What an AI Platform Includes
Model gateway
A single API layer that routes model calls, enforces rate limits, manages API keys, handles retries and fallbacks, and logs every call with cost and latency metadata. Project teams call the gateway, not the model APIs directly. This centralises cost visibility, security, and reliability without requiring each team to implement it independently.
Shared data infrastructure
Document ingestion pipelines, vector databases, embedding generation services, and data quality frameworks available as shared services. A team building a new RAG system uses the platform's vector store and ingestion pipeline rather than deploying their own. This standardises data patterns and reduces duplication.
Evaluation framework
A shared library of evaluation metrics, test set management infrastructure, and dashboards that any AI project can plug into. Rather than each team building their own evaluation tooling, they register their evaluation criteria with the platform and run evaluations through the shared infrastructure.
Observability and monitoring
Centralised dashboards for AI system health, cost tracking by project and team, quality metric trending, and alert routing. Project teams get observability without building their own infrastructure; platform teams get organisation-wide visibility for governance and cost management.