Custom Services
Your end-to-end journey in AI
Custom Services represent an integrated offer designed to support companies in every aspect and requirement related to the adoption of Artificial Intelligence.
From strategy design to operational implementation, our teams integrate consulting, technological expertise, and change management, ensuring a tailored approach that is fully aligned with your business objectives.
Each intervention is designed to transform visions and ideas into concrete projects, reducing risks, accelerating adoption times, and maximizing business impact.
AI Strategy & Roadmap
The starting point for introducing artificial intelligence where it really matters through our “AI Introduction Framework.”
A practical, reliable operational approach oriented towards collaboration between stakeholders and sufficiently structured.
AI-Ready processes identification
- Exploration of our AI Smart Products and opportunities for further study aimed at initial and continuous fertilization.
- Introduction of our Archetype tool for an AI-Ready process and first extended internal selection
First mapping
- Guided collection and comprehensive information on processes identified by applying the AI-Ready Process Checklist
- Comparative evaluation of processes based on multiple parameters and visualization in an overall evaluation map
- Automatic matching of emerging processes with our AI Smart Products to identify ready-to-use solutions
Vertical assessment
- Performing a Vertical Assessment only on processes evaluated as the best candidates
- Production for each process and illustration of our AI-Ready Process Scorecard, which evaluates the process in terms of Business Impact and Tech Complexity.
- Comprehensive and comparative visualization through the AI-Ready Process Matrix
Design Strategy and Roadmap AI
- Working with the client to develop a customized implementation Roadmap for the effective introduction of AI into the company, based on the strategy to be pursued.
- Definition of KPIs for measuring and monitoring individual AI-based solutions and the development roadmap
Data Foundation & Governance
Ensure that the organization has a reliable, accessible and compliant database, ready to support the adoption of Artificial Intelligence at the operational level.
Data Audit and Gap Analysis
- Mapping of existing data sources (databases, Excel files, documents, etc.)
- Quality analysis in terms of completeness, consistency, and updating
- Identification of information silos and duplications
Architecture and Data Infrastructure Enhancement
- Structuring of the necessary data architecture
- Integration with business systems (ERP, CRM, IoT, etc.)
- Unification and standardization of data from different sources
- Data enhancement and enrichment with external sources and predictive models
- Integration of Security-By-Design principles for secure data management
Data Governance & Stewardship
- Definition of the roles of Owner, Steward, and Custodian
- Writing guidelines for data classification, access, and use
- Introduction of cataloging, lineage, and metadata management tools if not already present.
AI Ethics & Compliance Readiness
- AI risk assessment (bias, transparency, accountability)
- Alignment with the European "AI Act” Regulation"
- Development or adaptation of internal ethical policies for AI and data
- End-to-end protection of personal and corporate data.
Prototype Design & Validation
Refine, shape, and test high-value use cases for artificial intelligence, starting from real business needs and quickly validating their effectiveness and scalability through concrete prototypes.
Business Case & Prioritization
- Defining the objectives of the use case
- Estimated expected economic value (ROI, time saved, quality)
- Assessment of technical feasibility, data availability, internal sponsorship
Rapid Prototyping (PoC/MVP)
- Agile development of the working AI solution (ML, NLP, GenAI)
- Tests on real data and operational usage scenarios
Redesign of human-AI interaction
- Study of the interaction between users and the AI system (dashboard, chatbot, alerts, suggestions)
- Definition of levels of automation and human control
- Accessibility, simplicity, and transparency of AI interfaces
Validation and Scale-Up
- Validation of results with solution performance and end-user feedback
- Pre-analysis of functional and technical characteristics of the industrialized solution
- Assessment of economic and technical sustainability
AI Engineering & Deployment
Implement, integrate, and make the validated AI solution fully operational, with a robust approach to development, security, architecture, and technical governance, ensuring continuity and scalability.
Industrialization of the solution
- Optimization of AI algorithms selected for the production environment
- Overall consolidation of the solution
- Validation on advanced test sets
Integration with/into business systems
- Connectors and APIs to ERP, CRM, portals, and data platforms
- Automation of input/output flows between AI and business systems
- Integration with BI tools, dashboards, notifications, or workflows
Deploy and Setup of MLOps / LLMOps
- Environment setup and solution release
- CI/CD pipeline for AI solution release
- Versioning, auditability, and management of periodic retraining
Monitoring and Maintenance
- Continuous performance monitoring, anomaly detection, and user feedback collection
- Model updates and security patch application
Safety & Performance
- Permission, access, and log management
- Stress testing and validation in real environments
- Verification of privacy, data anonymization, AI bias
Change Management & Training
Supporting the organization in the informed and widespread adoption of AI, facilitating user engagement, the development of new skills, and seamless integration into operational processes.
Training, Upskilling and Internal Communication
- Training programs for end users, IT teams, managers, and leadership
- Different paths based on role: “AI for business,” “AI for HR,” “AI for IT”
- Delivery methods: microlearning, e-learning, practical workshops
- Guides, user manuals, and supporting training materials
- Multi-channel engagement campaigns: intranet, internal events, newsletters
Organizational Transformation Support
- Redefining roles and processes based on the introduction of AI
- Supporting HR and Change Leaders in monitoring the impact generated
- Alignment with the organization's strategic objectives
- Establishment of internal Center of Excellence
Impact Tracking & Continuous Evolution
Measure the value generated by artificial intelligence in a structured and continuous manner, monitor its impact on business processes and results, and define future developments in an iterative and sustainable way.
KPI Definition and Monitoring
- Economic impact (efficiency, savings, increased revenues)
- Solution performance
- Adoption and user satisfaction KPIs
- ESG or compliance KPIs (ethics, bias, transparency)
Interactive Dashboards & Reviews
- Custom dashboards for C-level and operational teams
- Periodic performance and value analysis
- Automatic alerts for anomalies or malfunctions in solutions
AI Evolution Review
- Periodic evaluation of implemented AI solutions
- Identification of new use cases, business areas, and improvements
- Revision of the AI roadmap based on operational experience
Continuous Learning process
- Collecting feedback from users and stakeholders
- Optimization of solutions
- Introduction and sharing of operational best practices
