From prototype to production
Move beyond demos into systems that meet reliability, security, and operational requirements.
Related capability
Enterprise AI Engineering
Production engineering across models, tools, data, infrastructure, governance and operations.
Platform architecture
The production AI stack
A production AI system spans experience, reasoning, models, tools, data, platform, infrastructure, and governance. The architecture must work as one stack.
Application Experienceusers / workflows
Agents / LLM Logicreasoning / orchestration
Modelsselection / routing
Tools / MCPapproved capabilities
Data / Retrievalcontext / grounding
AI Platformgateways / usage controls
Infrastructuredeployment / reliability
Governance / Monitoringevaluation / oversight
Multi-model architecture
Route workloads across models and providers based on task, cost, and policy.
Controlled deployment
Keep sensitive data and decision logic within approved boundaries.
What Gromnii builds
Enterprise AI engineering capabilities
The final stack can use only the layers the requirement needs, while preserving clear interfaces between them.
01Custom AI applications and embedded AI features
Put AI inside the workflow, product, portal, or internal system where users already do the work.
02LLM application development and context engineering
Design prompts, context, retrieval, tools, and output structure around the task rather than treating the model as the whole application.
03Agent infrastructure and MCP tooling
Give agents controlled access to capabilities through explicit runtimes, tools, state, permissions, and observability.
04AI platform engineering and model gateways
Create shared access, routing, controls, and operational visibility across multiple AI applications and model providers.
05Private and controlled enterprise AI deployments
Design VPC, hybrid, isolated, or on-premise patterns where data handling and system boundaries require greater control.
06Evaluation, observability, and operational controls
Measure quality and behaviour continuously so production AI can be changed, governed, and supported with evidence.
Production control
Production controls belong in the stack
Evaluation, security, observability, cost, approvals, and lifecycle operations are architecture layers, not post-launch add-ons.
Quality
Evaluation and regression testing.
Cost
Usage and routing visibility.
Security
Access, data, and tool controls.
Operations
Monitoring and lifecycle ownership.
Discuss the requirement.
This page remains available for broader AI engineering work that spans models, applications, data, infrastructure and production controls.