Private AI — Your Infrastructure. Your Models. Your Data.

Deploy AI locally, privately, self-hosted, on-premise, or in a hybrid architecture. Full data sovereignty. Zero third-party exposure. Complete compliance.

Deployment Options

Deploy AI your way

Choose the deployment model that best fits your infrastructure, compliance requirements, and operational strategy.

Local

Run AI models directly on local machines — workstations or edge devices. Ideal for development, testing, and ultra-low-latency inference without network dependency.

Private

AI infrastructure within your private network. All data processing, model inference, and knowledge retrieval happen within your perimeter — nothing leaves your environment.

Self-Hosted

Deploy on your own servers or private cloud. Total control over software versions, scaling policies, and resource allocation. You own the entire stack from hardware to application layer.

On-Premise

Traditional on-premise deployment within your data center. Meet regulatory and compliance requirements with physical infrastructure under your direct control.

Hybrid

Split workloads between on-premise and cloud. Keep sensitive data and inference local while leveraging the cloud for training, batch processing, or additional capacity.

Features

Beyond RAG — a complete intelligence stack

We don't just build RAG pipelines. We design complete AI knowledge and retrieval systems — combining semantic understanding, graph reasoning, persistent memory, and agentic tool use.

Semantic Search

Dense vector retrieval that understands meaning, not just keywords. Find contextually relevant information across your entire knowledge base.

Hybrid Search

Combines keyword (BM25) and semantic (vector) retrieval to maximize recall and precision. Tunable weighting for your domain.

Agentic Retrieval

Autonomous agents that decompose complex queries, plan multi-step retrieval strategies, and synthesize results from multiple sources.

Knowledge Graph

Structured representation of entities and relationships. Enables reasoning, traversal queries, and discovery of hidden connections in your data.

GraphRAG

Graph-enhanced retrieval that traverses entity relationships to answer complex, multi-hop questions that vector search alone cannot resolve.

Memory Systems

Persistent short and long-term memory for agents. Context that accumulates across sessions, conversations, and interactions over time.

Context Engineering

Systematic design of what information enters the LLM context window. Maximizes relevance, minimizes noise, and controls cost per inference.

Vector Databases

Managed vector storage with metadata filtering, namespace isolation, and horizontal scaling. Supports pgvector, Qdrant, Milvus, Weaviate, Pinecone.

Document Intelligence

Analysis, chunking, embedding, and indexing of PDFs, contracts, reports, and complex documents. Table extraction, OCR, and multi-format ingestion pipeline.

Multimodal Retrieval

Retrieval across text, images, tables, charts, and audio. Unified embedding space that understands content regardless of format.

Tool Calling

Enable AI models to invoke functions, APIs, calculators, databases, and external services. Structured output schema with validation and error handling.

MCP (Model Context Protocol)

Standardized protocol for connecting AI models to tools, data sources, and services. Interoperable agentic infrastructure with security boundaries.

Philosophy

This is not "just RAG"

Retrieval-Augmented Generation is a starting point, not a destination. We build complete intelligence systems that go far beyond document chunking and cosine similarity.

Our private AI architectures combine structured knowledge graphs, multi-step agentic retrieval, persistent memory, context engineering, and tool calling — all running entirely within your infrastructure.

The result is an AI system that does not just search — it understands, reasons, remembers, and acts.

Private AI — Query Pipeline
query.classify("complex")
agent.plan_retrieval(strategy: "multi-hop")
knowledge_graph.traverse(entities, depth: 3)
vector_db.search(embedding, top_k: 20)
hybrid_rerank(query, graph_context, vectors)
context_engineering.compress(results, budget: 8k)
memory.store(conversation_id, entities)
llm.generate(prompt, tools: available_tools)

Everything within your infrastructure. Zero external calls.
Use Cases

What can you build with Private AI?

From document search to autonomous agents — all running locally within your infrastructure.

Legal Document Analysis
Internal Knowledge Base
Medical Records Search
Financial Report Intelligence
Government Compliance
Enterprise Agentic Systems

Legal Document Analysis

Index millions of contracts, court filings, and legal opinions. Enable semantic search across case law, extract key clauses, compare precedents, and generate summaries — all within your law firm's private network. Attorney-client privilege is preserved.

Semantic Search Document Intelligence Knowledge Graph On-Premise

Internal Knowledge Base

Connect Confluence, Notion, SharePoint, wikis, and Slack into a unified AI-powered knowledge system. Employees ask questions in natural language and get precise answers with source citations — no data leaves your network.

Hybrid Search MCP Memory Systems Self-Hosted

Medical Records Search

HIPAA-compliant AI that searches medical records, clinical literature, and clinical notes. Supports differential diagnosis assistance, patient history, and cross-referencing with medical databases — completely private.

Multimodal Retrieval Vector Databases Context Engineering

Financial Report Intelligence

Analyze quarterly reports, SEC filings, earnings calls, and analyst notes. Enable natural language queries over financial data with GraphRAG linking entities across companies, sectors, and time periods.

GraphRAG Knowledge Graph Agentic Retrieval Hybrid

Government Compliance

On-premise AI for government agencies. Search classified documents, automate compliance checks, and assist decision-making with AI running in restricted environments without external connectivity.

On-Premise Semantic Search Tool Calling

Enterprise Agentic Systems

Deploy autonomous AI agents connected to internal APIs, databases, CRM, and ERP. They plan, retrieve, reason, and execute multi-step tasks — with full audit trails and human-in-the-loop controls, all running on your infrastructure.

MCP Tool Calling Memory Systems Context Engineering
Privacy & Security

Your data never leaves your control

Every component of our private AI stack is designed for data sovereignty. No telemetry. No external API calls. No data sharing with model providers.

Zero Data Transfer

All inference, embedding, indexing, and retrieval happen within your infrastructure perimeter. Nothing is transmitted to external services.

Compliance-Ready

Designed to support GDPR, HIPAA, SOC 2, ISO 27001, and industry-specific regulations. Audit logging, access controls, and encryption at rest and in transit.

Open Source Models

Run open-weight models (Llama, Mistral, Qwen, Command R, Phi) locally. No dependency on closed APIs. Full transparency and model control.

Role-Based Access Control

Granular permissions on documents, data sources, agent capabilities, and administrative functions. Integrates with your identity provider (LDAP, SAML, OIDC).

Security — Audit Log
[14:23:01] user:j.smith query("Ricavi Q3")
[14:23:01] Verification ACL: PASS
[14:23:02] recupero: 3 doc, 2 nodi grafo
[14:23:03] inferenza: llm-locale (0 chiamate esterne)
[14:23:03] trasferimento: 0 byte
[14:23:04] audit_id: a7f3c2e1
[14:23:04] response_time: 1.2s
Architecture

Full-stack private AI architecture

A reference architecture for deploying complete AI systems within your infrastructure.

Your Infrastructure (Server / Private Cloud / Edge)
LLM (Local / Private)
Embedding Model
Reranker
Agents / Orchestration
Knowledge Graph
Memory Engine
Vector Database
GraphRAG
Context Engineering
Document Store
MCP / Tools
Internal APIs
CRM / ERP
Database
File System

Ready to deploy AI on your terms?

Tell us about your infrastructure, compliance requirements, and AI goals. We will design a custom private AI architecture for you.

Start a project Explore AI Integration