APPLIED AI / ENTERPRISE AI PLATFORMS
One foundation for AI
We build the shared platform every team uses to run AI: one gateway, one set of controls, any model.
When every team picks its own model, keys and logging, nobody can see costs, risks or what data is leaving. A shared AI platform gives every team the same foundations — gateway, routing, prompt and version control, evaluation, observability and cost management — in your own cloud account, on Amazon Bedrock, Google Cloud Vertex AI, Microsoft Foundry or direct APIs.
What we do
LLM gateway
One controlled entry point for model access, with authentication, data rules and logging applied the same way for every team.
Model routing
Send each task to the model that fits it on quality, cost and data rules, and switch models without rewriting applications.
Evaluation
Shared test sets and regression checks so a prompt or model change is measured before it reaches users.
Cost control
Usage visible by team and use case, with budgets, alerts and caching where it helps.
Our solutions
The gateway, identity, logging and network controls deployed in your own cloud account.
- Gateway with access policies per team
- Private networking and key management
- Central logging and observability
Prompt and version control, routing rules and evaluation pipelines.
- Versioned prompts and configurations
- Routing across Claude, OpenAI and open-weight models
- Evaluation gates before release
Know what AI costs, who is spending it and what it returns.
- Usage and cost by team and use case
- Budgets and alerts
- Optimization through routing and caching
How it runs
Assess
We review current AI use, data rules and cloud estate, and agree what the platform must enforce.
Design
A target architecture for gateway, routing, evaluation and cost control in your cloud.
Build
Infrastructure as code, onboarding of the first use cases and runbooks.
Run
We operate and extend the platform, or hand over to your platform team.
What you get
- A platform architecture and decision record.
- A gateway and routing layer deployed in your cloud account.
- Shared evaluation pipelines and test sets.
- Cost dashboards by team and use case.
- Onboarding guides for new teams and use cases.
Questions
Do we need a platform before our first use case?
Not always. We often build the first use case and the minimum platform it needs together, then extend the platform as more teams join.
Can we use more than one model provider?
Yes. The platform is model-neutral by design, so you can use Claude, OpenAI models and open-weight models side by side and change them later.
Which cloud do you build on?
The one your estate already uses. We build on Amazon Bedrock, Google Cloud Vertex AI and Microsoft Foundry, or with direct APIs.