Define the boundary of your enterprise private AI
Private AI for business starts with an explicit deployment and data-access design. The application, model, retrieval index and logs can be deployed in a customer-controlled AWS or Azure account, a private cloud or on-premise infrastructure. We document where each component runs, what it stores and whether any external model API is involved.
Connect company knowledge with RAG
Retrieval-augmented generation, or RAG, retrieves relevant internal documents to support an AI answer. An employee might ask for a technical procedure and receive an explanation linked to its approved source. Retrieval must enforce the employee’s permissions before documents reach the model. We also define how updates, deleted documents and unanswered questions are handled.
Choose a deployment you can operate
Private cloud AI can fit organizations with an established cloud environment. On-premise AI requires local capacity, maintenance and monitoring. Self-hosted models, including open-weight models where appropriate, are evaluated against your tasks, licensing constraints and hardware needs. Deployment location alone does not establish what every service does with data; that boundary must be verified for the selected components.
Access controls, audit logs and integrations
Connections to ERP and document systems use scoped credentials and agreed actions. Identity, access control, audit logs, retention, backups and updates belong in the implementation scope. Customer-controlled infrastructure still requires careful configuration and operational ownership. Security and compliance requirements are evaluated for the actual deployment rather than assumed from the word “private.”
Validate with your documents and users
A pilot uses an approved document set and representative questions. We test answer quality, source references, permission boundaries, response time and operating cost. The findings guide the model choice, infrastructure requirements and whether further business-system integrations are justified.
Related solutions: AI for everyday operations.
Which documents can the assistant use?
We start with approved PDF instructions, internal procedures, technical documentation and price lists. Each source needs an owner, a current version and clear access permissions. ERP or CRM data is included only when suitable interfaces and permissions are available.
How does it differ from a public AI tool?
An internal assistant connects answers to selected sources and company access rules. Private AI does not automatically mean all data stays on one server: data flows depend on the model, storage, logs and any external services. Before the pilot, we document where each part is processed.
When a document needs to become a record or order, explore AI document processing and business system integrations.
