Model gateways
Define the task and acceptable result for Model gateways before choosing models, prompts or supporting data.
AI & Intelligent Systems
AI Platform Engineering
Gromnii designs shared AI platform capabilities for model access, governance, usage and delivery.
Requirement
Context
Reason
Tools
Control
Outcome
When this is useful
Use AI platform engineering when multiple teams or applications need shared access to models, inference, retrieval, evaluation and policy controls. A common platform can reduce duplicated integration work while keeping routing, identity, cost and provider choices explicit.
What Gromnii builds
AI API management
Define AI API management with clear contracts, versioning, authorization, error behavior and ownership so connected systems can evolve without fragile point-to-point dependencies.
Routing & policy
Represent both the normal path and material exceptions in Routing and policy.
Inference infrastructure
Measure Inference infrastructure against task-specific quality, latency and cost limits rather than one generic score.
Usage controls
Set confidence and impact rules for Usage controls, and send uncertain cases to a person with the evidence needed to decide.
How the AI system is controlled
This reference shows one possible AI Platform Engineering arrangement. The actual design depends on the systems, constraints and controls involved.
01Applications
02AI gateway
03Models
04Data & tools
05Policy
06Observability
What matters in production
Access policy
Separate who may use models, deploy prompts, change routing, access evaluation data and administer platform services, with stronger controls around high-impact actions.
Cost controls
Track model, embedding, storage and tool-call cost by application and environment so teams can compare quality gains with the recurring expense of each design.
Reliability
Define fallbacks for unavailable models, gateways, vector stores and tools, with health checks that can route traffic away from degraded AI components.
Vendor portability
Separate application logic from provider-specific APIs where practical, but avoid abstraction layers that hide useful provider features without a real portability need.
What it can improve
Consistent model access
Provide governed gateways and common APIs so applications do not each implement their own provider logic and credentials.
Better cost control
Track usage by application, team, model and workload so routing and capacity decisions are based on actual demand.
More resilient AI services
Add routing, fallback, quotas and observability so model-provider failures or performance changes do not become application-wide outages.
Additional technical detail
Technical implementation notes for AI Platform Engineering.
Show additional technical detail
AI platform capabilities
Platform scope should follow the number of applications, providers, teams, policies, and operational controls that actually need to be shared.
01Enterprise and internal AI development platforms
Provide shared foundations for teams to build AI applications with consistent access, controls, and observability.
02Model gateways and AI API management
Centralize model access, provider routing, policy enforcement, usage visibility, and application credentials.
03Model routing and usage controls
Select models by task, quality, latency, privacy, or cost while enforcing approved usage patterns.
04Inference infrastructure and workload management
Run AI workloads with the capacity, isolation, latency, and operational visibility the application requires.
05Multi-model architecture
Use different models where their strengths matter without hard-wiring the business system to one provider.
Related solutions and industries
Discuss a Project
Describe what AI Platform Engineering should change, the systems it must work with and the constraints that matter.