Data & AI
Data Science Service
Unlock the full potential of your data with ELEKS' data science services. Our tailored solutions empower your business to make informed decisions, enhance operations, and gain valuable insights into customer behaviour, ensuring you deliver exceptional user experiences that boost acquisition and retention.
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Our Ńlient's success
Our clientās feedback about delivering a data management platform enhanced with computer vision and AI capabilities
Over a two-year period, the team at ELEKS created exactly what we needed, providing exceptional expertise, professionalism and project management throughout the process, testing, and launch. ELEKS succeeded in helping us improve our ability to trace the flow of recycled tyres while significantly reducing manual transactions and reporting. Thank you to the team at ELES for their professional, dedicated support in reaching this incredible milestone!
Melissa Carlaw
eTRacks, Director of Communications and Marketing
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Data science services
Data science services
we deliver
AI development services
Enhance productivity and cut operational costs by integrating artificial intelligence tools into your infrastructure and software systems. Our artificial intelligence development services help businesses streamline workflows, automate manual tasks, and boost customer satisfaction with generative AI solutions, intelligent virtual assistants and smart recommender systems.
Machine learning development
MLOps services
Intelligent automation
Case studies
Our data science customer
success stories
Improved data processing and operational analytics for a logistics enterprise
Improved data processing and operational analytics for a logistics enterprise
Enhanced resiliency and improved performance with Big Data infrastructure in the cloud
New machine learning models for day-to-day operating and customer insights
ELEKS assisted Aramex in optimising the performance of its Big Data storage by migrating it to the Azure cloud and implementing significant functional enhancements. After configuring the infrastructure, ELEKS successfully migrated existing machine learning models and introduced new ones to monitor customer ratings and streamline the delivery process in terms of time and effort.
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A comprehensive data management platform to manage recycling processes data from end to end
A comprehensive data management platform to manage recycling processes data from end to end
Adaptable and scalable solution enhanced with computer vision and AI capabilities
Successful market launch and adoption by service providers and producers
ELEKS undertook a comprehensive re-engineering of the eSRP (Sustainable Recovery Platform), an application in use by tire manufacturers and producers to fulfill their legislative obligations. Our team focused on enhancing traceability and scalability while integrating computer vision and AI capabilities to automate transactions and reporting, eliminating the need for manual processes.
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Providing data science expertise to help Edge AI startup mature and meet market demand
Providing data science expertise to help Edge AI startup mature and meet market demand
As Latent AI grew, it was able to leverage ELEKS' technical expertise and adaptability
ELEKS has delivered niche skills to a prominent edge AI tech startup
ELEKS supported Latent AI's resourcing needs in the AI field throughout their growth, enabling the rapid development, testing, and deployment of features of their MLOps SDK designed to optimise and secure AI models and runtimes to meet market demands and onboard new customers.
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01
Problem identification and project planning
Data scientists work closely with your stakeholders to identify your business challenges and define project objectives. We define project scope, timelines, and resource requirements, ensuring alignment with your data strategy and goals.
02
Data collection and exploratory data analysis
We collect relevant data from various data sources, ensuring its quality and integrity. Through exploratory data analysis, our data engineers identify patterns, relationships, and potential insights that will inform subsequent steps in the data science project.
03
Data preparation and preprocessing
Our data engineering expert focuses on cleaning and preprocessing raw data to ensure it is suitable for analysis. This involves addressing missing values and conducting feature engineering. We split the data into training and testing sets to evaluate model performance accurately.
04
Data model development and training
We select machine learning algorithms based on the nature of the problem and available data. Then, our data scientist trains multiple models, fine-tuning them for optimal results by adjusting model parameters and assessing performance against predefined metrics.
05
Model evaluation and testing
Once trained, the models are evaluated using the testing data to assess their performance. We compare different models based on various metrics to choose the best-performing model for deployment.
06
Model deployment
We integrate the final solution into your production environment. The process may involve building APIs or deploying the model within your existing software systems. We perform rigorous testing to ensure seamless integration and functionality.
07
Model monitoring and maintenance
Our data scientists will monitor the model's performance in real-world scenarios, tracking key metrics and collecting feedback to identify issues. As part of ongoing maintenance, we can update the model with new data and retrain it to ensure optimal performance.
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What we'll take care of:
- Creating your analysis and data modelling roadmap.
- Developing predictive modules.
- Acquiring access to any required assets.
- Creating optimisation/factor analysis modules.
- Creating an initial hypothesis based on data understanding and requirements.
- Collating insights and then validating and interpreting the model's performance.
- Information retrieval and insights extraction from data.
- Developing technical and graphical descriptions/report(s).
- Data usage and transformation consulting.
- Efficient module deployment (ready-to-use solution).
- Designing a descriptive analysis module.
Industry-specific use cases for
data science services
Healthcare
Retail
Media and entertainment
Finance
Insurance
Logistics
Manufacturing
Automotive
Oil, gas and mining
Healthcare
- Deep learning for medical imaging solutions
- Predictive healthcare
- Data analytics for drug discovery and development
- Virtual health assistants and chatbots
- Machine learning for personalised treatments and diet plans
Retail
- Intelligent recommender systems
- Demand forecasting models
- Visual search, object detection and facial recognition systems
- Data-driven dynamic pricing, discounts and rewards
- Chatbots and virtual assistants
- Computer vision for quality control and fraud prevention
Media and entertainment
- Content recommendations and personalisation
- Content creation with generative AI
- Advanced audience analytics and insights
- Personalised marketing campaigns
- Content moderation and copyright protection
Finance
- RPA tools for fraud detection and prevention
- Data-driven risk assessment and management
- Business intelligence solutions
- Machine learning for personalised financial planning
- Portfolio optimisation solutions
- Compliance and regulatory reporting automation
Insurance
- Data science for underwriting automation
- Automated claims processing and fraud detection
- Data analytics for risk assessment and loss prevention
- Virtual assistants using natural language processing
Logistics
- Dynamic route planning and fleet management
- Predictive maintenance for vehicles and equipment
- Intelligent inventory management and demand forecasting
- Software robots for warehouse automation
- Solutions for last-mile delivery optimisation
Manufacturing
- Predictive maintenance and automated defect detection
- Supply chain optimisation solutions
- Robotic Process Automation (RPA)
- Data-driven energy management and optimisation
- Generative AI for product design
Automotive
- Autonomous vehicles powered by neural networks
- Smart manufacturing and robotics
- Predictive maintenance for vehicles
- In-car infotainment and smart user experience
- Vehicle safety and Advanced Driver-Assistance Systems (ADAS)
Oil, gas and mining
- Predictive equipment maintenance
- Asset integrity management
- Data-driven production optimisation and reservoir management
- Machine learning for safety monitoring and incident prevention
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Methods and techniques we apply
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Descriptive analyticsWe help you interpret historical data to gain valuable insights that you can use to enhance your business intelligence, aid decision-making processes, and optimise your business value chain. Descriptive analytics involves extracting insights, pre-processing and evaluating anomalies, central tendency analysis and variance. We also evaluate other key statistical moments such as distributions, frequency, dependency, and factor analysis.
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Predictive analyticsUsing predictive analytics, businesses can focus their efforts and investments where they're likely to get the best ROI. You can apply predictive modelling to qualify leads, predict market demand, and manage risks. Our data science team will validate your model and provide a first-time baseline, then fine-tune and retrain it for optimal results. We also measure performance and confidence intervals to ensure you get the most from your modelled data.
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Prescriptive analyticsPrescriptive analytics helps you make decisions using probability-weighted projections rather than hypotheses. Our data engineers will help you obtain insights from your modelled data and provide recommendations on how to leverage them. By using predictive analytics outcomes, prescriptive analytics provides solid evidence to support future actions and strategies. This is vital in industries like finance, government, and healthcare, where human errors are costly.
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Machine learning algorithmsWe use different types of machine learning algorithms depending on your needs. Supervised learning algorithms are best suited for classification and regression tasks, such as medical imaging analysis or stock price predictions in trading. Unsupervised learning algorithms can be used for clustering tasks, like customer segmentation in retail. Reinforcement learning methods are applied to aid decision-making processes as well as to develop personalization engines.
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Neural networks and deep learningWe leverage various neural networks and deep learning techniques, including CNNs and RNNs like LSTM and GRU, for tasks such as natural language processing (NLP) and sequential data analysis. Additionally, we employ autoencoders for medical image analysis and GANs for generating images to train ML algorithms. DQN is used for cases like energy consumption optimization and smart manufacturing, while Bayesian deep learning enhances speech recognition and translation accuracy.
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Process optimisation and automationWith our expert data science consulting services, you can implement machine learning algorithms and advanced analytics models to streamline repeatable processes with intelligent automation ā for example, call centre workload optimisation. You will benefit from enhanced controls and data management, enabling better decision-making. You can also measure and manage operational performance efficiently, optimising your time, resources and budget.
Technologies we use
Article
Edge Computing for Industry 5.0: Enabling Next-Generation Industrial Intelligence
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R&D
Generative AI in Healthcare: Solving Medical Staff Performance Issues
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Article
AI in Clinical Trials: Improving Patient Recruitment and Retention
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R&D
AI Code Review: Can Technologies Really Ensure Code Quality?
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R&D
Tackling Power Grid Challenges: ELEKS’ AI-Supported Solution for Power Infrastructure Mapping
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Article
AI Voiceover vs. Human Talent ā Choose Who Will Speak For You
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R&D
Addressing Healthcare Data Management Challenges with Technology
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R&D
Expert Insights on Generative AI: Evolution, Challenges, and Future Trends
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