Consistent governance controls across internal AI integrations
A regulated organization wanted to adopt large language models across internal systems, but needed to maintain full control over data, infrastructure, and governance.
Core Purpose Tech implemented a unified LLM gateway architecture, allowing internal applications to interact with language models through a single interface while the organization retains full control over where and how AI is executed.
Each system connects directly to specific models. Tightly coupled, hard to govern.
All requests route through one control layer. Decoupled, governed, flexible.
Instead of connecting systems directly to specific models, all requests go through a central AI gateway. This creates complete control over AI usage.
Core Purpose Tech implemented a central LLM gateway layer that acts as the single entry point for all AI requests within the organization. Internal systems and user interfaces communicate with this gateway as if they were calling a single language model. Behind the gateway, requests can be routed to locally hosted models, external providers, or specialized models for specific tasks.
This architecture separates applications from model infrastructure, allowing both sides of the system to evolve independently.
Applications interact with the gateway as if it were a single language model. This means internal systems do not depend on a specific model provider.
If the organization changes models, adds new capabilities, or deploys new infrastructure, internal applications continue working without modification. This creates true model independence.
Swap or upgrade underlying LLMs at any time
Deploy new specialized models without app changes
Move between local and external as needed
An internal application sends a request to the LLM Gateway.
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Because all AI interactions pass through the gateway, the organization gains full visibility and control over model usage. This turns AI from scattered experiments into managed infrastructure.
Swap or upgrade models without touching applications
Control which systems access which models
Manage organizational AI usage policies
Full visibility over all AI interactions
Implement governance and compliance rules
Monitor and audit all model usage centrally
The same gateway powers a unified language interface for employees. Users interact with AI through a simple conversational interface while the gateway handles routing, model selection, and governance behind the scenes. This allows organizations to offer AI capabilities similar to consumer tools while maintaining enterprise-grade control.
Core Purpose Tech supports multiple deployment models to match organizational requirements.
Models running directly within the organization's infrastructure.
Dedicated infrastructure where the organization retains full operational control.
Local models combined with external providers through the gateway.
Outcome
Application teams can adopt models faster while risk, routing, and policy controls remain centralized in one operational gateway.
Consistent governance controls across internal AI integrations
Flexible routing between local and approved external models
Reduced vendor lock-in through provider abstraction
Clear audit trail for requests, model paths, and policy decisions
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Secure document retrieval and RAG systems.
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LLM gateway architecture with local and external models.
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AI embedded into real applications such as Min Beboer Parkering.
Read how the gateway architecture is applied directly inside operational workflows and user-facing case handling in production.
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