AI & Data

• AI & Data maturity assessment
  • Diagnosis of your existing capabilities (technical, human, organizational)
  • Mapping of strengths and weaknesses
  • Industry benchmark and identification of improvement levers

• AI / Data roadmap definition
  • Co-creation of a 1- to 3-year strategic plan
  • Prioritization of use cases
  • Alignment with business processes
  • Deployment planning
  • Estimation of budgets and necessary resources

• AI Project scoping
  • Qualification of business needs
  • Analysis of available datasets
  • Assessment of technical and regulatory feasibility
  • Writing of functional and technical specifications

• High-Impact use case sudies
  • Selection and modeling of value-creating use cases (automation, personalization, prediction, decision support), with ROI estimation and risk evaluation (operational, reputational, regulatory
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• Definition of Management Indicators
  • Development of AI performance KPIs, risk KRIs, adoption and operational efficiency metrics, integrated into your existing management tools

• Support for Strategic Decision-Making (Executive Committee, IT, Business)
  • Facilitation of strategic workshops
  • Concise reporting for decision-making
  • Investment prioritization assistance
  • Stakeholder alignment

• Regulatory and ethical consulting
  • Integration from the strategic phase of AI Act requirements, GDPR recommendations, ISO 42001 (AI governance), and best practices in fairness and transparency

• Definition of the data governance framework
  
  • Development or structuring of roles (data owner, data steward, CDO), governance rules, responsibilities, validation processes, and governance committee
  • Definition of policies aligned with your organization
• Mapping of data assets
  
  • Identification of critical datasets, analysis of data flows, sources, uses, and owners
  • Creation or enhancement of your data catalog
• Implementation of Data Quality Rules
  
  • Definition of quality dimensions (completeness, accuracy, uniqueness, consistency, timeliness)
  • Formalization of alert thresholds and integration into business processes
• Quality monitoring and alertin
  
  • Deployment of monitoring tools or routines
  • Visualization of quality indicators
  • Detection of anomalies or drifts
  • Automatic alerts and reporting
• Data documentation and traceability
  
  • Implementation of data lineage solutions to track origin, transformations, and usage across systems
  • Shared documentation accessible to business and technical teams
• Access security and data classification
  
  • Classification by sensitivity
  • Implementation of access policies
  • Access rights audits
  • Management of sensitive data (personal, financial, regulatory)
• Regulatory compliance of data processing
  
  • Integration of AI Act, DORA, ISO 42001, ISO 27001/27701, GDPR requirements with documented purposes
  • Data minimization
  • Retention periods
  • Rights management
• Stakeholder acculturation
  
  • Awareness programs for business, IT, and support functions on data governance, quality, and security
  • Support for implementing new practices within teamss

• Establishment of an AI governance framework
  • Implementation of roles and responsibilities, including AI Committee members, usage leads, and ethics liaison
  • Establishment of processes for decision-making, oversight, and escalation
  • Ensuring alignment with governance policies across IT, data, and cybersecurity domains
• Assessment of AI risks, including technical, ethical, and regulatory aspects
  • Identification and assessment of AI-related risks, including opacity, bias, bias, misuse, dependency, security vulnerabilities, and regulatory non-compliance
  • Critical scenario modeling
  • Evaluation of effects on fundamental rights and stakeholder interests
• Ensuring compliance with the provisions of the forthcoming AI Act
  • Typology of AI systems deployed, categorized by risk level: high-risk, critical, or prohibited uses
  • Assessment of gaps relating to regulatory demands, including documentation, explainability, data governance, system robustness, and human monitoring
  • Remediation plans
• Establishment of AI ethical guidelines and charters
  • Collaborative creation of internal standards embedding core organizational values such as fairness, transparency, responsibility, and non-discrimination
  • Engagement with teams and embedding within business workflows
• Assessment of transparency and explainability in AI models
  • Assessment of the interpretability capabilities of models (Explainable AI - XAI)
  • Recording and documentation of algorithmic decision-making
  • Oversight of Confidence Levels and Human Involvement within the Decision-Making Loop
• Establishment of AI registries, logging, and documentation processes
  • Development of AI processing records
  • Audit Logging of Automated Decision-Making
  • Comprehensive documentation of datasets, modeling processes, and outcomes to meet regulatory compliance and accountability standards
• Evaluation of data quality and fairness in datasets employed
  • Assessment of bias within training data
  • Assessment of representativeness, data exclusions, and statistical integrity
  • Suggestions for enhancements and corrective actions
• Guidance for the Executive Committee, Business Stakeholders, and Legal Departments
  • Targeted awareness workshops
  • Assistance in decision-making regarding critical AI applications
  • Coordination and facilitation of AI governance committees
  • Preparation of contractual provisions and AI policies for clients and partners

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• Identification and scoping of high-value sse cases
  
  • Co-creation of business use cases (forecasting, scoring, segmentation, decision automation) in collaboration with operational teams, including ROI assessment and evaluation of technical, legal, and organizational constraints
• Statistical modeling & Machine Learning
  
  • Development of predictive models, classification, regression, anomaly detection, time series analysis, and churn analysis.
  • Cross-validation, performance evaluation, and overfitting reduction
• Generative AI & NLP
  
  • Design of tools based on generative AI (text, images, code) and natural language processing (information extraction, automatic summarization, business chatbots, report generation)
  • Responsible integration with human oversight when necessary
• Anomaly Detection & Decision Suppor
  
  • Identification of abnormal behaviors or values in large datasets (fraud, failures, defects, drifts)
  • Visualization of results and recommendations for automated or semi-automated actions
• Customer/Process Segmentation & Scoring
  
  • Creation of marketing or operational segmentation models, lead scoring, eligibility or risk scoring, with actionable outputs for business teams (CRM, internal tools, dashboards)
• Model explainability and transparency (XAI)
  
  • Integration of explainability methods (SHAP, LIME, PDP, etc.) to ensure model understanding by business users, end-users, and regulators
• Model industrialization and monitoring
  
  • Preparation for production deployment (MLOps)
  • Model versioning management
  • Data drift monitoring
  • Long-term performance management
• Training and acculturation of data and business teams
  
  • Collaborative workshops
  • Knowledge transfer
  • Documentation
  • Training on analytical result interpretation and AI-business collaboration

• Strategic acculturation for Executive Committees and business units
  
  • Short and impactful sessions to understand the challenges of AI, data-driven decision-making, and responsible AI
  • Aligning with the company’s strategy, relevant use cases, and their associated risks
• Technical team training (Data, IT, Security)
  
  • Advanced modules on MLOps, model industrialization, data governance, AI systems cybersecurity, performance analysis, and model auditability
• AI and data awareness for business teams
  
  • Interactive workshops and practical cases for HR, marketing, finance, customer service functions, etc. Objective: understand the principles behind algorithms, learn how to formulate use cases, and interpret AI results
• Training modules covering AI ethics and regulatory compliance
  
  • Training on GDPR requirements, the AI Act, and trusted AI principles (non-discrimination, transparency, human oversight)
  • Integration into the daily practices of business and legal teams
• Design of personalized learning paths
  
  • Development of tailored training plans based on profiles, business objectives, and maturity levels
  • Blended learning programs combining e-learning, in-person sessions, workshops, and self-assessments
• Organizing and facilitating seminars, learning expeditions, and hackathons
  
  • Engaging sessions featuring inspiring presentations, immersive simulations, AI challenges, and sector-specific case studies designed to build collective momentum for data and AI transformation
• Measurement of progress and management of cultural adoption
  
  • Evaluation of knowledge, tracking of adoption metrics, pre- and post-training surveys, and continuous monitoring of skill progression

• Design of scalable AI & Data architectures
  
  • Designing cloud, hybrid, or on-premises architectures aligned with data volumes, model requirements, and organizational strategies such as centralized, distributed, or federated.
• Implementation of data pipelines and AI workflows
  
  • Building automated processing pipelines (extraction, transformation, loading, cleaning, enrichment)
  • Integration of real-time or batch data for training and inference
• Deployment of MLOps and CI/CD environments
  
  • Automation of the AI model lifecycle: versioning, testing, validation, monitoring, retraining. Integration with open-source tools (MLflow, Airflow, Kubeflow) or cloud solutions (Azure ML, SageMaker, etc.)
• Integration with existing information systems
  
  • ntegration with business applications (ERP, CRM, BI tools)
  • Implementation of inference APIs
  • Deployment of AI microservices"
  • Compliance with security and performance constraints
• Management of technical governance and access rights
  
  • Implementation of authentication mechanisms and access control for models, datasets, and training/deployment environments. Integration with IAM/SSO systems
• Performance supervision and monitoring
  
  • Monitoring performance of pipelines and models (latency, availability, error rate)
  • Implementation of alerts and dashboards for ongoing operations and maintenance
• Security of AI & Data infrastructures
  
  • Segmentation of environments
  • Model protection
  • Data encryption
  • Audit of cloud configurations
  • Integration with SOC or VOC for continuous monitoring
• Technical documentation and knowledge transfer
  
  • Formalization of architecture choices, workflows, and technical dependencies
  • Supporting internal teams with onboarding, maintenance, and future developments

• Defining the functional and technical scope of the need
  
  • Collaborative definition with business stakeholders of target functionalities, operational goals, technical limitations, and success metrics
• Creation of tailored algorithmic solutions
  
  • Development of tailored models for automating specific tasks, including scoring, classification, prediction, natural language processing, computer vision, anomaly detection, document processing, and conversational agents
• Design and development of user-friendly business interfaces
  
  • Design and development of business-oriented user interfaces (web, mobile, or integrated) to enhance end-user adoption of AI solutions
  • Incorporating user experience, model explainability, and human-in-the-loop supervision
• Integration within IT systems
  
  • Implementation through APIs, microservices, or connectors integrated with internal systems such as ERP, CRM, Document Management Systems (DMS), and business applications
  • Ensuring conformity with standards for architecture, security, and performance
• AI lifecycle management
  
  • Implementing MLOps methodologies for model lifecycle management, including version control, monitoring, retraining, continuous testing, explainable logging, and automated deployment
• Security of the AI solution
  
  • Managing access rights, isolating sensitive data, ensuring model auditability, and complying with GDPR and the AI Act
  • Embedding within the enterprise security ecosystem, including SOC, VOC, and CISO functions
• End-user training and supporting documentation
  
  • Development of usage guides, focused training, startup support, and competency transfer to foster user independence and long-term solution adoption
• Post-deployment support and scalability management
  
  • Assistance throughout scaling phases, ongoing enhancements, and model or feature updates driven by operational feedback and emerging business requirements

• Preparation for model production deployment
  
  • Evaluation of model robustness, scalability, and performance
  • Regression testing and verification of quality and regulatory compliance
• Industrialization of the AI/Data Pipeline
  
  • Deployment of automated inference pipelines
  • Management of inputs/outputs
  • Integration of supervision mechanisms, centralized logging, timestamping, error handling, and business monitoring
• Implementation of MLOps Architecture
  
  • Integration of models into CI/CD pipelines (build, test, automated deployment), with model versioning, validation, and traceability
  • Use of suitable platforms (MLflow, Kubeflow, Azure ML, etc.)
• Deployment in target environment
  
  • Containerization (Docker, Kubernetes), deployment in cloud, on-premise, or hybrid environments
  • Integration with your technical environments, security systems, and monitoring tools
• Connection with Information Systems
  
  • Integration via REST APIs, webhooks, ETL connectors, or middleware
  • Connection to business tools, data warehouses, CRM, ERP, or internal specialized applications
• Access Management, Security, and Compliance
  
  • User and service access control (IAM, RBAC), secrets management, call auditability, securing sensitive data flows
  • Compliance with GDPR, ISO standards, AI Act
• Performance testing and functional validation
  
  • Load testing, business validation, simulated error scenarios, stress tests
  • Resource allocation adjustment, sizing of execution environments
• Go-live support and post-deployment monitoring
  
  • Assistance during commissioning
  • Supervision of initial operational weeks
  • Anomaly tracking
  • Adaptation based on user feedback
  • Documentation and knowledge transfer to internal teams

• Model performance supervision
  
  • Monitoring prediction accuracy, identifying concept and data drift, and generating alerts for precision degradation, declining relevance, or business misalignment
  • Embedding within MLOps monitoring dashboards
• Oversight of pipelines and operational environments
  
  • Supervising data processing and inference workflows, including stability, latency, and integrity of input and output data
  • Automated alerting for failures
• Retraining and fine-tuning of models
  
  • Scheduling and performing model retraining with fresh data, threshold recalibration, and feature updates
  • Functional and technical validation of updated releases
• Resolution of anomalies and incidents
  
  • Management of technical and business anomalies, including inference errors, input problems, and service unavailability, with swift resolution and comprehensive incident documentation
• Functional and technical developments
  
  • Integration of new features, consideration of emerging use cases, and adaptation to evolving business needs or internal organizational changes
• Regulatory updates and ongoing compliance
  
  • Monitoring regulatory developments (AI Act, GDPR, NIS2, ISO 42001, etc.), adapting documentation, supervision mechanisms, and internal procedures to maintain compliance
• Version control and lifecycle management of models
  
  • Archiving, documentation, and version history of deployed models
  • Deployment of rollback procedures and handling of outdated or non-compliant models
• Providing user assistance and facilitating skill transfer
  
  • Assisting business and IT teams
  • Updating documentation
  • FAQ
  • Handling user feedback
  • Support for adoption of changes