Ai Use Cases – Rawafed Tech

Empowering Businesses with Artificial Intelligence

At Rawafed Tech, we enable organizations to harness the transformative power of Artificial Intelligence (AI). From predictive analytics to intelligent automation, our AI solutions are designed to optimize operations, enhance decision-making, and drive innovation across industries.

Key Benefits

Improved Decision-Making

Gain actionable insights with AI-driven analytics and forecasting.

Enhanced Efficiency

Automate time-consuming tasks, allowing your team to focus on strategic priorities.

Cost Savings

Reduce operational costs with optimized resource utilization and intelligent automation.

Personalized Customer Experiences

Use AI to deliver tailored recommendations and support.

Competitive Advantage

Stay ahead of the curve by adopting cutting-edge AI technologies.

AI Use Case Samples

A / E Intelligent Documentation

Process Brief:

Organizations have many English or Arabic Documents (ID, Iqama, Passports, Invoices). To utilize information in these documents without Intelligent solution, need to re-write these document on PC or input data to PC. Our solution capable to read types of documents mentioned above and transfer it to electronic version (English: 99.5 – 100 % accuracy, Arabic: 88%+)

Challenge

Reading Arabic Data with High Accuracy

Solution

Implement AI techniques and Programs to read documents (IDs, Iqama’s, Invoices) and convert to Electronic version.

Recommendation Engine

Process Brief:

Revenue growth: the main objective of upselling and cross-selling is to maximize the lifetime value of each customer/insured. By encouraging existing customers to purchase additional products, insurers aim to increase the number of possible sales and thus, future revenue.

Challenge

Offer the right product/Service to right customer

Solution

Implement AI Model(s) for practicing and studying current customers behaviors and segment them

Demand Forecasting

Process Brief:

Marketing and Sales team would like to predicting future sales by using historical data to make informed business decisions about everything from inventory planning and warehousing needs to running promotions and meeting customer expectations.

Challenge

business’s supply chain, its customers’ purchasing habits, and other factors are not well connected .

Solution

Implemented an AI model that connects a business’s supply chain, its customers’ purchasing habits, and other factors for a data-driven estimate of expected future sales.

Optimization models for production lines

Process Brief:

mix a few ingredients or products in order to maximize nutrition/revenue, minimize cost, and so on.
i.e. there are three ingredients:

•Limestone
• Corn
• Soybeans

Challenge

How many kilograms of each ingredient should you choose? What is the final cost?

Solution

Implemented an AI model that apply all restrictions related to all ingredients that required to produce final products with high quality and less Cost

Customer Complaint Management.

Process Brief:

Customer Service Team receives customer complaint logged by Level 1 support team, the complaints need to read, segregated and acted up on using defined SOP. As part of SOP service team need to analyze different data point available in CRM to reply to the customer query or escalate it to next level of service agent

Challenge

Customer Service Team receiving huge complaint calls. The received complaints could be repeated from same customer.

Solution

Implemented an AI model that detect why repeated complaints (wrong complaint assignment from the team, or engineers who fixing this complaints, or anything else

Predict maintenance and preventive machine

Process Brief:

Machines fails costing an organization money and sometimes their customers as failed machines may spend sometimes to get their spare parts and getting the qualified engineer for fixing it

Challenge

Reading historical data for all machines and detect reasons for its fails.

Solution

Implemented an AI model that combine al source of data related to machines either operational or related to historical fail then predict time of machine fail or reason for this fail.

Cross-Sell/Up-Sell

Process Brief:

As one of the marketing say, “getting new customers more expensive than keeping current customers”. So, we have a model that helps Banking and other sectors to nominate best product to each type of customers that belongs to their pattern and segement

Challenge

Which product or services could be offered to every segment of customers

Solution

Implement AI model that study retail customers behaviors and pattern then suggesting best product/services for each segment based on customer patterns.