Daniele Grotti
The author of the pieces published on this site.
CEO & Founder, Futura AI
Leads the design of Futura AI's Generative AI systems, from initial assessment to production rollout. On projects he personally handles problem framing, metric definition and the criteria that decide whether a system is ready for daily operations.
Writes the technical and methodological pieces published on the blog and authors the AI Act whitepaper. The articles come out of problems met on real projects: which is why they talk about evaluation sets, denominators and edge cases more often than about models.
Background
Researcher in Data Science
Scientific method applied to industrial projects: hypotheses, metrics, evaluation sets and reliability criteria defined before implementation.
Faculty at Bologna Business School
Executive education and continuous dialogue with managers, CIOs and innovation leaders on the real constraints of AI adoption.
Designing systems that run in production
Document Intelligence, Enterprise Search and AI Agents for public administration, finance and industry, integrated with the systems already in use.
Published articles
31 articlesEvery piece published on this site, most recent first.
- It's not the model. It's the data.
- What RAG actually means, explained without acronyms
- Chatbot or AI system? The difference that determines the return
- Why most AI projects stall at the demo
- What to ask an AI vendor before signing
- Prompt engineering isn't an individual skill, it's a design layer
- Adopting AI without rewriting your processes
- How to build an AI roadmap in a complex organization
- Beyond text: where Speech AI and Vision AI solve real problems
- Case study: extracting structured data from documents with no fixed format
- AI doesn't replace people: it redesigns processes around them
- Data security in AI projects: seven questions to ask your vendor
- Quoting and procurement: where AI moves the margin
- Vector databases and knowledge graphs: two ways to represent knowledge
- How to evaluate an AI system before it goes into production
- The cost nobody budgets for: maintaining an AI system
- Five public-sector tasks you can automate today
- AI governance: who is accountable when the system gets it wrong
- The AI that talks to your business systems: integrating with ERP, CRM and records management
- Hallucinations aren't an unpredictable flaw: they can be governed
- PNRR and EU funds: AI-assisted document monitoring
- Enterprise search: why employees can't find what the company already knows
- Fine-tuning or RAG: an architecture choice, not a trend
- Why a few weeks of assessment are worth more than a year of experiments
- Manuals, bills of materials and technical support: the knowledge the company already has
- KYC, AML and due diligence: where generative AI actually saves time
- On-premise or cloud: how to decide when data is sensitive
- How to measure the return on a generative AI project
- Document intelligence in public administration: from the protocol system to administrative determinations
- Agentic AI: when a system stops answering and starts acting
- AI Act: what changes in practice for companies introducing AI
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