Services / 03

Artificial intelligence
applied.

AI produces value when it solves a concrete problem and enters the systems already used by the organization. We don't insert it everywhere.

Explore the interventions →

Use cases

Fewer demonstrations.
More operational utility.

We analyze tasks, documents and information flows to identify realistic applications, considering quality, risk, data and human supervision.

Business assistants

Research documents, procedures, manuals and internal knowledge.

Document intelligence

Classification, data extraction, synthesis and reporting.

Requests and tickets

Sorting, analysis and preparation of preliminary responses.

AI in systems

APIs, external or local models integrated into sites and platforms.

Responsible method

Value, control
and proportion.

01 — Opportunities

Expected impact and suitability of the use case.

02 — Data and risks

Sources, privacy, possible errors and supervision.

03 — Prototype

Controlled test with defined success criteria.

04 — Integration

Workflow, training, guidelines and monitoring.

AI is useful when

  • Interpret large amounts of content
  • Supports repetitive decisions
  • Reduces time without eliminating control

It is not the right choice when

  • A simple rule solves the problem better
  • The data is not sufficient or reliable
  • The risk of error is not manageable

Interventions that can be activated

From the first assessment
to the operating system.

Each intervention remains part of the artificial intelligence service. The perimeter is chosen based on the maturity of the organization, the data available and the result to be obtained.

01Analysis and roadmapAI Opportunity Assessment

Identify where AI can generate value before investing in disconnected experiments.

What we analyze

  • Objectives, constraints and candidate activities
  • Processes, sources, data quality and tools
  • Impact, complexity, privacy and risks

Output

Use cases ordered by priority, prerequisites, hypothesized technologies, indicative costs and roadmap of the first project.

02Pilot projectAI Workflow Starter

Transform an opportunity into an integrated, documented and measurable.

Applications

  • Email, requests and routing
  • Data extraction and document management
  • Reports, summaries and internal FAQs

Included

Mapping, flow design, integration, testing, error handling, documentation and training.

03Enterprise knowledgeAI Knowledge Assistant

Makes procedures, manuals and distributed information easier to consult, maintaining the connection with the sources.

The system

  • Quality analysis, structure and permissions of sources
  • Semantic search and conversational interface
  • Response control, testing and governance

Applications

Employee support, onboarding, technical consultation and assisted access to commercial information.

04InfrastructurePrivate AI and local models

Evaluate local models only when there are concrete requirements on data, control, volumes or dependency on external services.

Verification of feasibility

  • Data, volumes and quality required
  • Hardware, performance, limits and costs
  • Access, security, support and maintenance

Principle

A local model is not automatically safer or more convenient: the choice is demonstrated by the technical and economic requirements.

Applied artificial intelligence

Before choosing a model, identify the right business problem.

Evaluate your use case