Data processing is the crucial first step in transforming semi- and unstructured information such as diverse spreadsheet formats, PDF documents, and similar data types into a machine-readable format.

This step is essential for systematic data storage in structured platforms, forming the foundation for downstream data analytics and machine learning applications.

We design flexible, scalable, and automated ETL (Extract, Transform, Load) pipelines that enable seamless data integration. Our solutions ensure efficient data ingestion, transformation, and storage, supporting a wide range of business and research applications.

A Data Platforms provides a robust, scalable, and efficient foundation for managing, integrating, and analyzing data across various sources.

A well-structured data platform enables organizations to store, process, and access their data seamlessly, supporting advanced analytics, artificial intelligence, and business intelligence applications.

Our data platform building efforts ensure secure, compliant, and high performance data processing tailored to your specific needs, enabling data-driven decision-making at scale.

In today's data-driven world, analytics plays a crucial role in helping organizations unlock valuable insights and drive strategic decision-making.

It is the systematic analysis of data to uncover patterns, trends, and insights. This process includes collecting, processing, and interpreting data to enhance decision-making and boost efficiency.

It can be categorized into descriptive (what happened), diagnostic (why it happened), predictive (what might happen), and prescriptive (what should be done) analytics.

By leveraging statistical methods, machine learning, and data visualization, analytics transforms raw data into actionable knowledge. This enables you and your organisation to make informed decisions based on data-driven insights.

Data processing is the crucial first step in transforming unstructured information—such as diverse tabular formats (e.g., Excel files), PDF documents, and other data sources—into a machine-readable format.

This step is essential for systematic data storage in structured platforms, forming the foundation for downstream data analytics and machine learning applications.

We design flexible, scalable, and automated ETL (Extract, Transform, Load) pipelines that enable seamless data integration. Our solutions ensure efficient data ingestion, transformation, and storage, supporting a wide range of business and research applications.

Machine Learning Operations (MLOps) integrates machine learning model development with production deployment, ensuring seamless transitions from experimentation to live inference environments.

With our expertise in data science and DevOps engineering, we enable automated model deployment, implement continuous monitoring, and provide ongoing maintenance.

Our structured approach ensures that machine learning models remain robust, scalable, and well-optimized to meet evolving business needs in a dynamic, data-driven landscape.

Explainable AI (XAI) is a subfield of AI that enhances transparency, trust, and accountability in AI-driven decision-making.

By making AI models interpretable, organizations can ensure regulatory compliance, improve user trust, and mitigate biases in automated systems.

Our Explainable AI solutions empower businesses with clear, actionable insights into their AI systems, enabling responsible deployment in critical applications such as healthcare, finance, and enterprise automation.

Machine Learning encompasses the development, training, and deployment of predictive models that leverage structured data to drive actionable insights.

At CORE64, we apply statistical and machine-learning techniques — both, supervised and unsupervised, and feature engineering to optimize decision-making processes across diverse industries.

Our solutions are designed for efficiency, interpretability, and seamless integration into existing business workflows, ensuring that our clients harness the full potential of their data assets.

Large Language Models (LLMs) or Foundation Models are advanced AI models built to comprehend and produce human language. With increasing scale, their emerging capabilities range from analyzing structured and unstructured text, generalised knowledge, (limited) reasoning and logical deduction, generating coherent responses, and handling various language tasks. 

In business applications, these models are pivotal in many areas. For example, LLMs enable companies to derive insights from extensive text data and applications range from content creation such as copywriting to zero-shot capabilities for image, text- and language applications. 

Most prominent stands Retrieval Augmented Generation (RAG), designed to retrieve contextually relevant information and generate accurate, actionable responses. A novel derivative of this technology are Agentic systems, engineered to operate autonomously and iteratively, with built-in feedback loops for self-correction and adaptability.

In many cases, the full potential of AI systems is realized only when users can interact with them effortlessly and efficiently.

Our Human-Machine Interaction (HMI) Suite focuses on designing intuitive interfaces that facilitate seamless communication between users and intelligent systems. Our HMI solutions aim to enhance user experience, reduce operational errors, and improve overall system efficiency.​

We emphasize a human-in-the-loop approach, ensuring that human attention is directed where it matters most. This strategy maximizes human engagement in critical areas while minimizing fatigue from excessive signals and distractions.

Intelligent Automation (IA) merges AI, machine learning, and robotic process automation (RPA) to tackle complex tasks that go beyond repetitive workflows.

Unlike traditional automation, IA adapts to changing environments, making decisions and learning from data. It solves challenges like processing unstructured data, managing exceptions, and evolving business needs.

The result? Enhanced efficiency, fewer errors, scalable solutions, allowing human teams to focus on tasks beyond automation. By blending precision with adaptability, IA drives smarter operations where rigid systems can’t compete.