Transform your enterprise with cutting-edge artificial intelligence solutions. From cyber intelligence to process automation, unlock the full potential of AI.
Service areas
NCAGE Code
NCAGE Code AX719
A · Active
NATO CERTIFIED SUPPLIER
Registered with the Italian Ministry of Defence's Centralized Automatic Identification System for Codification (SIAC). Our NCAGE code uniquely identifies us as a qualified supplier across the Defence Administrations of all NATO member countries.
The NATO Commercial and Government Entity Code enables participation in NATO procurement procedures and access to the NATO Codification System (NCS), guaranteeing traceability, quality, and technical compliance standards in defence, intelligence, and cyber security projects.
ACTIVE CODE
Comprehensive AI services tailored for global enterprises
Advanced AI-powered threat detection and prevention system for enterprise security.
Intelligent automation solutions to streamline operations and maximize efficiency.
Advanced analytics platform for data-driven strategic decision making.
Strategic consulting for successful AI implementation and transformation.
Intelligent virtual assistants for superior customer experience.
Ensure GDPR, AI Act, and international compliance for all solutions.
The capability areas in which we design and build artificial intelligence solutions: from strategy to production, from model security to regulatory compliance. Select an area or search for a technology, a regulation or a topic.
C01 · Area
From vision to value: we identify where AI creates real impact, how to measure it and how to bring people and the organization along.
Assessment of data, processes, skills and infrastructure to gauge organizational AI maturity and pinpoint priorities for action.
Identifying and prioritizing use cases by impact, feasibility and risk, turned into a phased roadmap balancing quick wins and strategic initiatives.
Business cases with TCO and ROI, value KPIs and control of inference costs (tokens, GPUs, licenses) across the whole solution lifecycle.
Choosing between proprietary and open-weight models, build vs buy and vendor due diligence, benchmarked on the company's real data and requirements.
Roles, processes and governance to scale AI: Center of Excellence, AI champions, idea intake procedures and solution reuse.
Change management with stakeholder engagement, communication and adoption metrics, so that AI tools are actually used day to day.
Role-based AI literacy programs (board, managers, technical staff, end users) aligned with AI Act Art. 4, with records of training activities.
C02 · Area
We engineer Large Language Model applications that are reliable, grounded in enterprise data and optimized for quality, cost and latency.
Retrieval-Augmented Generation over enterprise content with hybrid search, reranking, access control and source citations for verifiable answers.
Knowledge graphs and ontologies combined with LLMs to answer complex, multi-hop questions over entities and relationships, with more explainable answers.
Designing what the model sees (instructions, examples, memory, tools) and schema-validated structured outputs for consistent results.
Adapting open-weight models to specific domains, tone and tasks with parameter-efficient fine-tuning and preference-alignment techniques.
Compact, distilled and quantized language models for specific tasks: lower cost and latency, running on-premise or directly on device.
Solutions combining text, images, audio and video: visual analysis with VLMs, chart and diagram understanding, multimodal search.
Generation of text, images and media with watermarking and provenance metadata, in line with AI Act Art. 50 transparency obligations.
Coding agents and AI assistants across the development lifecycle: test generation, code review, documentation and legacy code modernization.
C03 · Area
AI agents that plan, use tools and collaborate to complete end-to-end processes, under human oversight and with least-privilege access.
Agents with planning, tool calling, memory and human-in-the-loop checkpoints, with explicit success and stop criteria.
Multi-agent architectures with specialized roles, supervisors and graph-based workflows, built on open-source frameworks and leading provider SDKs.
MCP servers and clients that connect agents to enterprise systems, databases and APIs in a standard, secure way, with OAuth 2.1 authorization.
Collaboration between agents across vendors and platforms via the open Agent2Agent protocol: discovery, task delegation and result exchange.
Agents that decide what to search, query multiple sources, cross-check and synthesize documented reports on markets, regulation and technology.
Agents that operate web and desktop interfaces like a user, automating legacy applications without APIs, sandboxed and under supervision.
Agent identity and permissions, spend and action limits, human approvals and complete audit trails for controlled, auditable autonomy.
C04 · Area
We automate document-heavy and operational processes by combining generative AI, agents and traditional automation, with people handling the exceptions.
Extracting and validating data from invoices, contracts, forms and scans with OCR and vision-language models, including complex layouts and tables.
Evolving RPA with AI agents that handle unstructured input and exceptions, keeping stable bots and replacing brittle ones.
Analyzing system event logs to reconstruct real processes, find bottlenecks and select the activities with the highest automation potential.
Orchestrated flows across ERP, CRM and document systems via APIs, events and BPMN engines, with AI embedded at decision points.
Automatic classification, routing and draft replies for emails, tickets and requests, with entity and priority extraction.
Automating accounts payable, reconciliations, onboarding and procurement, including Italian e-invoices (FatturaPA/SdI) and document consistency checks.
C05 · Area
Text and voice assistants that answer, act on enterprise systems and hand over to a human when needed, across every channel.
Agents that resolve customer requests, consult CRM and knowledge bases, and escalate to a human agent with full context.
Real-time voice agents for switchboards and contact centers, with natural speech recognition and synthesis, barge-in handling and telephony integration.
Assistants on WhatsApp Business, web chat, Microsoft Teams and Telegram, with approved templates, opt-in management and operator handover.
Internal copilots answering on procedures, policies and company documentation while respecting each user's access permissions.
Solutions optimized for Italian and multilingual use: translation, summarization, plain-language rewriting and evaluation of Italian-native models.
Analyzing calls and chats for sentiment, contact reasons, service quality and script compliance, with automatic summaries.
C06 · Area
Models that anticipate demand, risks and anomalies and recommend the best decision, with quantified uncertainty and explainable results.
Forecasting demand, sales, prices and energy with statistical models, machine learning and time-series foundation models, with prediction intervals.
Detecting anomalous behavior in transactions, sensors, logs and KPIs, including streaming data, to catch failures, fraud and incidents early.
Digital twins of assets and processes combining IIoT data and physics-based models to estimate remaining useful life and simulate operating scenarios.
Optimizing planning, logistics, shifts and resource allocation with mathematical programming, heuristics and reinforcement learning.
Recommendations for products, content and next-best-action using ranking models, measured through A/B testing.
Estimating the true effect of pricing, campaigns and interventions, separating causation from mere correlation for sounder decisions.
Scenarios on markets, commodities and geopolitical risk using Monte Carlo simulation, Bayesian models and verified open sources.
C07 · Area
We use AI to anticipate, detect and understand threats: threat intelligence, OSINT, SOC support and countering digital manipulation.
Automated collection, correlation and enrichment of indicators and TTPs from diverse sources, normalized to standard formats and mapped to MITRE ATT&CK.
SOC assistants and agents that enrich alerts, reduce false positives, suggest response playbooks and draft incident reports.
Open-source analysis with multilingual NLP, entity resolution and link analysis for due diligence, risk monitoring and reputational intelligence.
Detecting audio/video deepfakes, disinformation campaigns and emotional manipulation (vibe-hacking), with content provenance verification.
Models to detect fraud and money-laundering patterns across transactions and relationships, with graph analytics and explainable alerts.
Risk-based vulnerability prioritization and AI-assisted code analysis, supporting Cyber Resilience Act vulnerability reporting duties.
C08 · Area
We protect models, agents and data from prompt injection, poisoning and misuse, with offensive testing and defenses across the whole lifecycle.
Manual and automated adversarial testing of LLMs, RAG and agents to uncover jailbreaks, data leaks and unwanted behavior before release.
Layered defenses against direct and indirect prompt injection: input/output filtering, isolation of untrusted content and execution policies.
Threat modeling and secure design of AI applications following the OWASP Top 10 for LLMs and ETSI EN 304 223, from design to decommissioning.
Securing agents and MCP servers: OAuth 2.1 authentication, least privilege, sandboxed code execution and control of exposed tools.
Verifying provenance and integrity of models, datasets and dependencies: AI-BOM, model signing, safe formats and artifact scanning.
Protecting training datasets and vector knowledge bases from poisoning and leakage, with document-level access controls.
C09 · Area
We guide AI adoption in line with the AI Act, GDPR, Italian Law 132/2025 and cyber regulations, through management systems and practical controls.
AI system inventory and risk classification, provider/deployer roles and an action plan against Digital Omnibus deadlines (Annex III high-risk: December 2027).
Designing and implementing an AI Management System (AIMS) per ISO/IEC 42001, integrated with ISO/IEC 27001 and ready for the certification audit.
AI risk management using the NIST AI RMF Govern, Map, Measure and Manage functions, the NIST AI 600-1 Generative AI Profile and ISO/IEC 23894.
DPIAs, lawful bases, data minimization and data-subject rights for AI systems, informed by EDPB Opinion 28/2024 on AI models.
Fundamental Rights Impact Assessments (FRIA, AI Act Art. 27) and societal impact assessments of AI systems per ISO/IEC 42005.
Alignment with Italian Law 132/2025: AI-use disclosure to clients and workers, use in intellectual professions and the roles of AgID and ACN.
Embedding AI systems into NIS2 (Italian Legislative Decree 138/2024), DORA and Cyber Resilience Act requirements: ICT risk, third parties, incident reporting.
Decision explainability, bias audits and independent model validation, with technical documentation and post-deployment monitoring.
C10 · Area
AI is only as good as the data and platforms behind it: we build trusted data foundations and take models into production measurably and sustainably.
Lakehouse platforms on open table formats with batch and streaming pipelines, unifying analytics, BI and AI on a single trusted data foundation.
Vector indexes and scalable hybrid search, with chunking, embedding and metadata-filtering strategies designed around real content.
Catalogs, lineage, data contracts and quality checks for relevant, representative data (AI Act Art. 10) and Data Act-compliant sharing.
Synthetic data, differential privacy and federated learning to develop and test models without exposing personal or confidential data.
CI/CD pipelines for models and prompts, registries, data and model versioning, staged rollouts and safe rollbacks.
Evaluating LLMs, RAG and agents with golden datasets and LLM-as-a-judge, plus end-to-end tracing of steps, tokens and costs on open standards.
Open-weight models on private infrastructure, EU cloud or air-gapped environments, with optimized serving and a single gateway for routing, quotas and logs.
Measuring and reducing AI energy use and emissions through right-sized models, quantization, distillation and carbon-aware scheduling.
C11 · Area
We turn images, video, sensor and satellite data into operational insight, even in the field, within AI Act and GDPR limits on biometrics and surveillance.
Automated quality control on production lines: defect detection, measurement and classification, even with few defect examples available.
Video analytics for workplace safety, flows and logistics, with edge processing and privacy-preserving techniques such as face anonymization.
Models that describe images, answer visual questions and segment objects from text prompts, without dedicated training.
Analysis of optical and radar satellite imagery to monitor infrastructure, land, environmental risks and logistics activity.
Models optimized for edge devices, industrial gateways and NPUs, with quantization and low latency even without connectivity.
Fusing camera, LiDAR and IIoT sensor data for perception in industrial settings, collaborative robotics and infrastructure monitoring.
C12 · Area
Vertical expertise combining technology with knowledge of sector rules, from finance and public administration to defense environments.
Credit scoring and underwriting (high-risk under the AI Act), anti-fraud, KYC and document automation, consistent with DORA and supervisory expectations.
AI for predictive maintenance, quality, production planning and energy efficiency, integrated with MES/SCADA systems and industrial standards.
Clinical NLP, imaging analysis and interoperable health data, in compliance with the MDR, GDPR and the European Health Data Space (EHDS).
AI for more accessible public services and faster procedures, following AgID guidelines, the Three-Year ICT Plan and Law 132/2025 principles.
Forecasting load, renewable output and prices, grid optimization and scenario analysis on energy commodities.
AI for high-security environments: air-gapped deployment, intelligence analysis, dual-use export controls and NATO Principles of Responsible Use of AI.
Contract analysis, legal research and assisted drafting with verifiable sources, keeping the professional's contribution prevalent as Law 132/2025 requires.
7thEdition
As an Official Supporter, we are proud to have participated in the Digital Security Festival 2025, the 7th edition inaugurated at the Italian Chamber of Deputies. A crucial moment of discussion on topics closely related to innovation, security, and the digital future of Italy.
"Being an official sponsor of the Digital Security Festival is an important recognition of our journey: combining technology, artificial intelligence, and cyber intelligence to strengthen digital security culture."
Enterprise AI from our headquarters at Via Monte Napoleone 8, Milan, Italy.
Pragmat AI S.r.l. develops Enterprise AI solutions for global enterprises in six areas: cyber intelligence, process automation, predictive analytics, AI consulting, conversational AI, and compliance & ethics.
We are registered with the SIAC of the Italian Ministry of Defence as a NATO certified supplier, NCAGE code AX719, and took part as an Official Supporter in the 7th edition of the Digital Security Festival, inaugurated at the Italian Chamber of Deputies.
Cutting-edge research from our global AI team
DOC-01 · AI Models · Research
Explore specialized AI models (LLM, MoE, VLM, SAM) transforming business. From natural language understanding to multimodal reasoning.
DOC-02 · Cyber Security · Intelligence
Analysis of vibe-hacking phenomenon and how AI is used to manipulate collective emotions. Risks and defense strategies for enterprises.
Artificial Intelligence is no longer a monolithic technology. Instead, it is evolving into a diverse ecosystem of specialized models, each designed for a different purpose. From natural language understanding to multimodal reasoning and computer vision, these architectures are redefining what AI can achieve. In this article, we explore eight of the most relevant specialized AI models—how they work, where they are applied, and why businesses should pay attention to them.
Large Language Models such as GPT, LLaMA, or Claude have become the poster child of AI. They process text input, tokenize it into manageable chunks, transform it through billions of parameters, and generate coherent, human-like output.
Use cases: customer service automation, knowledge assistants, text summarization, code generation.
Strengths: fluency and versatility.
Limitations: hallucinations, large computational cost.
Before generative LLMs dominated the scene, masked models like BERT paved the way. Instead of predicting the next word, MLMs learn by filling in the blanks within a sentence. This bidirectional attention mechanism makes them excellent for tasks such as classification, search relevance, and question answering.
Strengths: high accuracy for comprehension tasks.
Limitations: weaker in creative generation.
As the industry shifts towards efficiency, SLMs offer compact alternatives to LLMs. Through techniques such as quantization, pruning, and memory optimization, these models can run on edge devices while maintaining respectable performance.
Use cases: mobile applications, IoT devices, privacy-preserving on-device AI.
Business value: democratizes AI by lowering hardware and energy requirements.
The Mixture of Experts architecture distributes workload across multiple specialized sub-models (“experts”). A routing mechanism decides which experts to activate depending on the input. Google’s Switch Transformer is a leading example.
Benefits: massive scalability with reduced training cost.
Challenge: requires complex orchestration and careful balancing between experts.
While LLMs are conversational, LAMs focus on reasoning and decision-making. They break down tasks, recognize intent, plan actions, and integrate memory. In practice, they resemble intelligent agents capable of executing workflows or making strategic decisions.
Use cases: AI copilots, autonomous agents, business process automation.
Strategic impact: they move AI from being a passive tool to an active collaborator.
LCMs operate with advanced embedding and diffusion techniques, enabling consistency across representations. They are particularly useful in compression, pattern recognition, and knowledge structuring. While still experimental compared to mainstream LLMs, they are gaining traction in domains requiring semantic coherence.
Application areas: anomaly detection, predictive analytics, deep pattern mining.
AI is not only about text. VLMs integrate visual and textual inputs, aligning them in a shared space. OpenAI’s CLIP or GPT-4 Vision are prominent examples.
Use cases: visual search, multimodal assistants, compliance automation (e.g., analyzing documents with both images and text).
Strategic advantage: enables businesses to build systems that “see” and “read” simultaneously.
Developed by Meta, SAM specializes in computer vision segmentation. Given an image and a prompt (e.g., “highlight this object”), SAM generates masks with high precision.
Use cases: medical imaging, autonomous vehicles, manufacturing quality control.
Relevance: provides reliable building blocks for vision-based applications.
Understanding specialized AI models is not just a technical exercise. It has direct business implications:
Organizations that learn to leverage the right AI architecture for the right problem will stay ahead in the digital economy.
AI is diversifying. From LLMs that generate human-like text, to MoE architectures that scale efficiently, to SAM models that understand images, each specialized approach is a puzzle piece in the broader AI landscape. The real opportunity lies not in choosing one over the other, but in orchestrating them together to build intelligent, adaptive, and trustworthy systems.
At PragmatAI, we believe that the future of AI will be hybrid, multimodal, and highly specialized—designed not only to generate data, but to deliver actionable intelligence.
Over the past few years, the debate around disinformation has mostly focused on fake news, data manipulation, and influence campaigns orchestrated by state and non-state actors. Yet a new phenomenon is emerging—subtler, less visible, and potentially more dangerous: vibe-hacking.
The term comes from digital slang and refers to the act of manipulating “vibes”—the emotional atmospheres, perceptions, and moods of individuals or entire communities. It’s not about persuading people with false information, but about reshaping the emotional context in which they interpret reality. In short, the attack does not target content itself but the collective mood.
At first glance, vibe-hacking may look like an extension of emotional marketing, where brands try to associate their products with positive feelings such as safety, belonging, or happiness. The difference is that in vibe-hacking, the goal is not to sell, but to steer political, social, or cultural behavior.
Psychological operations (psy-ops) have always relied on emotions. What’s new is that today, with the rise of generative AI, manipulation becomes scalable, personalized, and almost invisible.
Techniques vary, but some of the most common include:
Artificial Intelligence makes vibe-hacking more pervasive for three main reasons:
This means the danger is not just running into a single piece of fake news, but living inside an information ecosystem that gradually shifts the collective mood without being noticed.
Countering vibe-hacking requires different tools than traditional fact-checking. Key strategies include:
The battle for cognitive security will not be fought solely over the accuracy of information, but over the ability to safeguard the emotional dimension that shapes collective decision-making in digital societies. Distinguishing fact from fiction is no longer enough: the real challenge lies in protecting the psychological environment in which information circulates. In an ecosystem where millions of people are exposed daily to content generated or amplified by algorithms, the danger is not just the distortion of facts, but the gradual erosion of trust, emotional balance, and critical thinking. Safeguarding the cognitive atmosphere ultimately means defending the very possibility of healthy, democratic public debate.
Vibe-hacking is already here. The real question is whether we will recognize it before it becomes the new normal.
See how AI is transforming global enterprises
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#Supporter | Net Patrol Italia, NT Nuove Tecnologie, Pragmat AI and Var Group are among the supporters of the #DigitalSecurityFestival, the national event for promoting digital security culture.
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We are proud to have participated in #DigitalSecurityFestival2025, inaugurated at the Chamber of Deputies. A fundamental moment of discussion on innovation, security and digital future.
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Opening Ceremony of the 7th Digital Security Festival at the Chamber of Deputies - Sala Matteotti, Rome. Friday, September 19, 2025, national event on digital security and innovation.
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Following Gödel's lesson: a logical system cannot prove itself to be non-contradictory. AI is not inconclusive, but must be based on verified, cross-checked and robust data.
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🚀 Gödel understood it in 1931: AI can never be infallible. No logical system can be both complete and consistent at the same time. Human judgment is always needed.
View on LinkedIn (post from August 15, 2025, opens in a new tab)Updates from the world of artificial intelligence
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NCAGE AX719 · VAT ID IT14271450968