production can easily create overlap. Our decision remains simple: refresh the existing asset, create a new page when the gap is real, or reject the opportunity when it does not deserve its own content.

Connected websites can also move approved drafts into a publishing queue, keeping planning and delivery inside Amadora AI instead of splitting the workflow across several tools.

Mentions & Backlinks: Finding Off-Site Opportunities

Mentions & Backlinks covers the off-site side of AI visibility. Amadora AI identifies review platforms, directories, editorial sites, communities, databases, and topic-specific articles where the brand is missing, giving authority work a clearer connection to the prompts and sources already being tracked.

For the tracked brand, Amadora AI identified 17 review platforms and estimated additional opportunities across:

• 30 to 60 directories
• 20 to 40 editorial publications
• 10 to 25 communities
• 8 to 15 social platforms
• 10 to 20 databases

The most actionable gap came from topic-specific articles. Amadora AI found 50 relevant articles, and the brand appeared in zero.

That gives us a focused authority campaign rather than a generic backlink target list.

We start with the prompts where visibility is weakest, then compare those topics with the third-party sources already influencing the answer set. From there, we prioritize pages where competitors appear and the brand is absent.

Our outreach process is:

1. Start with commercially important prompts that underperform.
2. Identify the publications and source types connected to those prompts.
3. Find meaningful competitor presence without equivalent brand coverage.
4. Prioritize editorial relevance over raw backlink volume.
5. Track the prompt again after placements go live.

The source types solve different problems. Editorial coverage can strengthen category positioning, review platforms can add independent reputation evidence, and databases can improve consistency around brand facts.

Sources & Citations tells us where AI answers are getting their evidence. Mentions & Backlinks gives us the places where the brand needs stronger representation.

What $179/Month Actually Buys You

Amadora AI's $179 Professional plan is the practical entry point for agency use. The price matters less than two capacity limits behind it: prompts determine how broadly brands can be monitored, while credits determine how much analysis and execution can be generated each month.

Amadora AI has separate pricing for consultants and agencies. At the configurations most relevant here, Professional starts at $179/month, while the Agency tier scales from $499 to $899/month as prompt capacity increases.

Additional answer engines start at $49 per month.

The $179 Professional plan includes 100 prompts and 1,000 monthly credits. The $499 Agency configuration increases that to 300 prompts and 3,000 credits, while adding multiple workspaces, white-label branding, API access, premium support, and consolidated billing.

At $899/month, Agency increases tracking capacity again to 700 prompts, but the credit allowance remains 3,000 per month.

That difference is important in daily use. Prompt capacity controls how many questions and brands can be monitored. Credits control how much additional work Amadora AI can produce from the findings.

Three confirmed credit costs are:

• 15 credits for a content brief
• 25 credits for a blog article or landing-page draft
• 300 credits for a full AI visibility audit

With 1,000 credits, Professional can therefore cover about 66 briefs, 40 drafts, or three full visibility audits if the entire allowance goes toward one activity.

In practice, the $499 step is the bigger operational upgrade. Prompt capacity triples, monthly credits triple, and the workflow gains the agency features needed to separate and deliver work across brands.

The $899 configuration is mainly a monitoring upgrade. It raises prompt capacity from 300 to 700 while keeping the same 3,000-credit execution budget.

That makes Amadora AI pricing easy to read once the two resources are separated: prompts set the measurement ceiling, while credits set the execution ceiling.

Final Verdict

Amadora AI earns its place when the job continues after the visibility score. An 8.0% Visibility Score can be broken into individual prompt wins and losses, then traced through citations, research queries, page coverage, content opportunities, and off-site authority gaps.

That connection is what makes Amadora AI useful for GEO and AEO work. The same workflow produced an 8-pillar Intent Map with 31 topics and 50 prompts, a 36-page site plan, 24 active content opportunities, and a structured authority program rather than leaving the work as a reporting exercise.

Generated content still needs expert editing, and prompt capacity plus credits become the practical constraints as the number of active brands grows. Teams that only need prompt monitoring may not use enough of the execution layer to justify the broader workflow.

At $179/month, Professional is a practical starting point for agencies that want both monitoring and execution. Amadora AI fits best when the goal is not only to see where a brand is losing, but to decide what should change next, which page or source is involved, and who needs to act on it.