ITBASS Private AI helps organizations work with internal documents and business information using AI deployed in an environment they control. Ask questions, explore information and create document drafts, with source references that make the results easier to check.
Business information is spread across policies, contracts, manuals, technical documentation and operational systems.
People spend time searching, comparing information and preparing documents. Public AI services may not fit the organization's data-handling requirements.
ITBASS Private AI brings retrieval and generation together within an agreed deployment boundary.
Search and ask questions using information indexed from approved document collections. Source references let users inspect the supporting material.
Create draft text using selected information from your organization's knowledge base. Review the generated material and its supporting evidence before it is used or published.
Organize sources and user access by project, so teams keep different collections of business information separate.
The platform supports an integration and plugin architecture for business-specific applications. Available connections and their security requirements are assessed as part of each deployment.
Instead of treating an AI answer as an unexplained conclusion, your team can inspect the referenced documents and decide whether the answer is adequate.
The current procurement policy requires finance approval and the relevant department authorization for invoices above the stated threshold [1]. Exceptions follow the delegated authority procedure [2].
Start with a defined collection of documents and approved data sources.
The platform processes supported content and creates a searchable index within the configured environment.
Users query the selected AI model through the configured private deployment.
Users examine answers, source references and document previews before relying on them.
Designed for deployment on customer-controlled servers or suitable private infrastructure. The application, stored information and inference model are configured around the organization's requirements.
Indexing and retrieving documents are not the same as training a foundation model. ITBASS Private AI does not require your documents to be used for foundation-model training.
Project-level access controls, protected credential handling and auditing of relevant operations.
Network connectivity, integrations, model hardware and additional hardening depend on the selected configuration. Restricted-network and stronger security requirements are assessed explicitly, not assumed to work with every integration.
Private AI becomes more valuable when it understands a specific business problem. The ITBASS platform provides a foundation for such applications, while specialized solutions can have their own owners, configurations and qualification requirements.
SAP-oriented change traceability and technical knowledge.
Cross-system engineering traceability linking work items, changes and artifacts, with GitHub as its first implemented integration.
The two Eurolabs offerings are distinct applications. Their supported integrations and deployment compatibility are confirmed individually. Partner solutions
Choose one business domain and a limited set of approved information sources.
Define the questions or workflows the system must handle, including accuracy, source verification, latency and data handling.
Select the infrastructure, model and integrations. Establish access controls and test the deployment.
Review representative results, limitations, operating requirements and the decision to expand.
No. The intended private deployment uses a model running on infrastructure selected for your organization. External integrations and network traffic are evaluated separately.
Document indexing does not retrain the foundation model. The standard private deployment does not require your documents for external model training.
The platform supports text-containing PDF and DOCX documents. Additional formats and OCR for scanned documents are assessed separately.
Requirements depend on the selected model, expected workload and deployment architecture. We size the environment during pilot discovery.
Restricted-network deployments can be evaluated. Full offline operation depends on how models, updates and integrations are supplied, and must be validated for the chosen configuration.
The platform has an integration architecture and specialized extensions. Specific connector availability, permissions and compatibility are confirmed before a pilot.
No AI system should make that guarantee. Our approach combines source-grounded generation, validation and human review. Pilot success criteria measure how reliably the system handles your actual use cases.
We start with a bounded pilot and qualify the intended infrastructure, security and workloads before discussing a wider rollout.
Start with one use case, approved data sources and measurable results.