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: they are the method we use to solve four recurring problems — automating document processes, automating operational work, making company knowledge queryable, and taking AI to production in regulated environments. In every project they are combined according to the process, data quality, existing systems, security constraints and the organization’s measurable objectives.
We start from the problem
The reasons clients call come down to four business problems. The eight areas below are the method we use to solve them.
AI Strategy
What you get: you know which process to act on first, with priorities based on measured impact and risk, not intuition.
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
What you get: tasks that today take hours become minutes, with a person reviewing the outcome instead of executing every step.
An AI Agent is a system that autonomously carries out a sequence of actions inside a business process, not just a conversation: it reads data, executes steps within defined rules, and knows when to stop. We design agents of this kind with explicit objectives, limits and responsibilities. It's distinct from a Document Intelligence system, which extracts and structures information from documents, and from a Custom LLM, which is about the underlying language model: the agent is the layer that orchestrates actions, often using one or both as components.
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
What you get: archives that today need manual search become queryable, verifiable data, with the source of every piece of information always cited.
Document Intelligence is the set of techniques that turn unstructured documents — scans, PDFs, forms, historical archives — into verifiable, queryable data. Much of the hidden value in complex organizations is trapped in exactly these documents and in operational communications. Unlike an AI Agent, it doesn't take action inside business systems: it turns documents into structured data that an agent, a search system or a person can then use.
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
What you get: whoever is looking for an answer finds it in seconds, with the source cited instead of tracking it down from memory or a colleague.
Enterprise Search is a system that makes the knowledge already present in an organization searchable in natural language, without requiring exact keywords. We preserve source traceability and control over answers in every implementation.
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
What you get: answers in your organization's real language and constraints, not the generic voice of a product built for anyone.
A generic language model is rarely enough for regulated sectors, specialist language and high-responsibility processes. It's neither an agent nor a document pipeline: it's the model layer itself, which can sit underneath an AI Agent, a Document Intelligence system or an Enterprise Search system alike.
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
What you get: the system connects to what you already use — ERP, CRM, protocol, PEC — without forcing a new management platform or an ungoverned silo.
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
What you get: every assisted decision stays justifiable and verifiable, even under external review or an AI Act audit.
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
What you get: the people using the system every day know what it can do, what to check, and when to stop — instead of distrusting or ignoring it.
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.
A closer look
Enterprise RAG
What you get: answers generated by the model but anchored to your real documents, with the source cited for every claim — not a chatbot answering from what it learned during training.
RAG (Retrieval-Augmented Generation) is the architecture that retrieves the relevant passages from your organization's documents before generating an answer, instead of relying only on what the model memorized during training. It is the layer that makes an Enterprise Search system trustworthy in a regulated context: every answer stays verifiable, not invented.
Retrieval
Retrieving the passages that are actually relevant from large, heterogeneous archives, before the model generates any answer.
Chunking
Splitting documents into portions that preserve meaning, so retrieval finds the right context instead of an isolated fragment.
Source citation
Every answer states the document and passage it came from, verifiable by whoever uses it before acting on it.
On-premise RAG
The same architecture, with model and document store hosted inside the organization's perimeter, for data that cannot leave internal infrastructure.
AI On-Premise
What you get: the same AI systems, with data and model hosted inside your infrastructure perimeter — no data leaving the organization to reach an external provider.
AI On-Premise means running the model, retrieval and document store inside the organization's own infrastructure, instead of on a third-party cloud service. It is the deployment choice when data residency is non-negotiable: public administration, banks, insurance and sectors with strict regulatory constraints.
Data residency
Data stays physically within the organization's perimeter, without transiting through third-party infrastructure.
On-premise LLM
Language models running on dedicated hardware, with capacity and cost sized to real load, not to a consumption-based subscription.
On-premise RAG
Retrieval and generation anchored to an internal document store, so company content never leaves the infrastructure.
Continuity and control
Availability under the organization's direct control, independent of an external provider's SLAs and policies.
AI for Payment Reconciliation
What you get: payments carrying a free-text reference get matched automatically to the right counterparty, with uncertain cases routed to an operator instead of being left unattributed or attributed to the wrong position.
Incoming payment reconciliation is one of those processes where volume makes manual review impractical, and where the two kinds of error — a suspended payment and a payment attributed to the wrong position — don't weigh the same. The system normalizes the payment reference, matches it against the counterparty registry, and routes only low-confidence cases to an operator, with the reasoning already attached.
Normalization and entity extraction
Reading the free-text payment reference — abbreviated names, partial references, a different format per ordering bank — and extracting the entities usable for matching.
Confidence-scored matching
Comparison against the counterparty registry (NDG), with thresholds configurable by amount band and calibration on the asymmetry between the two error types.
Routing of uncertain cases
Below threshold, the case goes to an operator with ranked candidates and the reason for each: the human decision starts from work already done.
Audit trail for every attribution
Every automatic attribution keeps the criterion that produced it, reconstructable afterwards for review or audit.
Document Intelligence for CAD Drawings
What you get: dimensional callouts on technical drawings are read and extracted automatically, checked against the applicable ISO/EN/DIN standards, with low-confidence cases flagged for engineering-office review instead of being missed in manual checking.
Manually reading dimensions off CAD drawings that aren't standardized across clients and suppliers is slow, repetitive work exposed to transcription errors, with a direct impact on quoting and conformity. A Vision-Language pipeline extracts dimensions straight from the technical drawing and checks them against a structured standards knowledge base, leaving the engineering office only the cases the system isn't confident it read correctly.
Vision-Language reading of the drawing
Interpreting dimensional callouts, tolerances and annotations directly from the technical drawing, regardless of the source graphic format.
Automated conformity checking
Comparing extracted dimensions against a structured knowledge base of the ISO, EN and DIN standards applicable to the product family.
ERP integration for quoting
Direct access to the quoting and production data already in use, without duplicating the technical archive.
Human review on low-confidence cases
Dimensions the system doesn't read with sufficient certainty stay flagged for the engineering office, not excluded from the count.
Automating Case Processing in Public Administration
What you get: incoming cases get classified, checked for completeness and summarized for the officer, so repetitive document checking doesn't consume the time that should go to actual evaluation.
A public body receiving thousands of cases every month in paper, PDF or certified-mail format faces the same bottleneck on almost every procedure: completeness checks, requirement verification, protocol registration, summary preparation for the officer. A Document Intelligence system speeds up these steps without replacing administrative responsibility: it flags missing documents, prepares a structured summary, and leaves the decision on ambiguous cases to the operator.
Automatic case classification
Sorting by type, urgency and competence as soon as a case arrives, regardless of the intake channel.
Completeness checking
Flagging missing documents or unmet requirements before the case reaches the officer.
Structured summary for the officer
Every extracted piece of information stays linked to its source document, verifiable before it is used.
Audit trail and traceability
Every automated step stays reconstructable: final decision responsibility does not shift from the officer to the system.
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.
- 01
Assessment
Initial evaluation of processes, data, document bases, systems, constraints, risks, objectives and success indicators.
- 02
Business Analysis
Analysis of the organizational context and translation of management, office and user needs into verifiable requirements.
- 03
AI Design
Design of the technical architecture, operating workflow, controls, human roles and project KPIs.
- 04
Development
Building the system in verifiable iterations, with functional prototypes, real-case testing and frequent feedback from involved users.
- 05
Testing & Evaluation
Verification of accuracy, robustness and traceability on real cases, with edge-case scenarios, adversarial testing and agreed acceptance thresholds.
- 06
Deployment
Controlled rollout to production, integrated with existing infrastructure, policies, identity systems, permissions and procedures.
- 07
Training
Training for the people who will use, supervise and govern the system, with attention to limits, responsibilities and escalation procedures.
- 08
Continuous Improvement
Monitoring outcomes, periodically reviewing outputs and continuously improving the system based on real data and new operational needs.
What you need, which system answers it
A quick map to orient yourself: the Assessment sets which combination fits your specific case.
How much autonomy the system has
Not every AI system decides the same way: the level of autonomy is a design choice, not a property of the model.
Cloud, on-premise or hybrid: how we choose
Deployment follows the organization’s constraints, not a default technology preference. These are the criteria we compare during the initial Assessment.
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: on every project they're chosen and combined based on the process, data quality, the systems already in use and the organization's measurable goals. A Document Intelligence project on an inconsistent document archive, for example, almost always draws on Enterprise Search as well, to make the extracted knowledge queryable, and on AI Security & Guardrails to keep responses traceable and aligned with existing permissions. The combination comes out of the initial Assessment, not a fixed price list: it's how we avoid selling technology the process doesn't actually need. AI Training & Enablement, in particular, accompanies nearly every other area, because a system people don't know how to use creates no value.
What does the AI Agents service involve?
We design intelligent assistants that operate inside business processes with explicit goals, limits and responsibilities, not generic assistants bolted onto a language model. The service covers conversational interfaces connected to real data, documents and systems within a defined operating scope; controlled workflow automation across multiple applications, with state, authorization and exception handling; decision support through synthesis, source comparison and traceable reasoning; and task automation with confidence thresholds, block rules and human oversight on high-impact actions. A typical case is an agent connected to the records-management and case-tracking systems, which classifies an incoming request, checks the attached documents against requirements and prepares a draft response for an operator to review before sending. The agent doesn't decide in the person's place: it cuts the time that person spends gathering and checking the information needed to decide.
What is Futura AI’s eight-phase method?
Every project goes through the same eight-phase 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. Assessment evaluates processes, data and constraints before a single line of code is written; Business Analysis translates the needs of leadership, teams and end users into verifiable requirements; AI Design defines the architecture, controls and project KPIs; development proceeds in short iterations with functional prototypes verified on real cases. Testing & Evaluation and Training aren't a final add-on but part of the framework from day one, because a system people don't know how to use, or that hasn't been tested on edge cases, isn't ready for production. Continuous Improvement closes the loop and reopens it, because documents, procedures and models keep changing after release.
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. The indicative timeline emerges from the initial Assessment, which takes about two weeks and delivers an impact and complexity estimate before any development commitment. A Document Intelligence project on a narrow, well-governed document domain, for example, reaches a usable first release faster than a system that has to integrate with several business systems at once and meet strict security constraints. Either way, we release in phases, starting from a low-risk case, and extend only after validation by the operators who will use the system every day: a choice that lengthens the first release slightly but reduces the risk of having to redo it.