Turn challenges into opportunities by combining human expertise with generative AI and advanced data science.

Data Layer :: Lay the right foundation

[Data Processing]

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 ofof business and research applications.

  • Our expertise in data processing spans from building ETL pipelines to processing and integrating tabular data, images, sequential data, and textual documents into unified data platforms. We work with both small-scale structured data and large-scale big data sources, ensuring optimal performance and reliability.

    Key Areas of Experience:

    Developing data processing pipelines for:

    • Building and managing data platforms for both SQL-based and NoSQL databases.

    • Creating data dashboards for analytics, reporting, and real-time monitoring.

    • Data pre-processing for diverse machine learning applications, such as biomarker discovery, protein prediction, or process optimization.

    Ensuring flexibility, robustness, scalability, and flexibility by:

    • Modular software engineering & microservices design architecture for flexible and rapid prototyping.

    • Test-guided development (TGD), implementing unit, integration, and system (end-to-end) tests using functional and property-based testing principles.

    • Fast and robust deployment with CI/CD pipelines leveraging GitHub Actions. 

    • Utilizing industry-standard open-source Python-based libraries for scalable and automated ETL workflows, including Apache Spark and Apache Airflow.

    Our solutions prioritize efficiency, automation, and scalability, enabling businesses and researchers to seamlessly transform raw data into actionable insights.

[Analytics]

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.

  • We have extensive experience in designing and implementing analytics solutions tailored to diverse business needs. Our expertise spans across data engineering, statistical analysis, and advanced machine learning techniques to provide robust analytics solutions.

    Key Areas of Experience:

    • Development of interactive dashboards for process history analysis, enabling businesses to identify inefficiencies and streamline operations.

    • Dynamic reporting solutions that provide real-time visibility into key business operations, empowering informed decision-making.

    • Implementation of top-down and bottom-up KPI dashboards using industry-standard BI tools like Power BI and Tableau, ensuring seamless integration with data warehouses.

    • Application of statistical modelling and machine learning to predict trends, optimise business strategies, and support demand planning.

    • Application of clustering and classification techniques to understand customer behaviour, improve targeting, and enhance user experiences.

    • Monitoring data distribution shifts over time to detect inconsistencies, prevent performance degradation, and improve model reliability.

    • Designing controlled experiments (A/B Testing & Experimentation) to validate business decisions, optimise marketing strategies, and drive continuous improvement.


    Let us help you transform your business with data-driven insights. Contact us today to explore how our analytics expertise can optimize your strategies and drive measurable growth.

[Data Platform]

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 solutions ensure secure, compliant, and high-performance data ecosystems tailored to your specific needs, enabling data-driven decision-making at scale.

  • We specialize in designing, developing, and maintaining modern data platforms that support enterprise data strategy, optimize workflows, and enhance data accessibility. Our expertise includes database design, cloud data infrastructure, data warehousing, and big data processing frameworks.

    Key Areas of Experience:

    • Designing and implementing scalable data platforms for structured and unstructured data.

    • Cloud-based data infrastructure using AWS, Azure, and Google Cloud.

    • Data warehousing solutions for efficient storage and retrieval (Snowflake, Redshift, BigQuery).

    • Real-time and batch data processing using Apache Spark, Kafka, and Flink.

    • Secure and compliant data governance, including GDPR and HIPAA compliance.

    • Integration of diverse data sources, including IoT, APIs, and external datasets.

    • Data cataloging and metadata management to enhance discoverability and usability.

    • Automating data workflows using Airflow, Prefect, and dbt for streamlined data pipelines.

    Let us help you build your scalable, secure, and robust Data Platform, integrating data from diverse sources into a unified and consistent infrastructure.

[DataOps]

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.

  • Our approach is centered on leveraging modern automation, tools and best practices to ensure that your data processes remain efficient, secure, and compliant.

    With a commitment to operational excellence and innovation, we help organisations to adopt DataOps in their operations to transform their data workflows into dynamic, resilient systems that support rapid business growth.

    Key Areas of Experience:

    • Fostering collaboration of data engineering and upstream source system teams to establish (internal) “data contracts” including Service Level Agreements (SLA), Service Level Objectives (SLO) and Service Level Indicators (SLI).

    • Automation of (cloud) infrastructure resource provisioning by the means of Infrastructure-as-Code tools such as Terraform.

    • Utilising data quality frameworks such as Great Expectations to evaluate and monitor key data quality metrics, such as data freshness, expected volume and data anomaly checks, e.g., column-based value range and null checks.


    Let us help you transform your business with DataOps to improve your data lifecycle management. Contact us today for a consultation, and let’s explore how we can enhance collaboration, automate your data pipelines and ensure data quality to your specific needs. Get in touch today to learn more!

Ai Layer :: Build cutting-edge capabilities

[ML/LLMOps]

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.

  • We have extensive experience in designing, deploying, and maintaining machine learning solutions across various industries. Our expertise covers the entire ML lifecycle, from data preparation and model development to scalable deployment and continuous monitoring.

    Key Areas of Experience:

    • Managing the full lifecycle of machine learning workloads, from exploratory data analysis and data preparation to model training, deployment, monitoring, and maintenance.

    • Containerizing machine learning workloads using Docker for gradient-boosted trees (Credit Card Fraud Detection, Lifetime Value Estimation), NLP classifiers (Natural User Messages), and image classifiers (User Image Content Filtering).

    • Deploying models with Seldon Core and TensorFlow Serving for scalable and efficient inference.

    • Supporting production deployment of both TensorFlow and PyTorch models.

    • Integrating models into enterprise infrastructure using Kubernetes with failover handling and autoscaling.

    • Implementing monitoring and drift detection by integrating tracking layers with Google BigQuery and enabling periodic retraining.

    • Automating the indexing and query pipeline setup for optimized search and retrieval performance.

    • Integrating Qdrant for vector search, Open WebUI for chatbot interfaces, and Open WebUI Pipelines for backend workflows.

    • Enhancing observability and model evaluation with LangSmith for structured performance tracking.

    • Automating machine learning workflows and deployments with CI/CD pipelines using GitHub Actions.

[Machine Learning]

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.

  • With extensive experience in traditional machine learning paradigms, we specialize in:

    • Regression and classification models for forecasting and pattern recognition.

    • Anomaly detection for risk assessment and fraud prevention.

    • Time-series analysis for predictive analytics in finance, healthcare, and energy.

    • Feature engineering and dimensionality reduction to enhance model performance.

    • Model interpretability and explainability to support regulatory compliance and stakeholder trust.

    • Deployment of scalable ML solutions, integrating seamlessly with cloud and on-premise environments.

    Our expertise allows us to tailor solutions to the unique challenges of each client, ensuring practical and effective machine learning applications that drive measurable impact.

    Key Areas of Experience:

    • Developing models for both classification and regression tasks. For instance, we've implemented lifetime-value prediction models to optimize marketing spending and designed classifiers to detect credit card fraud, serving as a first line of defense for payment service providers.

    • We manage all aspects of data preprocessing, feature engineering, and feature selection. This includes enriching existing data sources to uncover the most significant features, ensuring that your machine learning models are built on a solid foundation.

    • Our experts are adept at utilizing a wide array of industry-standard tools. For example, we leverage Prodigy to efficiently tackle natural language processing tasks and employ Hugging Face to access the latest and most performant image and text embedding models.

    • Besides industry use cases, we have applied ML to scientific applications such as Biomarker discovery, Protein function prediction. 

    • We have extensive experience with Distributed systems (e.g. Federated Learning, Data- and Model-Parallel Training setups).

    • We believe that predictions should be accompanied by calibrated probabilities. Our standard toolchain includes methods for assessing and calibrating uncertainties, enabling decision-making processes under uncertainty and providing classifiers and regressors with rejection options when necessary.

    • Recognizing that ML models can become less accurate over time due to distribution shifts, we assess the robustness of our systems against data drift. We develop roadmaps for continual or retraining strategies to ensure the continuous reliability of our models in production environments.

[Explainable AI]

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.

  • We specialize in developing interpretable AI models, post-hoc explainability techniques, and customized XAI frameworks that align with business goals and compliance requirements. Our solutions help organizations adopt AI responsibly by making machine learning models more understandable, trustworthy, and auditable.

    Key Areas of Experience:

    • Pre-modeling explainability: Enhancing model interpretability through feature engineering and knowledge-driven feature selection - namely Pre-modeling explainability

    • Feature attribution methods (SHAP, LIME) to quantify the impact of input features on model predictions.

    • Example-based methods to provide case-specific explanations via counterfactuals or prototypical instances.

    • Rule-extraction techniques to generate human-readable logic from complex models.

    • Ensuring compliance with Privacy and AI regulation, such as GDPR and the EU AI Act.

    • Developing AI dashboards that provide transparency into model decision-making.

    • Enhancing risk management by identifying biases and inconsistencies in AI outputs.

    • Extending explainability approaches beyond feature attribution to include uncertainty estimation, surrogate models, and causal modeling for better decision-making insights.

    We help businesses uncover the decision-making logic behind black-box AI models, ensuring interpretability, fairness, and regulatory compliance. Our solutions enable organizations to confidently leverage AI while maintaining trust and accountability.

[LLM-based Systems]

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.

  • At CORE64, we leverage Large Language Models (LLMs) to empower businesses with intelligent, context-aware solutions through Retrieval Augmented Generation (RAG) and Agentic Systems. Our methodology is rooted in collaboration, technical precision, and measurable impact, ensuring tailored systems that align with clients’ unique operational needs and strategic goals.

    Key Areas of Experience:

    • Conducting Discovery & Needs assessment to deeply understand our clients’ challenges, system landscapes and objectives. 

    • RAG Systems: We architect solutions that combine real-time data retrieval with LLM-powered generation. This involves curating and indexing domain-specific data (e.g., internal documents, customer interactions) into structured knowledge bases, often using vector databases for efficient semantic search.

    • Agentic Systems: We map out autonomous agent workflows, defining roles (e.g., decision-making bots and process orchestrators) and integrating tools like APIs, databases, and external platforms.

    • We prioritize data quality, structuring unstructured text and ensuring compliance with security protocols. For RAG, this includes creating robust data pipelines. For Agentic Systems, we focus on training data that reflects real-world decision-making scenarios.

    • Models are tailored to your domain, whether through prompt engineering, fine-tuning on proprietary data, or selecting optimal base models (e.g., GPT-4, Claude, or open-source alternatives).

    • We deploy MVP solutions to validate performance in controlled environments. For RAG, this might involve testing retrieval accuracy across diverse queries; for Agentic Systems, we simulate task execution to refine autonomy and reliability.

    • Systems are embedded into existing client infrastructure (e.g., CRMs, analytics platforms) via APIs or custom interfaces, ensuring minimal disruption and maximal user adoption.

    • Post-deployment, we monitor system performance through metrics like response accuracy (RAG) or task success rates (Agentic Systems). Regular updates adapt to evolving data, user feedback, and emerging LLM advancements, ensuring sustained value.

    Let us help you transform your business with LLM-based systems. Contact us today for a consultation, and let’s explore how we can build systems to enhance knowledge retrieval, automate complex workflows, and enable adaptive decision-making. Get in touch today to learn more!

Application Layer :: Harness your potential

[Human-Machine Interaction Suite]

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.

  • Our team specialises in creating task-oriented HMI designs tailored to the specific needs of diverse industries. We combine empirical research with practical application to develop interfaces that align with human cognitive processes and operational requirements.​

    Key Areas of Experience:

    • We elicit requirements and needs to inform the optimal solution design of your bespoke HMI to identify inefficiencies and areas for improvement, leading to enhanced system performance and user satisfaction.​

    • We evaluate existing HMI solutions to avoid undifferentiated heavy lifting, aiding you in the Make-or-buy decision 

    • We develop HMIs that present information based on the specific tasks and workflows of operators, enhancing usability and decision-making efficiency.

    • We facilitate cross-disciplinary collaboration combining UI/UX design with automation developed in context with our Ai Automation Suite and HMI principles that meet industry-specific demands.

    Let us help you transform your business with our Human-Machine Interaction Suite. Contact us today for a consultation, and let’s explore how we can enhance your system interfaces to meet your specific needs. Get in touch today to learn more!

[Intelligent Automation]

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.

  • We blend proven frameworks with cross-industry insights to align IA with your business goals. We bridge strategy and execution, delivering tailored roadmaps, scalable architectures, and change management—ensuring seamless adoption of your Intelligent Automation initiative.

    Key Areas of Experience:

    • Process Discovery & Optimization: Pinpointing automatable workflows and refining them for IA.

    • Legacy system Integration: Merging legacy systems with intelligent, context-aware bots.

    • Performance Monitoring & Optimization: Continuously refining IA tools for peak efficiency.

    • User Experience (UX) Design: Crafting intuitive IA interfaces for human-bot collaboration.

    • Compliance & Governance: Ensuring ethical AI use and regulatory alignment in automated systems.

    Let us help you transform your business with Intelligent Automation. Contact us today for a consultation, and let’s explore how we can automate workflows, increase efficiency, and support your specific business needs. Get in touch today to learn more!

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