Axiom Analytics

At Axiom Analytics
we forge competitive advantage for our customers.


by helping them to leverage their data assets, enabling them to become smarter and data driven. We deliver innovative solutions that drive real business value and create exponential growth with cutting edge AI technology, via three service lines


Strategic Advisory To create and design your AI roadmap & strategy
Consulting & Execution To design & implement AI solutions.
Managed Services To ensure ongoing, operational excellence
 

How can artificial intelligence help?

 
 

Axiom Analytics Packages

To accelerate AI inside your business we provide 4 packages:.

Readiness Assessment

Do you want to find out how ready your business is for artificial intelligence?.

In our AI assessment we will perform a comprehensive review of the facets needed to build successful, ROI driven AI systems. At the end, you will receive recommendations on how to accelerate the adoption of AI inside your business and a roadmap to successful enterprise AI..

 

Jumpstart

Are you looking to pilot an Artificial Intelligence program or solution inside your company?

With our jump-start package, we’ll complete a tailored assessment of your business to identify how Artificial Intelligence can be leveraged to add value to your organization. At the end of the assessment, we’ll help you to build a proof of concept, so you can see how much Return on Investment AI can deliver to your business.

Adoption Program

Are you worried that your AI programs will struggle to get traction?

Our AI adoption program works with your business and IT stakeholders to make sure that your AI initiatives are a success. The program sets out a clear roadmap, schedule and objectives that need to be achieved. The program includes our readiness assessment as well as training and support for any solutions developed during the program.

 

Project

Are you looking to start an AI project inside your organization?

With our consulting services we will be able to work with your business stakeholders to scope out the solution that will deliver the highest impact, decide on the best technologies for your organization and then build a fully functional production system that fits in seamlessly with your business processes.

The age of Artificial Intelligence is now


The growing adoption of machine learning and artificial intelligence by businesses has triggered organizations to think about how they can build competitive advantage and prevent disruption in the age of artificial intelligence. Organizations should adopt now, rather than later, so that they can experiment with and validate use cases and as well as build familiarity with AI. How will your company like many others, realize the value of machine learning and artificial intelligence?.


 

The Data Science Success Framework

All our project work is delivered using our unique Data Science Success Framework and Accelerate Project Delivery frameworks which are designed to make sure that your AI project is delivered on time, generates real business value and follows data science and machine learning best practice.

 

Why partners choose Axiom ?

 

By Industry


Creating data-driven solutions for whole industries

 
The retail industry presents itself as one of the largest value opportunities for AI and machine learning. Major areas in which AI can be best implemented include pricing optimization, inventory optimization, promotional forecasting, customer acquisition,and customer service management. In addition, a large number of leading retailers are implementing hyper-personalized recommendation systems through unsupervised learning techniques and deep learning.
A lot of attention has been given to computer vision and spatial temporal models in autonomous vehicles; however, the application for AI and machine learning in the automotive industry is far wider. In supply chain management, machine learning models can be used to optimize energy usage, yield, procurement, and inventory while considering throughput targets and other constraints. Advanced AI systems can be further applied to customer service management, churn prediction, and customer acquisitions in sales and marketing operations
The education industry presents a wide array of powerful applications for AI and machine learning. One use case is for AI to deliver personalized curricula and content to the individual strengths and challenges of each student, which can further be refined by learning from the student themselves. Machine learning techniques can be used to examine student performance and identify key opportunities in which learning can be supplemented. Further, improvements in recurrent neural networks and other sequence processing models can be used to develop unbiased and consistent grading systems that scale beyond the typical classroom.
Opportunities in which AI and machine learning can be applied are prevalent in healthcare, from operational and organizational systems, diagnostics and testing, devices and pharmaceuticals. Business problems in healthcare services include workforce productivity and efficiency, predictive modelling, and fraud analytics. Moreover, solutions targeting marketing budget allocation, channel management, product feature optimization, and demand forecasting generate the biggest value proposition for pharmaceutical and medical product companies.
Early adopters in the finance industry are at the forefront of applying state-of-the-art AI and machine learning solutions to generate competitive advantage. Given the range of functions which finance encompasses, these solutions can optimize value across business areas. Back office functions of fraud detection, underwriting valuation, insurance, risk management and hedging, and asset management can benefit from a scalable AI data-driven approach. Front office functions such as lead generation, customer interaction, personalized advice, and budgeting are ready for disruptive models based on AI and machine learning solutions. Recent examples include major banks’ interest in adopting sequence-to-sequence models to drive customer engagement through chatbots and robot-advisors.
Given the prevalence of sophisticated robotics and mechanization, AI and machine learning are natural extensions of an overall trend to greater automation in the manufacturing industry. Leading predictive models are easily applied to yield, energy, and manufacturing throughput analysis. Likewise, these models can be subsequently reframed for inventory and resource optimization. In addition, large leaps in computer vision through convolutional neural networks has enabled accuracy rates that surpass humans in object detection and identification – leading to new areas of application such as the automated monitoring of safety gear usage and working conditions.
 

By Business Function


Data-driven solutions for each area in your organization

 
Sales
Sales is an area in which machine learning and AI solutions can be readily applied. Demand forecasting enables the optimization of inventory to minimize storage and provisioning costs and the opportunity costs of lost sales and profits. In addition, customer segmentation models and recommendation engines facilitate hyper-personalization of targeted marketing for cross-selling, up-selling, and enhanced customer engagement.
Marketing
The marketing business function contains ample opportunities for machine learning systems. Algorithmic marketing enables the optimization of pricing strategies in accordance to market fluctuations and the expectation of competitor products and actions. Moreover, life-cycle value analysis and RFM (recency, frequency, monetary) analysis facilitates a greater holistic understanding and targeting of high value customers. Lastly, sentiment analysis evaluates customer engagement and the reception of various marketing strategies.
Customer Service
Machine learning and AI systems can be effectively applied to the customer service business function. Natural language programming models are applied to monitor customer satisfaction across call Centres and survey responses. Emotion and sentiment analyses are able to capture changes in customer mood throughout an interaction with a customer service representative. Call classification can also be applied to automatically delegate incoming calls to staff based on their expertise and strengths.
Operations
The operations business function is currently being disrupted by machine learning and AI. Inventory and supply chain optimization algorithms are able to automate and optimize the inventory management process to minimize loss of profits and storage costs. In addition, predictive maintenance models are able to forecast unscheduled equipment downtime based on historical patterns – in effect reducing downtime, maintenance costs, and increasing operational efficiency.
Finance
Machine learning and AI systems are most commonly used in the finance business function in order to forecast future expenditure for budget allocation and prediction. Fast Fourier transformations are able to generate stable approximations of even the most complex seasonality patterns while recurrent neural networks capture non-linear asymmetric cyclic patterns. However, with the advancement of sophisticated algorithms and computational power, anomaly detection systems are able to traverse internal financial databases, proactively searching for fraudulent or abnormal financial behavior – automatically notifying the financial department when abnormalities are discovered.
HR
The human resources space has large potential for application of AI and machine learning techniques to better improve search and operational efficiencies. Performance and satisfaction management systems are able to monitor employee engagement and satisfaction, minimizing churn while providing quantifiable performance metrics. Moreover, attrition models are able to forecast expected employee turnover by department and seniority. Most prominently, however, are the use of natural language programming models to conduct resume screening processes with speed and impartiality that are unattainable by human reviewers.
Product development
Integrating AI into your software or service product can make a significant impact on the ability of your systems to provide synergistic selling, sales and preference tracking, and gain a deep understanding of your user base’s motivations and purchasing behavior. Machine learning and AI also advances the process of physical product development to new unseen territories. State-of-the-art methods facilitate automatic product development systems that generate design patterns optimized for chosen objectives such as aerodynamic resistance or ergonomics. Furthermore, ML-based systems are able to isolate the design features and attributes that generate the greatest customer satisfaction and usage – guiding the focus of the product development team.
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