Service
Private AI Platform
Models, gateway and retrieval on your cloud. Your documents answer questions with the access rules they already have. Every call logged, every cost traced to a team.
- Engagement
- Fixed scope
- Typical duration
- Typically 4 to 8 weeks
- Ideal for
- Companies with data that cannot leave, several teams building on AI, or a first AI product heading for production.
Problems it solves
Sound familiar?
- Teams call five AI providers directly with five sets of keys and no visibility of spend.
- Internal knowledge lives in wikis, drives and mailboxes and nobody can find it, including the AI.
- Sensitive data cannot leave the country or the tenant, so the useful AI products are off limits.
- Nobody can answer "which model, which data, which cost" for the AI features already in production.
Outcomes
What changes
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One gateway for all model traffic, with authentication, spend limits, logging and provider fallback.
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Retrieval over your documents that respects the permissions of the person asking.
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Models running where the data must stay. Your VPC, your tenant, your region, or your own hardware when that is the requirement.
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Evaluation and cost reporting, so every AI feature has a quality score and a monthly number.
Deliverables
What you receive
- Platform architecture with data flow, residency and access decisions recorded
- Gateway, vector store, ingestion pipeline and model endpoints as infrastructure code
- Access model tied to your identity provider (Entra ID, Keycloak, Okta) with audit logging
- Evaluation harness, dashboards and a cost model per team or product
How it works
How the engagement works
A private AI platform is mostly plumbing done properly. The model is a component. The value is in the gateway every call goes through, the retrieval that respects who is asking, the identity that decides what an agent can reach, and the numbers that tell you what it all costs.
I build it on the cloud you already run, with the same discipline as any production system: infrastructure as code, least privilege, encryption, logs and a rollback path. Where regulation says the data stays, the model comes to the data.
The platform ends up owned by your team, with the decisions written down and the pipelines they need to keep evolving it.
Related work
Where this engagement has been applied
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-71% first-response time on routine categories
Built an agent workflow that classifies, enriches and drafts answers for inbound support tickets, resolves the routine ones with one-click approval and escalates the rest with full context. First-response time fell while a person stayed on every risky reply.
Next step
Start with Private AI Platform.
Send a few lines about your setup and what is failing. I reply with the questions I need answered before we scope, and a slot for a first call.