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As organizations move beyond isolated AI experiments, a recurring pattern emerges: each team integrates models differently, manages credentials separately, and measures quality in its own way. A shared AI platform addresses this by providing common capabilities that every use case can build on.
Core platform capabilities
- A model gateway that centralizes access, routing, rate limiting, and cost attribution.
- Retrieval services that enforce the same access controls as the source systems.
- Evaluation pipelines that test quality and safety before and after release.
- Observability for prompts, responses, latency, and spend.
Security considerations
AI systems introduce new risks, including prompt injection, unintended data exposure, and over-permissioned agents. Treat model inputs as untrusted, restrict tool permissions to the minimum required, and log decisions in a way that supports review.
Most importantly, apply the same engineering discipline used elsewhere: threat modeling, change control, and continuous monitoring.
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