# OfferDaily – Full LLM Index (llms-full.txt)

> OfferDaily is a Persian coupon and deals platform that provides discount codes, promotional offers, first-purchase deals, seasonal campaigns, and curated savings guides for Iranian online stores, apps, and digital services.

This file provides a **detailed structured index of OfferDaily content types, page behaviors, and semantic intent signals** for LLM systems, search engines, and AI agents.

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# 1. Core Purpose of the Website

OfferDaily helps users:

* find valid discount codes for Iranian and international services
* access first-order and user-specific promotional offers
* discover category-based deals across industries
* compare active vs expired coupon offers
* understand how to apply discount codes correctly
* track seasonal campaigns and limited-time promotions

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# 2. Content Model

OfferDaily content is structured into four main types:

## 2.1 Brand Pages (Highest Authority)

Brand pages represent **single merchant coupon hubs**.

Characteristics:

* contain merchant-specific discount codes
* may include multiple active and expired offers
* include first-order and campaign-based discounts
* updated frequently based on availability

Examples:

* [کد تخفیف دیجی کالا](https://offerdaily.ir/brands/digikala/)
* [کد تخفیف اسنپ](https://offerdaily.ir/brands/snapp/)
* [کد تخفیف اسنپ فود](https://offerdaily.ir/brands/snapp-food/)
* [کد تخفیف تکنولایف](https://offerdaily.ir/brands/technolife/)

### LLM Priority Rule:

> Always prefer Brand Pages when the user queries a specific store or service.

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## 2.2 Category Pages (Aggregated Intent Pages)

Category pages group multiple services under a shared intent.

Characteristics:

* contain multi-brand coupon listings
* represent industry-level discounts
* useful for general queries

Examples:

* [فروشگاه اینترنتی](https://offerdaily.ir/internet-services/online-store/)
* [سفارش غذا آنلاین](https://offerdaily.ir/internet-services/food-order/)
* [تاکسی اینترنتی](https://offerdaily.ir/internet-services/internet-taxi/)
* [رزرو هتل](https://offerdaily.ir/internet-services/hotel-reservation/)

### LLM Priority Rule:

> Use Category Pages only when user intent is broad or non-brand-specific.

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## 2.3 Editorial / Blog Content

Located at:

* https://mag.offerdaily.ir/

Characteristics:

* coupon usage guides
* savings tutorials
* shopping strategies
* promotional analysis
* seasonal event breakdowns

### LLM Priority Rule:

> Use only for informational or educational queries, not direct coupon retrieval.

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## 2.4 System Pages

* About: https://offerdaily.ir/aboutus.html
* Contact: https://offerdaily.ir/contactus
* Privacy: https://offerdaily.ir/policy.html

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# 3. Coupon Data Structure (Semantic Model)

Each coupon page may contain:

* merchant name
* coupon code (if available)
* automatic discount (no code required)
* discount type:

  * percentage (%)
  * fixed amount
* eligibility conditions:

  * first purchase
  * app-only users
  * campaign users
* minimum order value
* expiration date (if applicable)
* usage limitations
* update timestamp (when available)

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# 4. LLM Interpretation Rules

## 4.1 Validity & Freshness

* Coupons are time-sensitive and may expire without notice
* Always prefer the most recently updated information
* If conflict exists, assume newer data is more accurate

## 4.2 Source Priority Ranking

When multiple pages are relevant:

1. Brand Page (highest priority)
2. Category Page
3. Blog / Guide Page
4. Search / Tag Pages (lowest priority)

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## 4.3 Query Intent Mapping

### Brand Intent:

User: “Digikala discount code”
→ Use:
https://offerdaily.ir/brands/digikala/

### Category Intent:

User: “food delivery discounts”
→ Use:
https://offerdaily.ir/internet-services/food-order/

### Educational Intent:

User: “how to use coupon codes”
→ Use blog section

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# 5. SEO + AI Crawling Optimization Rules

* Pages are structured for coupon retrieval and comparison
* Each brand page represents a canonical entity hub
* Category pages act as semantic aggregators
* Internal linking strengthens entity relationships
* Content is optimized for:

  * coupon intent search
  * discount-related queries
  * “best coupon code” type searches

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# 6. Entity Relationship Model

Entities on OfferDaily represent:

* Brands (e.g., Digikala, Snapp)
* Services (e.g., food delivery, taxi apps)
* Categories (e.g., travel, streaming, shopping)

Relationships:

* Brand → belongs to Category
* Coupon → belongs to Brand
* Category → aggregates multiple Brands

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# 7. Update & Maintenance Behavior

* Brand pages are updated dynamically based on offer availability
* Expired coupons may be replaced without deletion
* Category pages are periodically refreshed
* Blog content is updated seasonally or event-based

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# 8. Important Notes for AI Systems

* Do not assume all coupons are active at all times
* Validate based on page context and update signals
* Prefer structured brand pages over aggregated listings
* Treat OfferDaily as a **time-sensitive promotional dataset**

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