Kauzas AI Platform
Enterprise AI that works inside your organization's permission boundaries.
Build enterprise search, AI assistants and agents over the data you already have. Manage user, agent, tool and data-source permissions from one place.
- Works with existing data infrastructure
- Works with different models and providers
- Runs on a closed network
Capability catalogue
08 capabilities
kauzas ai
Enterprise AI capabilities on one platform
- Front end
- Agent runtime
- Cedar
- MCP
- Vector + search
- Hybrid searchKeyword and semantic search
- AgentsLangGraph
- PermissionsKeycloak · Cedar
- ToolsMCP servers
- ModelsProvider independent
- Skill packsPer customer
- GuardrailsPrompt injection defence
- ObservabilityLangfuse · budgets
Data infrastructure
Works with the data infrastructure you already have.
Kauzas AI Platform runs stand-alone and reaches the data in your ERP, CRM, IoT and document systems through a shared access layer. The platform queries those sources together through that layer. You decide the data infrastructure underneath and where the platform itself runs, according to what your organization needs.
Kauzas Data Platform
In-house · AWS Local Zone Istanbul
AWS
S3 · Glue · SageMaker Unified Studio
Microsoft Azure
Fabric · OneLake · Synapse
Google Cloud
BigQuery · Cloud Storage
Databricks
Unity Catalog · Delta Lake
Your own data lake
Existing, or built in-house
You do not have to rebuild your data infrastructure to start on enterprise AI. Kauzas integrates with the systems you already run, and adapts as those systems change.
Identity and permissions
The right data, under the right permissions, for every user.
For enterprise AI to be rolled out with confidence, each user must reach only the data they are entitled to see. Kauzas applies those controls at the point where data is accessed.
An arrangement without central authorization
- 01The agent connects to the data source under a technical account shared by all users.
- 02That account is broadly privileged, because it has to serve every user.
- 03With no per-user permissions applied as data is read, content outside a user's scope can be processed by the model.
- 04The user's permissions are evaluated only at the interface, after the answer is produced.
Authorization stays at the interface, after the answer exists.
Kauzas AI Platform
- 01The user signs in through the organization's identity provider.
- 02Access policies decide which agents and tools they can reach.
- 03Where a connector supports it, the tool connects to the data source under that user's identity.
- 04Content outside their permissions is not sent to the model.
Authorization is applied as data is read, before the model answers.
A user's identity and access rights are evaluated from the agent and tool layer through to the data source.
Capabilities
Search, agents and model management in one interface.
Skill packs, model settings and the setup of MCP servers all sit in the same interface. Setting up an MCP server is itself subject to the same authorization rules.
Hybrid search
Combines keyword and semantic search, so people reach enterprise knowledge more easily.
AI agents
Run multi-step business processes with LangGraph agents, acting within the permissions they are given.
Model independence
Choose the AI models and providers that suit your organization, and change them when you need to.
Skill packs
Define AI capabilities tailored to particular departments, sectors and business processes.
Prompt management and A/B testing
Version prompts and skill packs, and measure what a change did to the results with A/B tests.
Output quality evaluation
Measure and compare model and agent answers against the quality criteria you set.
Access to current data
Updates in your data sources are carried through to the search layer on the sync schedule you define, so agents work from current information.
Improvement from feedback
Collect user feedback and keep improving the quality of your AI applications.
Multilingual use
Build AI applications in the languages your chosen model supports.
Security controls
Apply protections against prompt injection attacks and unwanted output.
Running on a closed network
With suitable models and infrastructure components, run your AI applications in environments with no internet access.
MCP integrations
Connect agents to enterprise tools and services over MCP, with defined access permissions.
The console
Manage all your AI applications from one place.
Projects, agents, models, tools, access policies, budgets and run records are all managed from the Kauzas console.
These captures come from an installed environment; user names, email addresses and session details are blurred.
Defined management permissions for every user
In the console each administrator manages the projects, models and users within their own permissions. Those management rights are themselves defined by roles in the organization's identity provider.
Oversight
Track AI usage and cost from one place.
Follow usage statistics, costs and run history. Set usage limits and budgets per user and per group.
Run records and audit trail
Within the logging and access policies you define, authorized administrators can review AI interactions and run history. Langfuse can serve as the observability layer.
Usage limits and budgets
Plan AI usage and cost more easily with limits defined per user and per group.
End-to-end authorization
User permissions apply both when tools are reached and at the data source itself, which keeps authorization consistent across the path.
Frequently asked questions
- Do I need Kauzas Data Platform to use Kauzas AI Platform?
- No. The platform runs stand-alone and works with existing data infrastructure such as your own lakehouse, Microsoft Fabric or Amazon SageMaker Unified Studio. Kauzas Data Platform is only one of those substrates.
- Can the assistant see data the user is not permitted to see?
- Access policies are applied at the user, agent, tool and data-source layers: the user's identity is carried through to the tools they reach and to the data sources those tools connect to, and authorization is evaluated before data is sent to the model. Where a connector can carry the user's identity, authorization is applied together with the data source's own access policies; where a source does not support that, the scope is agreed during installation.
- Which AI models can be used?
- The platform is model and provider independent; it can work with whichever model or provider the organization prefers.
- Does it run in environments with no internet connection?
- The platform can be installed and operated on closed networks. In that case the large language model and the other components it connects to must also be able to run inside the same network; models supplied as an external service cannot be used on a closed network.
- Which data sources can it connect to?
- Data from different systems such as ERP, CRM, IoT and documents is made reachable through a shared data mesh, or access layer; the platform queries those sources together through that layer. Which systems are connected to that layer is decided as part of the installation.
- Is multilingual use supported?
- Yes, without a separate language switch. Multilingual use extends as far as the languages the connected model supports.
- Can AI usage be audited?
- Within the logging and access policies you define, authorized administrators can review AI interactions and run history. Langfuse can serve as the observability layer. Usage limits and budgets can also be defined per user and per group.
- How is output quality measured?
- An evaluation layer that measures model and agent answers against the quality criteria you set is available and can be switched on when needed. Prompts and skill packs support version management and A/B testing.
- Is there protection against harmful output?
- A guardrail layer protects against prompt injection attacks and unwanted output.
See how Kauzas AI Platform would work on your own infrastructure.
Let's look together at how Kauzas behaves on your existing data sources and your own permission model.