Omnifact

Omnifact is a generative AI platform built for enterprise organizations, emphasizing security, data privacy, and system integration, with the goal of replacing shadow IT and tool silos and providing a unified, long-term enterprise AI platform.

I. Main Uses Omnifact is aimed at enterprises and institutions with the highest standards for security and compliance, helping regulated organizations use AI under controllable and auditable conditions. Typical uses include: automating internal processes, handling complex customer inquiries, internal information flows and request processing, knowledge access, document understanding, and secure communication. There are corresponding solutions for scenarios in finance, the public sector, healthcare, and more.

II. Main Capabilities

1. Multi-model integration: Use market-leading models from OpenAI, Anthropic, Mistral, and Google in one interface, under unified control by enterprise IT. Administrators can decide which models and capabilities are available to each group, maintaining vendor independence.
2. Privacy Filter™: Through rule-based and AI-based detection, automatically identify and pseudonymize personal and confidential information before requests are sent to external AI models, ensuring that customer data, patient data, or internal identifiers are not transmitted to third-party providers. Supports Zero Data Retention models, and business data is not used to train models.
3. Integration capabilities: Provides 35 ready-made integrations and supports custom connections. CRM, DMS, databases, as well as team tools such as SharePoint, Atlassian, HubSpot, GitLab, OneDrive for Business, and Google Calendar can be connected through standardized APIs.
4. Identity and permissions: Supports enterprise SSO, role-based access control, and multi-tenancy. Administrators can assign access permissions for models, capabilities, and Spaces by group.
5. Spaces: Combine internal documents, policies, and other knowledge sources into secure AI assistants, providing context-relevant answers based on internal information. Answers can include source citations and can optionally be shared with teams or user groups.
6. Compliance and auditing: Provides audit logs, usage logs, and policy controls, supports zero trust and transparent operations, and meets the highest security requirements.
7. Enterprise support: Provides technical setup, employee training, administrator enablement, and ongoing support.

III. How to Use The platform uses Chat and Spaces as its core interfaces. Administrators add tools from the integration catalog and connect the enterprise's existing systems to Chat and Spaces. Users receive models, by group,access permissions for capabilities and Spaces. Organizations typically start with a clearly defined pilot group and specific use cases, validating privacy, permissions, usage, and business value before gradually rolling out.

IV. Hosting and Data Sovereignty The platform operates in Germany. Organizations can restrict access to European or European-hosted models; the enterprise edition offers optional EU residency, and dedicated hosting and on-premises deployment scenarios are also available to meet specific infrastructure and compliance requirements.

V. Key Points of Common Questions

• Compliance: The platform is designed for professional use in processing sensitive business data, integrates a Privacy Filter that detects and pseudonymizes personal and confidential information before requests are sent, and offers zero data retention models.
• Data Training: Business data is not used to train the connected AI models; contractual data processing and zero data retention agreements are in place.
• Hosting: The platform operates in Germany and can be restricted to European or European-hosted models, supporting dedicated hosting and on-premises deployment.
• Internal Knowledge: Spaces can combine internal documents and policies to form a secure AI assistant, supporting source citations and selective sharing.
• Models: Centrally manage access to models from multiple providers; administrators decide which models and capabilities are available by group.
• System Connections: Connect internal data sources and business applications through integrations, APIs, and MCP; the specific scope depends on the target systems and use cases.
• Permission Management: Administrators assign access permissions for models, capabilities, and Spaces by group, so employees only receive access to the data and tools needed for their work.
• Pricing and Usage: Costs consist of platform licensing and transparent AI credits; credits are shared within the organization, model costs are passed through, and there are no hidden token markups; the specific plan depends on the number of users, model selection, and expected usage. No specific price figures are given in the text.
• Getting Started: Most organizations start with a clearly defined pilot group and specific use cases, then roll out gradually.

VI. Limitations and Differentiation Positioning Omnifact is positioned as a scalable platform for regulated enterprises, rather than a general-purpose AI tool for individuals. Its differentiators include: support for EU residency or self-hosting in terms of data protection and hosting, a built-in Privacy Filter, audit trails, and policy controls; freedom of model choice; Spaces supporting the creation of independent AI assistants by team; and enterprise-grade settings, fine-grained access control, SSO, and training. The text does not provide technical details, performance metrics, or pricing figures beyond the specific feature list.