Private AI and local network
Private AI and local LLMs for companies that want control over data and access
We design AI assistants installed or confined within the company environment, with controlled access to the local network and verifiable connections to internal databases.
AI on the local network for internal data and procedures
We support companies that want to use intelligent assistants with internal data, databases, management systems and procedures while keeping access, sources and permissions under control.
How we work
A direct, client-focused approach: we listen, design, build and improve together.
01
Access restricted to the local network
We configure solutions designed for use within the company, with access limited to authorised users.
02
Queryable company databases
We connect the assistant to data and management systems to obtain answers, summaries and search results without navigating through dozens of screens.
03
MCP and natural language
We use MCP tools to allow software to receive natural-language questions and turn them into controlled actions or queries.
What we assess for local LLMs and private AI
A local LLM is an infrastructure project: it must be designed around the network, data, users and maintenance.
Why choose local AI
A local AI model lets you work on company data with more control, less dependence on external providers and greater freedom of integration.
Data under control
Documents, databases and procedures can remain on company servers, Italian VPSs or infrastructure chosen by the company.
AI assistants without the cloud
AI keeps working inside a controlled environment, without always relying on external APIs, cloud plans or third-party services.
More predictable costs
A local system reduces dependence on token consumption and allows the model to be used more freely in internal flows.
More room for agents and automation
Local models, RAG, internal tools and AI agents can work together on repetitive tasks, queries, documents and business processes.
When to use local AI
Local AI makes sense when you want to bring artificial intelligence into business processes without losing control over data, access, costs and infrastructure.
To create an internal AI assistant that answers questions about documents, procedures and company knowledge.
To use local LLM models on confidential data without sending it to external cloud services.
To query databases, management systems and dashboards with natural-language questions.
To integrate business RAG on archives, contracts, manuals, tickets, price lists or knowledge bases.
To reduce token costs, usage limits and dependence on external AI providers.
To build AI agents and automation connected to internal tools, servers and business workflows.
Privacy and compliance
A local LLM helps reduce data exposure because the environment is more controlled: internal network, authorised users, defined sources, verifiable access and rules aligned with company policies.
Services connected to private AI and data
Local LLMs, assistants and management systems require coherent architecture, privacy and integrations.
Local and private AI
We support technical choices better aligned with privacy, access and company compliance.
Chatbots and AI assistants
We build digital assistants for customers, internal teams, procedures and company information.