Artificial Intelligence that generates operational results.
We design Generative AI systems for complex organizations: we analyze processes, build reliable architectures, integrate models and data, automate operational tasks, and measure the value created with concrete KPIs.
Technology-agnostic: we adapt to your specifications
What we mean by an "AI system"
An AI system is not a chatbot, a plugin, or a well-written prompt. It is the combination of elements that lets artificial intelligence operate inside a real process, with accountability, controls and measurable results.
Models
Chosen based on the task at hand, not on how well-known the vendor is.
Data
Authorized sources, with known quality and provenance, that the system can base its answers on.
Workflow
The exact point in the process where AI steps in, and what happens before and after.
Permissions
Who can use the system, under what conditions, and which data and actions they can access.
Integrations
Real connections to the systems already in use: management software, protocol, databases, APIs.
Guardrails
Explicit limits on what the system can and cannot do, even when facing malicious input.
Audit trail
Traceability of sources, actions and decisions, verifiable at any time.
KPIs
Concrete indicators that show whether the system is working, not just whether it answers well.
If even one of these elements is missing, it isn’t a system: it’s an experiment that, sooner or later, someone will have to stop using. This is also why we don’t talk much about ChatGPT: the choice of model isn’t what makes the difference — designing everything around it is.
AI cannot be improvised. It must be designed.
A discipline that combines research, engineering and business.
An AI system for public administration, finance or industry cannot simply perform well in a demo. It must fit into real processes, handle sensitive data, comply with regulatory constraints, leave a trace of decisions and produce measurable evidence. This is why we start from the architecture, not the tool: models, data, human roles, integrations, controls and KPIs are designed together.
Applied research
PhDs in Data Science and scientific method to define hypotheses, metrics, tests and validation.
Executive education
Bologna Business School faculty and continuous dialogue with managers, CIOs and innovation leaders.
AI & ML Engineering
Technical expertise to turn language models into robust, governable systems.
Data Science
Data quality assessment, performance measurement and error control.
Software Architecture
Integration with infrastructure, databases, APIs and information systems already in use.
This combination of applied research, industrial expertise and production engineering is what distinguishes a solid AI project from an experiment that never reaches daily operations.
Ranked 10th out of more than 300 applications in the Emilia-Romagna Region's grant for the development of innovative startups — 2025 Edition.
Partners & programs
Security and governance by design
For us, security is not a final chapter of the project: it is a design requirement. An AI system that exposes data, can be manipulated by malicious input, ignores permissions or leaves no trace of its decisions is not ready for production.
Application guardrails
Input and output filtering, response validation, limits on executable actions and explicit handling of cases where the system must stop.
Prompt injection defense
Every retrieved piece of content from documents, pages, emails or knowledge bases is treated as untrusted input: instruction isolation, sanitization and action control.
Data governance
No training on client data without explicit authorization, environment separation, permissions aligned with existing roles and policies.
On-premise & hybrid
Cloud, on-premise or hybrid deployment based on data residency, operational continuity, audit requirements and existing infrastructure.
Audit trail & human-in-the-loop
Decisions, sources and relevant steps are traced; high-impact actions remain subject to human review or confirmation.
AI Act & compliance alignment
Risk classification, technical documentation, transparency and operational accountability are considered from the design phase.
Certification and compliance roadmap
ISO 9001
Quality management
Certification in progress
ISO 27001
Information security management
Certification in progress
NIS2
EU cybersecurity directive
Alignment in progress
GDPR
Personal data protection
Operational compliance
Red teaming, adversarial testing and edge-case behavior checks are part of validation: a system goes into production only when accuracy, robustness, security and traceability have been tested on realistic cases.
Why we are different
We don’t install chatbots.
We design systems.
Value emerges when AI is connected to procedures, data, permissions, responsibilities and controls. Our work starts from the process and the decisions it must support, not from the conversational interface.
We don’t sell licenses.
We build solutions.
We are not platform resellers. We select, integrate and, when needed, develop the technology best suited to the operating context, without being tied to a single vendor.
We don’t do demos.
We deliver projects used every day.
An AI system only matters if it enters daily operations: used by people, integrated with information systems, monitored over time and improved based on real outcomes.
Our Approach
Every project follows a precise framework, designed to reduce risk and make results measurable from the very start.
- 01
Analysis
Understanding the current process: activities, roles, exceptions, systems used, available data and where time, errors or costs are generated.
- 02
Process design
Redesigning the workflow around AI: what to automate, what to assist, and what must remain under human responsibility.
- 03
AI architecture
Choice of models, data sources, document repositories, guardrails, integrations and cloud, on-premise or hybrid infrastructure.
- 04
Development
Building the system in short iterations, with verifiable releases, technical documentation and feedback from involved users.
- 05
Testing
Verifying accuracy, robustness, security, traceability and behavior on real cases, including edge cases and prompt injection attempts.
- 06
Deployment
Controlled rollout to production, integration with existing environments, operational training and definition of management responsibilities.
- 07
KPI measurement
Concrete indicators defined with the client: average time, errors, volumes handled, avoided costs, response quality and internal adoption.
- 08
Continuous improvement
Periodic review of outcomes, data and prompt updates, threshold tuning and adaptation to process changes.
This is not a theoretical method: it is the sequence we apply, adapted to each client's technical, regulatory and organizational constraints.
What We Do
AI Strategy
Process assessment, adoption roadmap, governance and priorities defined by impact, risk, feasibility and expected ROI.
AI Agents
Operational assistants that read data, consult documents, execute controlled tasks and support traceable decisions.
Document Intelligence
OCR, classification, data extraction and knowledge bases to turn archives and cases into usable information.
Enterprise Search
Knowledge Graphs, Vector Databases and RAG to make internal knowledge searchable with verifiable sources.
Custom LLM
Selection, adaptation, evaluation and prompt engineering for models aligned with the organization’s language, constraints and processes.
AI Integration
Connection with ERP, CRM, MES, protocol systems, PEC, management software, databases and APIs, without duplicating existing tools.
AI Security & Guardrails
Prompt injection defense, application guardrails, audit trails, human-in-the-loop and AI Act requirements built into the design.
AI Training & Enablement
Training for operators, technical leads and managers on the system’s use, limits, supervision and escalation procedures.
Sectors
When it makes sense to introduce AI
Not every process benefits from artificial intelligence. Recognizing the right signals keeps you from introducing it where it isn’t needed, and helps prioritize where it actually matters.
High-volume repetitive processes
Activities repeated dozens or hundreds of times a day, with recognizable logic and identifiable rules.
Large amounts of unstructured documentation
Extensive archives of PDFs, emails, forms and scans that are hard to query with current tools.
Long information-retrieval times
People know where to look, but spend too much time finding and verifying the correct information.
Frequent errors in checks
Manual reviews that, due to volume or complexity, produce an error rate that is no longer acceptable.
Knowledge concentrated in a few experts
Critical know-how that depends on a handful of people, with real risk in case of turnover or absence.
Need for auditability
Processes where every decision must be reconstructable, justified and verifiable after the fact.
Technologies chosen with intent
Technologies matter when they support a design decision. Every component is selected based on the process, risk level, available data and infrastructure constraints.
Cloud, on-premise or hybrid are not preferences: they are architectural choices guided by security, compliance, data residency and operational continuity.
average accuracy in Document Intelligence projects, using OCR and Vision-Language Model pipelines on real documents
We work with both open-source models and PaaS services delivered through major cloud providers — AWS, Azure, GCP — chosen case by case based on cost, performance, data residency and the client's security constraints. We are not tied to a single model provider.
Results
The value of an AI project is measured in its effects, not in the technology used.
Reduced time
Hours recovered from case handling, document search, checks, analysis and repetitive tasks currently managed manually.
Reduced costs
Less operational load on low-value activities, with people redirected to specialist work, relationships and control.
Fewer errors
More consistent checks across documents, data and critical steps, with traced sources and uncertain cases escalated to operators.
Automation
More continuous processes, fewer manual passages and human supervision concentrated where decision accountability is needed.
Productivity
More requests, cases or activities handled with the same resources, without reducing control, quality or compliance.
ROI
Economic and operational indicators defined during assessment and monitored after release, not estimated only at project end.
Frequently asked questions
What does Futura AI do?
Futura AI designs Generative AI systems for complex organizations: we analyze existing processes, build reliable architectures (AI Agents, Document Intelligence, Enterprise Search, Custom LLM), integrate models and data, automate operational tasks, and measure the value created with concrete KPIs.
Which sectors does Futura AI work with?
We work mainly with Public Administration, Regional Governments, banks and insurers, and industry and manufacturing: contexts with regulated processes, sensitive data and high security requirements, where traceability of AI-assisted decisions is a requirement, not an option.
Where does a project with Futura AI start?
Most projects begin with an AI Assessment on a single high-impact process: a focused analysis that delivers process mapping, a feasibility assessment, a high-level architecture and an indicative estimate, with no development commitment.
Does Futura AI work only in the cloud, or on-premise too?
We are technology-agnostic. Deployment — cloud, on-premise or hybrid — follows the organization’s constraints: when data residency or sector regulation is non-negotiable, the architecture stays on-premise or hybrid.
Every organization has different processes.
Artificial Intelligence creates value only when it is designed around the people who will use it, the available data, the security constraints and the processes where it must operate.
Let’s talk