Swap models without app rewrites
Keep one governed integration layer while providers, model families, and approval rules evolve.
Enterprise AI. Under your control.
Put AI to work. Keep control of your data.
Connect your business data to the AI models you choose, with the access controls, policies, and visibility your organization needs.
From your first internal assistant to workflows across the business, DataSafeHouse brings model access, grounded answers, and governance together.
Your data. Your choice of models. One place to manage access, enforce policies, and understand how AI is being used.
AI Model Agnostic
Choose your models. Keep your controls.
Use approved models from providers such as OpenAI, Google, and Amazon Bedrock through one governed integration.
Manage access, routing, and usage in one place as your teams explore which models work best for each task.
Apply policy before requests leave
Provider allowlists, model-level controls, and token constraints are enforced before traffic reaches external endpoints.
Keep audit and usage telemetry consistent
Route disparate providers through one observable gateway instead of stitching together separate control paths.
Your application
Sends a request through the gateway integration.
DataSafeHouse gateway
Checks access and policy, then routes the request to an approved model.
Approved model
Processes the request and returns a response through the gateway.
Usage and audit records stay consistent across providers.
Explore model providers
Claude
OpenAI
Gemini
Amazon Nova
AI21 Labs
Cohere
DeepSeek
Meta
MiniMax
Mistral AI
Moonshot AI
NVIDIA
Qwen
Stability AI
TwelveLabs
Writer
Z.AICore Capabilities
Practical tools for putting AI to work
Give each team the right access
Separate teams and applications with scoped credentials and access policies. Manage who can use AI and which resources they can reach.
Choose the models that fit
Bring approved models from Bedrock, OpenAI, Gemini, and local endpoints into one place. Adapt model choices as your needs change.
Put your policies into practice
Set provider permissions, model rules, and usage limits. Apply those controls before requests reach a model provider.
Get answers grounded in your knowledge
Turn documents and transcripts into useful context for AI applications, with source-backed retrieval and citations that help teams check answers.
Governance
Controls your team can put to work
Security Controls
Scoped admin credentials, API key isolation, role-based console access, and guarded connector host validation.
Governance and Audit
Policy change events, admin-auth events, usage events, and connector action logs for review and compliance workflows.
Provider Flexibility Under Policy
Multi-provider support with policy controls to allow or block providers and provider-model combinations.
Deployment Flexibility
Architecture supports enterprise-hosted deployment patterns, including controlled egress policies and environment-specific service configuration.
How We Engage
From a useful idea to a working application
Start with a useful business problem. We help you connect the right data, establish controls, and build a path from pilot to production.
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Discovery and governance alignment
Define target workflows, risk boundaries, data readiness, and stakeholder responsibilities.
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Build and integrate
Implement platform controls, configure integrations, and build pilot application workflows.
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Validate and productionize
Perform scenario testing, policy verification, and operating model readiness for launch.
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Scale and optimize
Expand to new use cases while maintaining observability, governance, and reliability standards.
What could AI make possible for your team?
Bring us a workflow, a data challenge, or an idea. Let’s map out a practical next step.