DataSafeHouse
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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.

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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.

Core 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.

  1. Discovery and governance alignment

    Define target workflows, risk boundaries, data readiness, and stakeholder responsibilities.

  2. Build and integrate

    Implement platform controls, configure integrations, and build pilot application workflows.

  3. Validate and productionize

    Perform scenario testing, policy verification, and operating model readiness for launch.

  4. 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.

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