Futura AI
it

Services

We design Generative AI systems across the full lifecycle: initial assessment, process design, architecture, development, production rollout, training and continuous improvement.

The areas below are not isolated packages. In every project they are combined according to the process, data quality, existing systems, security constraints and the organization’s measurable objectives.

AI Strategy

Before introducing any technology, we analyze the organization’s processes to understand where AI can create real, sustainable and measurable impact.

  • Process analysis

    Mapping workflows, involved roles, recurring exceptions and the points where time, errors or costs are created.

  • Roadmap

    A phased AI adoption plan, prioritized by expected value, risk, technical feasibility, organizational impact and time to return.

  • Assessment

    Evaluation of digital maturity, data quality, document bases, existing systems, regulatory constraints and security requirements.

  • AI Governance

    Rules, roles, controls, responsibilities, metrics and escalation procedures for secure, transparent and compliant AI use.

AI Agents

We design intelligent assistants that operate inside business processes, with explicit objectives, limits and responsibilities.

  • Intelligent assistants

    Conversational interfaces connected to the organization’s real data, documents, procedures and systems, with a defined operating perimeter.

  • Workflow automation

    Controlled automation of operational sequences across multiple applications, with state management, permissions, exceptions and audit.

  • Decision support

    Decision support through summaries, source comparison, consistency checks and traceable reasoning.

  • Task automation

    Automation of repetitive activities with confidence thresholds, blocking rules, permissions and human supervision.

Document Intelligence

Much of the hidden value in complex organizations is trapped in unstructured documents, historical archives and operational communications.

  • OCR

    Digitization and automatic reading of PDFs, scans, attachments, forms and historical documentation even when formats are inconsistent.

  • Classification

    Automatic sorting by type, urgency, responsibility, case status, content and risk level.

  • Data extraction

    Turning heterogeneous documents into structured, verifiable data, with evidence of the source each piece of information came from.

  • Knowledge base

    Searchable knowledge repositories, updateable, linked to sources and organized around the organization’s real language.

  • Semantic search

    Search by meaning, context and relationships, useful when the same concepts are expressed with different words.

Enterprise Search

We make the knowledge already present in the organization searchable in natural language, while preserving source traceability and control over answers.

  • Knowledge Graph

    Structured relationships between information, documents, people, regulations, cases, customers, suppliers and other operational entities.

  • Vector Database

    Infrastructure for scalable semantic search across large, heterogeneous archives, with update, version and permission management.

  • RAG

    Responses generated by models but anchored to the organization’s real documents, accompanied by sources and designed to reduce hallucination risk.

  • Document Search

    Unified search across archives, repositories, management systems, intranets, manuals, regulations and heterogeneous document bases.

Custom LLM

A generic language model is rarely enough for regulated sectors, specialist language and high-responsibility processes.

  • Fine Tuning

    Specializing a model when the use case requires specific language, output format, decision style or behavior.

  • Model Selection

    Choosing the most suitable model for accuracy, cost, latency, security, deployment, privacy and infrastructure constraints.

  • Evaluation

    Systematic assessment of accuracy, robustness, source citation, errors, stability and edge-case behavior.

  • Prompt Engineering

    Instructions, policies, examples, operating context and refusal criteria that guide the model’s behavior in production.

AI Integration

An AI system isolated from existing information processes creates no value: it must work with the tools the organization already uses.

  • ERP, CRM, MES

    Direct integration with management, commercial and production systems to read data, update statuses and support operational activities.

  • Protocol and PEC

    Automation of document flows, protocol registration, communication classification and certified correspondence.

  • Management software and DBs

    Connection to existing archives while respecting policies, roles, permissions, data segregation and access traceability.

  • API

    Integration through standard interfaces, without disrupting existing systems and without creating ungoverned application silos.

AI Security & Guardrails

A production system must protect data, withstand malicious input and remain traceable in every AI-assisted decision.

  • Application guardrails

    Input and output filters, action limits, response validation and management of cases where the system must request confirmation.

  • Prompt injection defense

    Retrieved content treated as untrusted input, system-prompt isolation and control over instructions coming from external sources.

  • Red teaming & adversarial testing

    Adversarial tests aimed at compromising the system before production release, followed by review of identified vulnerabilities.

  • Audit trail & compliance

    Traceability of sources, outputs, actions and assisted decisions, aligned with the AI Act and sector-specific regulatory requirements.

AI Training & Enablement

An AI system creates value only if the people using it understand its capabilities, limits and responsibilities: training is part of the project, not a final add-on.

  • Operational training

    Programs for the operators who will use the system daily, built on the real cases of their process rather than on generic examples.

  • Technical enablement

    Knowledge transfer to IT contacts covering architecture, integrations, monitoring and routine maintenance of the solution.

  • Governance and supervision

    Training for managers on roles, controls, indicators, escalation procedures and the obligations introduced by the AI Act.

  • Documentation and continuity

    Operating manuals, technical documentation and onboarding material to keep the system independent of specific individuals.

Method

Regardless of the area of intervention, every project goes through the same eight-phase framework: this is how we make risk controllable and results measurable.

  1. 01

    Assessment

    Initial evaluation of processes, data, document bases, systems, constraints, risks, objectives and success indicators.

  2. 02

    Business Analysis

    Analysis of the organizational context and translation of management, office and user needs into verifiable requirements.

  3. 03

    AI Design

    Design of the technical architecture, operating workflow, controls, human roles and project KPIs.

  4. 04

    Development

    Building the system in verifiable iterations, with functional prototypes, real-case testing and frequent feedback from involved users.

  5. 05

    Testing & Evaluation

    Verification of accuracy, robustness and traceability on real cases, with edge-case scenarios, adversarial testing and agreed acceptance thresholds.

  6. 06

    Deployment

    Controlled rollout to production, integrated with existing infrastructure, policies, identity systems, permissions and procedures.

  7. 07

    Training

    Training for the people who will use, supervise and govern the system, with attention to limits, responsibilities and escalation procedures.

  8. 08

    Continuous Improvement

    Monitoring outcomes, periodically reviewing outputs and continuously improving the system based on real data and new operational needs.

Frequently asked questions

What services does Futura AI offer?

Eight areas, combined based on the project: AI Strategy, AI Agents, Document Intelligence, Enterprise Search, Custom LLM, AI Integration, AI Security & Guardrails and AI Training & Enablement. These aren’t isolated packages: they’re chosen based on the process, data quality and the organization’s measurable goals.

What does the AI Agents service involve?

We design intelligent assistants that operate inside business processes with explicit goals, limits and responsibilities: conversational interfaces connected to real data and systems, controlled workflow automation, decision support and task automation with confidence thresholds and human supervision.

What is Futura AI’s eight-phase method?

Every project goes through the same framework: Assessment, Business Analysis, AI Design, Development, Testing & Evaluation, Deployment, Training, Continuous Improvement. It’s how we make risk controllable and results measurable, regardless of the area of intervention.

How long does it take to develop an AI system with Futura AI?

It depends on the process and the required architecture: development proceeds in short iterations, with verifiable functional prototypes and testing on real cases from the early phases, rather than a single release at the end. An indicative timeline emerges from the initial Assessment.