Artificial Intelligence
Artificial Intelligence Solutions discover the power flux of Ai Technology driving your business upward and rise above and beyond your rival companies’ reach.
What Makes Rayi System a Reliable AI Business Consultant?
- Since 2015, we have specialised in data science and data analytics.
- Since 2015, we have provided business intelligence and data warehousing services.
- Big data services have been available since 2015.
- Since 2015, we have provided image analysis consulting and development services.
- Expertise in more than 10 industries, including manufacturing, energy, retail and wholesale, professional services, healthcare, financial services, and telecommunications.
Rayi system AI Consulting Service Scope
Rayi system’s AI consulting services vary in scope depending on our customer’s business needs, the maturity of data management practices, and the current AI environment (if any). Among our AI services are:
1. Business Analysis
A business definition and analysis must be pursued with an AI solution.
Examination of the current AI environment (if any).
An examination of the company's current data management practices, as well as the technologies and tools in use.
2. AI solution conceptualization and project planning
Outlining AI strategy and roadmap.
Designing the AI solution's architecture and developing the best tech stack for the AI project.
Choosing AI solution deliverables and KPIs.
AI solution implementation planning, including cost and timeline estimates, risk management strategy outline, and so on.
Proof-of-concept AI solution delivery (in case of complex projects).
3. Data management and preparation
Examination of the available data sources (data type, volume, etc.).
For the purpose of training (re-training) ML models, data must be gathered and cleansed (standardising, substituting missing and deviating variables, and anonymizing sensitive data).
4. AI solution development (ML modelling)
Research and improvement of ML models.
Testing and assessment of ML.
ML model settings must be adjusted until the output is satisfactory.
ML model deployment.
5. Integration of ML output
Creating modules for applications that consume ML output.
Integration of the AI solution with the ML modules for feeding the models and using the result.
6. Quality control of AI solutions
Testing an entire AI system.
Assessing the output quality of AI solutions in accordance with established KPIs.
7. Support and optimization of AI solutions
Constant monitoring and improvement of an AI solution.
Including additional data sources to feed the AI system for increased precision and understanding.
Creating new ML models to address the recently arose business requirements.
Rayi System's AI Solutions
Increased efficiency through automation, improved decision-making, and enhanced customer experiences are the top 3 AI solutions, according to PwC. The Rayi System’s solutions are consistent with the overall picture:
Inventory management and demand forecasting
- Calculating the risks of both an oversupply and a shortage.
- Forecasting of demand and sales.
Benefits include: Balanced supply and demand, and reduced supply chain risks.
Both preventative and curative maintenance
- The detection of unusual activity in machinery and equipment.
- Estimating the likelihood of failure over time or in a set number of steps.
- Estimating the usable lifetime that remains.
Benefits include: Increased asset lifetime and uptime, decreased maintenance and repair expenses, and increased OEE and productivity.
Customer support
- Agents for virtual customers.
- AI-powered suggestions for the optimal course of action for human agents.
- Text-to-speech conversion.
Benefits include: Higher customer request resolution rates, decreased labor expenses for customer support, and greater customer loyalty.
Financial risk evaluation
- Calculating and keeping track of financial risks using data on customer, operational, geopolitical, and other hazards.
- Forecasting financial risk.
- Analyses that are prescriptive to reduce financial risks.
Benefits include: Reduced financial losses and increased profitability for businesses.
Engines for personalization
- Segmenting customers.
- Recommendations for upselling and cross-selling.
- Automated creation of personalised information, including product recommendations, discounts, and other offers.
Benefits include: Automated marketing initiatives, higher average order values and conversion rates, less cart and browse abandonments, etc.
Application of IoT
- Items in the Internet of Medicine.
- software for simulating industrial robots.
- consumer goods with connectivity.
- Intelligent cities, etc.
Benefits include: Improved operational effectiveness, automatic remote control, the ability to track objects, etc.
Visual computing
- Recognition of emotions and faces.
- Regulation of product quality (checking for surface defects, discoloration, absence of components, etc.).
- Diagnostic testing with computer assistance.
- Reconstruction in 3D, etc.
Benefits include: Savings in money and time as a result of the automated processing of a lot of visual data.
Rayi Systems Approach to Artificial Intelligence Consultancy
Data security
We use the three-pillar method for efficient data security management when creating AI-powered solutions:
- Transparency – Only with users’ permission are personal data collected and processed.
- Highly secured – Rayi system integrates security principles into the development processes to create a protected environment for data processing and storage (DevSecOps).
- Compliance – AI-powered solutions that have been offered are entirely compliant with regulatory and industry standards (HIPAA, GLBA, GDPR, etc.).
Data integrity
We work with qualified data engineers and data scientists who use a variety of tools to automate data validation, cleansing, and reduplication procedures in order to enable high data quality.
The value of the AI solution is assured
To guarantee the viability of your future AI solution, the quickness and quality of insights powered by AI, we:
- A proof of concept is a good place to start an AI project to show how the solution will operate, determine its potential value, raise issues, etc.
- Combining white-box AI models, which include comprehensible links between input variables and output predictions, and black-box AI models based on deep learning algorithms, which are not comprehensible on their own, can balance the system’s transparency with the accuracy of the output.
- Compare the generated solution’s viability and quality to the agreed-upon quality KPIs.