Retrieval-Augmented Generation (RAG) is a technology that is changing the way companies approach the use of artificial intelligence. By combining large language models with current, verified data, RAG enables faster, more accurate, and fact-based decision-making. It is no surprise that more and more organizations - from FoodTech to fintech to the public sector - are looking for partners capable of creating enterprise-level RAG solutions.
Enterprises are currently generating more data than ever before - from POS and ERP systems to CRM, loyalty apps, and supply chain platforms - yet executives are finding it increasingly difficult to make the right decisions at the right time. Instead of clarity, data silos, delayed reports, and fragmented dashboards slow down the process, forcing executives to rely on intuition when they should be guided by facts. This paradox costs companies millions in lost opportunities, wasted resources, and lost margins - and it is this problem that I will describe in detail in this article.
Many restaurants in the GCC region are currently facing a unique opportunity, but also a serious challenge. The dynamic growth of the delivery market, rising customer expectations, and cost pressures mean that even a few percentage points of margin loss can determine the competitiveness of the entire business. Many owners and C-level managers recognize that traditional POS systems and analytical reports are not keeping pace with the rate of change. Data is scattered, decisions are made with delays, and inaccurate forecasts lead to waste, missed revenue, and higher labor costs.
Since OpenAI launched GPT-4, the business world has been closely watching every subsequent update. Most C-level managers realize that artificial intelligence is no longer a futuristic curiosity, but a real tool that can revolutionize how a company operates. Today, we are on the verge of another, even bigger change.
Every night, restaurants across the globe discard thousands of dollars worth of perfectly good food while simultaneously running out of popular menu items. This paradox has plagued the restaurant industry for decades, contributing to the staggering $162 billion in annual food waste. However, artificial intelligence (AI) is rewriting this story, transforming how restaurants approach inventory management and turning operational chaos into precision-driven profitability.
Do you have a groundbreaking app idea but feel lost in the process of bringing it to life? Are you overwhelmed by the sheer number of mobile app development companies out there, all promising to be the "best"? The last thing you want is to choose the wrong partner, waste your budget, and end up with a low-quality product. You're not alone in this challenge.
Do you run a restaurant and feel like you're constantly fixing mistakes instead of growing your business? Are you lost in piles of receipts, staff working hours, and never know how much stock you have in your warehouse? Or maybe you just want to increase your profits, but have no idea where your money is going?
The speed, reliability and functionality requirements of modern applications exceed the standards of traditional system design. Microservices are becoming the foundation of modern applications, enabling flexible, error-proof and easy-to-develop technology environments. This often raises questions about how to design such an architecture that is highly scalable, stable and future-proof. This publication will help you learn the techniques, principles and tools that will enable you to build a complex microservices architecture. If you are interested, here you will find everything you need to know about this solution.
Managing a restaurant has become a strategic challenge that reaches far beyond the kitchen. Rising ingredient costs, staff shortages, and evolving guest expectations mean that restaurateurs must master logistics, data, and experience design - all at once. This article explores how integrated restaurant management software are reshaping operations, transforming them from fragmented routines into scalable, insight-driven engines of growth.
The excitement about generative artificial intelligence (GenAI) is still intense. From creating content and summarizing complex documents, to generating code and creating complex ideas. Large language models (LLMs) are a driving force for many industries. But despite so many impressive advantages, hallucinatory challenges can be seen. These are cases. in which GenAI models confidently present incorrect, misleading or completely fabricated information as fact. For companies that want to rely on artificial intelligence for critical operations, this represents a breach of trust.
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