Swap models without app rewrites
Keep one governed integration layer while providers, model families, and approval rules evolve.
Platform
A governed platform for enterprise AI applications
DataSafeHouse unifies policy-aware model access, integration connectors, grounded retrieval pipelines, and operational governance in a production-ready platform architecture.
Designed for organizations that need to deploy AI capabilities without creating unmanaged risk. Route across approved providers and models, apply app-level overrides, and track usage events through a consistent control plane.
The platform architecture emphasizes controlled access, explicit policy resolution, auditability, and operational diagnostics. Teams can route across approved providers and models, apply app-level overrides, manage connector integrations, and track usage events through a consistent control plane.
AI Model Agnostic
Every approved model provider, routed through one governed funnel
Bring Claude, OpenAI, Gemini, and the full Amazon Bedrock provider roster into a single control plane without rebuilding around a single vendor.
DataSafeHouse normalizes provider access, policy enforcement, routing, and audit events so teams can switch model providers under explicit controls while keeping one operational surface.
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.
Governed routing
One policy-aware funnel
Approved providers and model families converge into one control surface.
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ClaudeOpenAIGeminiAmazon NovaAI21 LabsCohereDeepSeekMetaMiniMaxMistral AIMoonshot AINVIDIAQwenStability AITwelveLabsWriterZ.AICore Capabilities
Everything your team needs to govern AI safely
Tenant and App Architecture
Segment environments by tenant and app, with scoped keys and app-level controls. Admin APIs include tenant/app lifecycle, key issuance and revocation, and effective policy/limit resolution endpoints.
Model Governance and Routing
Manage logical model catalogs and per-app provider model overrides. Provider discovery and model import workflows support controlled curation across Bedrock, OpenAI, Gemini, and local endpoints.
Policy Enforcement in Request Path
Apply provider, provider-model, and token constraints before requests reach model providers. Access policy and rate-limit enforcement execute in chat and model-list paths, including tenant/app/api-key scope inheritance.
Grounded Content Operations
Build context-aware applications with source ingestion and citation-backed retrieval. RAG services support transcript ingestion, context documents, chunk/embedding pipelines, and app-scoped query endpoints.
Trust Pillars
Built on a foundation of enterprise trust
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
Structured to reduce risk and accelerate outcomes
Our engagement model is structured to reduce implementation risk while accelerating delivery of real operational outcomes.
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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.
Ready to govern AI at enterprise scale?
Deploy DataSafeHouse in your environment and take control of your AI operations.