omnifact
Omnifact gives IT and compliance teams a managed AI platform with multiple model providers, assistants grounded in internal documents, and controls for data handling and access. It helps organizations deploy team AI while connecting it to existing systems.
Who it's for
- IT and security teams at regulated organizations that need to manage AI models, user access, and data flows centrally.
- Compliance and legal teams that want assistants for internal policies and regulatory questions, with source references and auditability.
- Department teams in finance, public administration, or healthcare that need AI assistance based on internal documents and processes.
How it fits your workflow
Administrators configure model and capability access by group, set up permissions, and connect internal sources or business applications through available integrations, APIs, or MCP.
Teams use Chat or create Spaces from internal documents, policies, and other knowledge sources. Spaces can be shared selectively, and answers can include source references.
The Privacy Filter masks or pseudonymises sensitive information before requests go to external models. Organizations can start with a pilot group, validate privacy and usage, and roll out incrementally.
Pricing
The vendor doesn't publish prices. Check with omnifact directly.
Prices checked on Oct 9, 2026 from the vendor's site. They can change; confirm before you buy.
Key features
- Team AI chat
- Generative AI chat for teams, with centrally managed access to supported models and capabilities.
- Spaces
- Create AI assistants using internal documents, policies, and other knowledge sources. The page says answers can include source references and Spaces can be shared selectively with teams or user groups.
- AI Gateway
- An OpenAI-compatible endpoint for accessing models through a single gateway.
- Multiple AI model providers
- Administrators can provide centrally managed access to models from OpenAI, Anthropic, Mistral, and Google, and control which models and capabilities are available to each group.
- Privacy Filter
- Automatically detects and masks or pseudonymises sensitive information before a request is sent to an external AI model.
- Enterprise integrations and access controls
- Connect internal data sources and business applications, including CRM, DMS, and databases, through available integrations, APIs, and MCP. The page also describes SSO, role-based access, and group-based permissions.
Works with
- CRM, DMS, and databases
- APIs and MCP
- Enterprise SSO
- OpenAI, Anthropic, Mistral, and Google models
Limitations to know
- The exact scope of an integration depends on the target system and use case.
- The appropriate pricing setup depends on user count, model selection, and expected usage; the page does not provide specific prices.
Alternatives to consider
-
Privatemode
Choose it if you need an API for AI services that keep data encrypted during processing, rather than Omnifact's team platform with Spaces, internal knowledge assistants, and enterprise access controls.
Getting started
- Define a pilot group and specific use cases.
- Book a live demo or use the website's Try for Free option.
- Set up privacy controls, permissions, and relevant internal knowledge sources with Omnifact's onboarding support.
- Validate the pilot's privacy, permissions, usage, and business value before expanding deployment.
Frequently asked questions
Is the platform GDPR-compliant?
The page says the platform is designed for professional use with sensitive business data. Its Privacy Filter detects and pseudonymises personal and confidential information before a request is sent to an AI model, and Zero Data Retention models are available.
Is our data used to train AI models?
The page says business data is not used to train connected AI models and that contractual data-processing and Zero Data Retention agreements are in place for supported models.
Where are the platform and our data hosted?
The platform is operated in Germany. The page also describes options to restrict access to European or Europe-hosted models, and says dedicated hosting and on-premises scenarios are available for specific infrastructure and compliance requirements.
Can we use our own documents and internal knowledge?
Yes. Spaces can combine internal documents, policies, and other knowledge sources into AI assistants. Answers can include source references, and Spaces can be shared selectively with teams or user groups.
Which AI models can we use?
The platform provides centrally managed access to models from multiple providers, including OpenAI, Anthropic, Mistral, and Google. Administrators decide which models and capabilities are available to each group.
Can the platform connect to our existing systems?
The page says internal data sources and business applications can be connected through available integrations, APIs, and MCP. The exact scope depends on the target system and use case.
How are permissions and user groups managed?
Administrators can assign access to models, capabilities, and Spaces by group. Enterprise options for central identity and access management are also available.
How do pricing and AI usage work?
Costs consist of a platform licence and a transparent AI allowance pooled across the organization. Model costs are passed through without hidden token markups. The appropriate setup depends on user count, model selection, and expected usage.
How do we get started?
The page recommends starting with a clearly defined pilot group and specific use cases, then validating privacy, permissions, usage, and business value before rolling out step by step.