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AI content health, intent mapping, and competitor coverage
See where your page wins, loses, and needs to improve for AI search.
AI Match Tracker compares your target page against competitor pages, maps real user questions, and turns AI visibility gaps into a structured action plan.
Built for content, SEO, growth, and product marketing teams that need a clear way to evaluate page performance across intent clusters, answer coverage, and third-party influence sources.
✓ Saved search history
✓ Phase 1 intent clusters
✓ Phase 2 coverage matrix
✓ Perplexity influence scan
best cashback credit cards canada
Persona: new immigrant · Status: done
Recommendations
Newcomer eligibility module
Add required documents, approval notes, and secured-card alternatives.
High
Comparison table
Show fees, cashback categories, caps, and redemption timing.
High
FAQ expansion
Answer real fan-out questions from Phase 1 clusters.
Medium
Intent clusters
Limited credit history
9 fan-out questions · TARGET partial
Secured & starter cards
8 fan-out questions · TARGET no
Fees & redemption
8 fan-out questions · TARGET partial
What this tool does
A practical content health workflow for AI answer visibility.
The tool helps you understand how well a page answers the questions people ask around a topic, how that page compares with four competitors, and which external sources may influence AI-generated citations.
Who it is for
Content strategists, SEO teams, growth marketers, financial-product teams, and agencies that need a repeatable way to evaluate page strength for GEO, AI Overviews, ChatGPT-style answers, and related LLM discovery surfaces.
The problem it solves
Manual competitor research is slow and inconsistent. AI Match Tracker turns a topic, persona, target URL, and competitor set into intent clusters, fan-out questions, coverage judgments, best-page comparisons, and specific recommendations.
- Finds the questions your page must answer to compete.
- Shows whether each page answers those questions: Yes, Partial, or No.
- Highlights what your target page is missing when another page wins.
How it works
From login to prioritized content recommendations.
The current application uses a two-phase workflow: Phase 1 generates intent clusters and real questions, while Phase 2 analyzes the target and competitor pages against those questions.
2
Enter scope
Add a topic, optional persona, one target URL, and four competitor URLs.
3
Run Phase 1
The AI returns intent clusters and fan-out questions for the chosen topic.
4
Run Phase 2
The tool fetches page content, judges coverage, and maps the best URL per query.
5
Review output
Use recommendations, query coverage, and external influence data to improve the page.
Main features
Everything needed to turn page gaps into an action plan.
These features are based on the current routes, templates, database fields, service files, and the provided tool details document.
Private search dashboard
Each user sees their own recent searches, including topic, persona, status, and creation time.
Topic and persona targeting
Run analysis from a specific audience perspective, such as newcomer, student, working professional, or general consumer.
Target vs. four competitors
Benchmark one target URL against four competitor URLs using the same intent and question set.
Intent clusters and fan-out queries
Phase 1 creates 6–10 clusters and multiple natural-language questions for each cluster.
Coverage matrix
Phase 2 marks every page as Yes, Partial, or No for each fan-out query, then identifies the best URL.
External influence scan
Perplexity-powered research checks selected third-party platforms and citations that may influence AI answers.
Product walkthrough
Actual-looking UI snapshots based on the current app.
The previews below are recreated as static HTML/CSS from the existing EJS templates and database sample content. They avoid external images so this landing page remains a single portable file.
Login screen
A simple sign-in card lets users access saved searches. The style mirrors the app: centered white card, slate background, small helper text, rounded inputs, and dark full-width button.
Actual route in the app: /login
LLM Performance Optimizer
Sign in
Access your saved searches.
ahmad
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Dashboard and saved search history
The dashboard lists each search with topic, persona, status, and created date. Statuses use the same done, running, and error badge language used in the current table.
Actual route in the app: / behind the configured base path.
Your searches
Only you can see your history.
| Topic | Persona | Status | Created |
|---|---|---|---|
| best cashback credit cards canada | new immigrant | done | 2/10/2026, 4:32 PM |
| mortgage renewal options Canada | working professional | running | 2/10/2026, 4:41 PM |
| student chequing accounts | student | error | 2/10/2026, 4:05 PM |
New topic match input screen
Users define the analysis scope: topic, optional persona, target page, and four competitor pages. The app currently runs Phase 1 first to generate clusters and questions.
Actual route in the app: /new
New topic match
Phase 1 generates clusters & fan-out queries only. Phase 2 analyzes pages.
best cashback credit cards in Canada
new immigrant
https://www.example-brand.com/ca/en/.../cash-back
https://www.competitor-1.example/...
https://www.competitor-2.example/...
https://www.competitor-3.example/...
https://www.competitor-4.example/...
Progress and status handling
Each search is stored with a status value: queued, running, done, or error. The report screen also displays fetch warnings when pages cannot be retrieved.
Status values are stored in the searches table.
Analysis progress
Phase 1 complete
Intent clusters and fan-out questions generated.
done
Fetching pages
Target and competitor pages are extracted once for judging.
running
Coverage and recommendations
Best URL, missing details, and page-level actions.
queued
Some pages couldn’t be fetched
Common reasons: bot blocking, redirects, or content requires JavaScript.
Final report and coverage matrix
Phase 2 shows recommendations, query coverage by page, best URL for each question, why it wins, and what the target page is missing.
Actual report sections: Recommendations, Query coverage + best URL, and External influence platforms.
Recommendations (Phase 2)
- Add a top-of-page newcomer eligibility section with required documents and approval pathways.
- Create a side-by-side table for fees, cashback categories, caps, and redemption rules.
- Add FAQ answers for questions where TARGET is currently Partial or No.
Query coverage + best URL (Phase 2)
| Cluster | Query | Best URL | TARGET | C1 | C2 | C3 | C4 |
|---|---|---|---|---|---|---|---|
| Limited credit history | Can I get a cashback card without a Canadian credit score? | Example Brand cash back page | No | No | No | No | No |
| Fees and value | When is an annual fee worth it? | Competitor comparison page | Partial | Yes | Partial | Yes | Partial |
| Redemption | How is cashback paid out? | Target card page | Yes | Partial | Partial | Yes | Partial |
External influence panel
Instead of an admin/settings screen, the current app includes a topic-level external influence section. It summarizes third-party platforms, mentions, evidence URLs, and top citations.
Current platforms include sources such as Industry Source 1, Industry Source 2, Industry Source 3, Community Source, Reference Source, and Regulatory Source.
External influence platforms
Perplexity web-grounded responses with citations.
source-1.example
Highlights top cash back cards and competitor mentions for the topic.
community-source.example
Community discussions may influence trust signals and AI citations.
regulatory-source.example
Regulatory and educational pages can support credibility.
| Domain | Summary | Evidence |
|---|---|---|
| source-1.example | Mentions category leaders and common card comparison criteria. | Top citations and platform URLs |
| regulatory-source.example | Provides neutral education about credit cards, fees, and rights. | Government source links |
Final output
What users get from the report.
A completed Phase 2 report gives users both the detailed evidence and the practical actions needed to improve the target page.
Intent clusters
Groups of related user needs, such as eligibility, fees, cashback categories, redemption, approval odds, and comparisons.
Fan-out questions
Natural questions users may ask AI tools, search engines, or comparison experiences around the topic.
Coverage statuses
Each target and competitor page is judged as Yes, Partial, or No for every question.
Best URL mapping
The report identifies which page gives the strongest answer and why that page wins.
Missing details
For gaps on the target page, users see what is missing and where the content needs more clarity.
Recommendations
Specific content improvements, such as new sections, comparison tables, eligibility notes, and FAQ additions.
Fetch warnings
If a page cannot be fetched, the report keeps the issue visible so users understand the limitation.
Saved history
Searches are stored in the dashboard so users can return to previous reports later.
Why it is useful
A faster, more structured way to improve AI-ready content.
The tool gives teams a repeatable review process instead of relying on scattered notes, one-off competitor checks, or vague content recommendations.
Saves time
Automates the slow work of expanding topics into question sets and reviewing multiple competitor pages.
Improves structure
Organizes analysis around clusters, queries, coverage, best answer, and missing content.
Finds opportunities
Reveals where competitors answer user questions better and where your page can win more often.
Makes actions clearer
Turns coverage gaps into practical recommendations for sections, tables, examples, and FAQs.
Ready to run a new content health search?
Open the dashboard, start a new topic match, and use the report to prioritize the next improvements for your target page.