Forem: Morteza Riahi

I Built a QR-Based Restaurant & cafe System

Morteza Riahi — Tue, 07 Apr 2026 14:27:59 +0000

I didn’t start this project thinking I was building a full product.

At first, it was just a simple idea:
Replace physical menus with QR codes.

But once I started looking deeper into how restaurants actually operate, I realized something important:

A menu alone doesn’t solve the real problem.

The issue is the entire ordering flow.

The Shift: From “Menu” to “System”

Most QR menu tools stop at a very basic level:

You scan
You see a menu
That’s it

But restaurants don’t need a static menu on a phone.

They need a system that actually works.

So I built something beyond that.

https://pleasescanmenu.com//
What PleaseScanMenu Actually Does

This is not just a QR menu.

It’s a full ordering and management system.

Restaurants have access to a dashboard where they can:

Manage menus in real-time
Monitor incoming orders
Control table configurations
Adjust system behavior

Without login, there is no real control layer. It becomes just a tool, not a product.

Real Ordering Flow

Customers don’t just browse.

They can:

View menu items
Select and customize orders
Send orders directly from their phone

On the restaurant side:

Orders arrive instantly
Each order is tied to a specific table
No manual input is needed
The Core Idea That Changed Everything

At some point, I stopped thinking in terms of users.

I started thinking in terms of tables.

Each table acts as:

A session
An identity
A source of events

This simplifies a lot:

No need for customer accounts
Orders are naturally grouped
State becomes easier to manage
Real-Time as a Requirement

One of the biggest lessons was this:

If it’s not real-time, it breaks the experience.

The system is designed so that:

Menu updates reflect immediately
Orders appear instantly
Notifications are delivered without delay

Otherwise, staff will revert to manual processes.

Why Login + Ordering Matters

Most QR menu tools miss one of these:

They either don’t have login
Or they don’t support real ordering

Without login:

No persistence
No control
No system

Without ordering:

No real value

Combining both turns it into a lightweight SaaS product for restaurants.

Tech Stack

This was built end-to-end as a personal product:

Next.js (App Router)
TypeScript
Prisma
PostgreSQL
Custom dashboard logic
QR code generation system
Real-time interaction layer

Deployment and DevOps were also handled from scratch.

What I Learned

Several things became clear during development:

Simple ideas often become complex in real-world execution
Reducing friction is more valuable than adding features
Identity doesn’t always require user accounts
Real-time systems significantly improve usability
Final Thought

What started as a simple idea:

Replace menus

Turned into something bigger:

Redesigning the ordering experience inside restaurants.
QR Menu Cafe System

The Rise of AI Content: Why Evaluating AI-Generated Writing Matters More Than Ever

Morteza Riahi — Mon, 16 Mar 2026 16:04:28 +0000

Artificial intelligence has fundamentally changed the way digital content is created. What once required hours of manual writing, editing, and brainstorming can now be generated within seconds using AI tools. From blog articles and marketing copy to social media captions and product descriptions, AI-powered systems have dramatically accelerated the content production process.

However, while AI has made content generation faster and more scalable, it has also introduced a new challenge: how can we ensure that AI-generated content remains natural, readable, and valuable for human audiences?

This is where the concept of AI content evaluation becomes essential.

As businesses, creators, and digital marketers increasingly rely on artificial intelligence to produce written material, the ability to analyze and improve the quality of AI-generated text has become a critical part of the modern content workflow.

The Explosion of AI Content Across the Internet

Over the last few years, AI writing technology has improved significantly. Modern language models can generate long articles, answer questions, summarize information, and even create creative storytelling.

Because of these capabilities, AI-generated content is now widely used in many industries, including:

digital marketing

blogging and publishing

e-commerce product descriptions

educational content

knowledge bases and documentation

newsletters and media publications

For startups and content teams, this shift has made it possible to scale content production faster than ever before.

Instead of spending days drafting a long article, a writer can now generate an initial version within minutes and then refine it.

However, this convenience also creates a new problem: not all AI-generated content is high quality.

The Quality Problem in AI-Generated Content

While AI can generate text quickly, the output is not always perfect.

In many cases, AI-generated content may suffer from several issues:

repetitive phrases

unnatural sentence structures

lack of depth or insight

overly generic explanations

inconsistent tone

These problems can make content feel robotic or artificial, which can negatively affect reader engagement.

Readers today are extremely sensitive to content quality. If an article feels overly automated or lacks authenticity, audiences may lose interest quickly.

For this reason, creators cannot rely solely on AI-generated drafts. Instead, they need tools and processes that help them evaluate and refine AI-generated content before publishing.

Why AI Content Evaluation Is Becoming Essential

As AI writing tools become more common, the next evolution in content technology is not simply generating more text, but evaluating the quality of that text.

AI content evaluation focuses on analyzing whether a piece of writing:

sounds natural and human-like

is easy to read and understand

maintains a consistent tone

avoids excessive repetition

delivers clear value to the reader

By measuring these factors, content creators can improve their writing before publishing it.

This process allows teams to combine the speed of AI generation with the quality control needed to maintain professional standards.

In many modern content workflows, evaluation is becoming just as important as generation.

The Workflow of AI-Assisted Content Creation

A growing number of digital marketers and writers now follow a structured workflow when working with AI-generated content.

A typical process may look like this:

Generate a first draft using an AI writing tool

Review the structure and main ideas

Evaluate the naturalness and readability of the text

Improve clarity and tone

Publish the refined version

This approach ensures that AI is used as a productivity tool rather than a replacement for thoughtful writing.

It also helps maintain a balance between automation and authenticity.

The Role of Tools Like ScoreContent

As the need for AI content evaluation grows, new platforms are emerging to help analyze and improve AI-generated writing.

One example is ScoreContent, a platform designed specifically to analyze AI-generated text and provide insights into how natural and readable the content appears.

Through tools like https://scorecontent.com/
, creators can review AI-generated text and better understand how the writing might be perceived by readers.

Rather than guessing whether content sounds robotic or natural, evaluation platforms aim to provide structured feedback that helps writers refine their content more effectively.

These tools can be particularly valuable for:

SEO professionals

content marketing teams

bloggers and independent creators

agencies producing large volumes of content

By integrating evaluation tools into their workflow, creators can maintain high-quality writing even when using AI-assisted generation.

AI Content and SEO Considerations

Another reason AI content evaluation is important relates to search engine optimization.

Search engines increasingly prioritize content that delivers genuine value to users. While AI-generated text can help scale content production, publishing low-quality automated text may lead to poor engagement metrics.

Indicators such as:

time on page

reader engagement

clarity of information

overall readability

all contribute to how content performs online.

When AI-generated content is carefully reviewed and refined, it can perform just as well as traditionally written content.

However, this requires proper evaluation and editing.

Tools that analyze AI-generated text help ensure that content remains clear, helpful, and user-friendly before publication.

The Human Element in AI Content

Despite the incredible capabilities of artificial intelligence, the human element remains essential in content creation.

AI excels at generating language patterns and summarizing information, but it does not possess personal experience, emotional understanding, or creative intuition.

Human creators bring qualities that machines cannot fully replicate, such as:

storytelling ability

unique perspectives

cultural context

empathy and emotional tone

When AI-generated drafts are combined with human insight and refinement, the result can be powerful.

The goal is not to replace human creativity, but to enhance it.

The Future of AI Content Evaluation

As AI technology continues to evolve, the ecosystem around content creation will likely expand as well.

We can expect to see more tools focused on:

AI content analysis

readability scoring

tone evaluation

audience engagement prediction

automated content refinement

These innovations will help creators move beyond simple content generation toward a more sophisticated approach to AI-assisted publishing.

Platforms that focus on AI content evaluation may become a standard part of content workflows, much like grammar checkers and editing tools are today.

Final Thoughts

Artificial intelligence has opened a new chapter in the history of digital content creation. It has made writing faster, more accessible, and more scalable than ever before.

However, speed alone is not enough.

To truly benefit from AI-generated content, creators must also focus on quality, clarity, and authenticity.

Evaluating AI-generated writing ensures that content remains engaging, informative, and valuable for real audiences.

As the digital landscape continues to evolve, tools like ScoreContent highlight an important shift in the industry: the future of content is not just about generating text with AI, but about understanding and improving the quality of that text.

And in a world where millions of articles are published every day, maintaining high content quality will always remain a competitive advantage.
Morteza Riahi

How AI Is Reshaping Digital Marketing Workflows in 2026

Morteza Riahi — Sun, 08 Mar 2026 16:13:22 +0000

Artificial Intelligence is no longer a futuristic concept in digital marketing. It has become a practical layer embedded into everyday workflows—from keyword research to content optimization, campaign automation, and data analysis.

Yet many marketers still approach AI as if it were just another content generator. In reality, the most powerful use of AI in digital marketing is not replacing marketers, but augmenting their ability to analyze, experiment, and scale strategies faster than ever before.

For developers, founders, and growth marketers, understanding how AI fits into marketing workflows is becoming an essential skill. In this article, we’ll explore how AI is transforming digital marketing operations and how teams can integrate it into their processes effectively.

The Shift from Tools to Intelligent Systems

Digital marketing tools used to be static.

SEO platforms analyzed keywords.
Analytics platforms tracked performance.
Automation platforms scheduled campaigns.

But AI has changed this paradigm. Instead of isolated tools performing one task, we now see intelligent systems that connect data, analysis, and execution.

For example, modern AI-powered marketing systems can:

Analyze search trends and predict emerging topics

Generate content outlines aligned with search intent

Suggest internal linking structures

Identify technical SEO issues

Optimize campaigns in real time

This evolution shifts marketing from manual execution toward strategic orchestration.

The marketer’s role becomes less about producing every asset manually and more about designing workflows that leverage AI effectively.

AI and SEO: A New Era of Search Strategy

Search Engine Optimization has undergone one of the most dramatic transformations in the AI era.

Search engines themselves now rely heavily on machine learning models to interpret queries and evaluate content quality. As a result, traditional SEO tactics such as keyword stuffing or mass link building have become ineffective.

AI now plays a crucial role in several areas of SEO.

Search Intent Analysis

Understanding search intent is one of the most important ranking factors today. AI models can analyze large sets of search results to identify patterns in how Google interprets specific queries.

Instead of guessing the intent behind a keyword, marketers can now use AI to analyze:

Top ranking pages

Content structures

Semantic keyword relationships

User behavior signals

This allows SEO professionals to create content that aligns more accurately with what search engines expect.

Content Optimization at Scale

Creating optimized content used to require extensive manual research.

AI tools can now assist in:

Topic clustering

Semantic keyword mapping

Headline optimization

Readability improvements

Content gap analysis

However, successful SEO still requires human expertise. AI can suggest structures and patterns, but strategic positioning and unique insights remain human-driven.

AI-Powered Content Creation (Beyond Text Generation)

Many discussions about AI in marketing focus only on text generation. But the real transformation goes far beyond writing blog posts.

AI now supports multiple layers of content production.

Visual Content

AI image generation tools allow marketers to create custom visuals for:

Blog headers

Landing pages

social media posts

ad creatives

This dramatically reduces production time while maintaining brand consistency.

Video Production

AI video tools can generate scripts, voiceovers, animations, and editing suggestions. For YouTube creators and digital marketers, this enables rapid production of educational and promotional content.

Content Personalization

AI also enables dynamic content personalization. Instead of showing every visitor the same page, AI-driven systems can adapt content based on user behavior, interests, and traffic sources.

This increases engagement and conversion rates significantly.

Data Analysis and Predictive Marketing

Digital marketing produces massive amounts of data. Historically, extracting insights from that data required significant manual effort.

AI is changing that.

Machine learning models can now detect patterns in marketing data that humans might overlook.

Examples include:

Predicting which blog topics are likely to generate traffic

Identifying conversion bottlenecks

Forecasting campaign performance

Detecting unusual traffic behavior

Predictive marketing enables teams to make proactive decisions instead of reactive ones.

Instead of analyzing past performance, marketers can anticipate future opportunities.

Automating Marketing Workflows

One of the most powerful applications of AI is workflow automation.

Marketing teams often spend large amounts of time on repetitive tasks such as:

Keyword clustering

Content formatting

data reporting

email segmentation

ad campaign optimization

AI agents and automation tools can handle many of these processes automatically.

For example, an AI-driven workflow might look like this:

Identify trending topics in a niche

Generate a structured article outline

Suggest relevant internal links

Produce draft content

optimize the article for SEO

generate social media promotion text

track performance metrics

This kind of pipeline dramatically reduces production time and allows marketers to focus on higher-level strategy.

The Role of Developers in AI Marketing Systems

Developers play an increasingly important role in modern marketing teams.

AI-powered marketing infrastructure often requires technical implementation, including:

API integrations

automation pipelines

data pipelines

analytics dashboards

AI model integrations

For example, developers may build internal tools that connect:

search data

content systems

analytics platforms

AI models

These systems allow marketing teams to operate more efficiently and experiment faster.

In many organizations, the boundary between growth teams and engineering teams is becoming increasingly blurred.

Ethical Considerations and AI Content Quality

While AI offers enormous potential, it also raises important ethical and quality considerations.

Search engines are becoming increasingly sophisticated at detecting low-quality AI-generated content. Simply producing large volumes of generic AI text is unlikely to succeed in the long term.

Successful AI-assisted marketing focuses on:

original insights

human editing

expertise-driven content

transparency

value for readers

AI should be viewed as an assistant, not a replacement for expertise.

The most effective marketers combine AI efficiency with human creativity and strategic thinking.

Practical Strategies for Implementing AI in Marketing

For teams looking to adopt AI effectively, the key is starting with clear workflows.

Some practical strategies include:

Build AI-Assisted Content Pipelines

Instead of generating isolated articles, design structured workflows where AI supports research, outlines, editing, and optimization.

Use AI for Data Interpretation

AI can process large datasets quickly. Use it to identify patterns in analytics, traffic trends, and user behavior.

Automate Repetitive Tasks

Focus automation on tasks that consume time but do not require strategic thinking.

Maintain Human Editorial Control

Always review AI-generated content to ensure accuracy, originality, and brand consistency.

The Future of AI in Digital Marketing

Looking ahead, AI will continue to reshape digital marketing in several key ways.

We can expect:

more advanced marketing automation

deeper personalization

AI-driven growth experimentation

real-time campaign optimization

intelligent content ecosystems

Rather than replacing marketers, AI will create a new generation of AI-augmented professionals who combine technical literacy with strategic thinking.

Those who learn how to design effective AI-driven workflows will have a significant competitive advantage.

Final Thoughts

Artificial Intelligence is fundamentally changing how digital marketing operates.

From SEO and content creation to analytics and automation, AI enables marketers to work faster, analyze deeper, and scale strategies more efficiently.

However, success in the AI era does not come from relying on automation alone. It comes from combining human expertise with intelligent systems.

The marketers and developers who embrace this hybrid approach will define the next generation of digital growth.

Author

Morteza Riahi
Digital Marketing Strategist