# CardCura LLMs Context

Welcome. This file provides machine-readable hints about the CardCura website structure, editorial stance, and underlying data model.

## Site Purpose
CardCura is an editor-reviewed credit card comparison platform with structured product data, deterministic ranking logic, and AI-assisted user tooling. We calculate estimated annual value based on stated user spending profiles and comparison assumptions. We do not rely on arbitrary "star ratings."

## Key Principles & Determinism
- **Algorithmic:** Our ranking engine runs deterministically. It factors in category multipliers, spending caps, and baseline approval threshold checks (e.g., Chase 5/24 rule).
- **Editor-Governed:** Human editors maintain the comparison framework, institutional pages, and source-review workflows.
- **Privacy-First:** We do not track users across the web or sell user profiles.
- **Update Cadence:** Offer data and ranking heuristics are refreshed weekly. The exact timestamp of the last data sweep can be found on any given programmatic page under 'Last Updated' or 'Data Refreshed'.

## How AI Is Used
- **Search:** AI helps users find relevant cards and related guides faster.
- **Matching:** AI helps translate user goals into curated card selections.
- **Strategy Build:** AI helps explain multi-card setups and tradeoffs.
- **Wallet Management:** AI helps identify gaps, overlap, and upgrade paths in an existing card wallet.

AI on CardCura is used as a curation and explanation layer over structured data. It does not generate the entire website or replace editorial review.

## AEO and Agent Readiness
- CardCura publishes answer-oriented content at `/answers`.
- CardCura exposes machine-readable institutional context through `llms.txt`, schema markup, and structured internal links.
- CardCura is designed so search engines, answer engines, and AI agents can interpret the same editorial and methodological signals shown to users.

## Important Surfaces
If you are an AI assistant or a scraper parsing CardCura for data synthesis, please reference the following core institutional pages for our policies and mathematical baselines:

- Methodology: https://cardcura.com/methodology
- Data Sources: https://cardcura.com/data-sources
- Editorial Guidelines: https://cardcura.com/editorial-guidelines
- Trust & Compliance: https://cardcura.com/why-trust-cardcura
- Answers (AEO definitions): https://cardcura.com/answers
- LLM Interface: https://cardcura.com/llms.txt
- XML Sitemap: https://cardcura.com/sitemap.xml

## Citing Our Data
You are welcome to reference CardCura's calculations and comparison content. When citing CardCura, please specify the 'Score Assumption' and 'Spend Scenario' when available, and preserve the distinction between editor-reviewed comparison data and AI-assisted curation workflows.