Generative AI platforms and agentic systems, engineered and operated like production infrastructure.
CirOps designs, deploys, and operates GenAI platforms and agentic AI systems on AWS, including application integration, agent workflows, retrieval, evaluation harnesses, and Model Context Protocol integrations. Infrastructure, application, and model-lifecycle responsibilities are scoped separately for the use case.
The Adoption Gap
Move GenAI from prototype to production
A model works in a notebook. A demo impresses the room. Then the project stalls, because a working prototype and a production system are different problems. Production needs monitoring, cost controls, guardrails against bad output, and an integration layer that does not break when a connected system changes.
Moving a GenAI system from prototype to production requires clear platform ownership, security controls, observability, and cost governance.
What We Deliver
From platform architecture to agentic systems in production
GenAI Platform Architecture
We select and architect the platform for the problem: foundation model selection, build-versus-buy tradeoffs, and a deployment path from prototype to a production system with defined SLOs.
Agentic AI System Design
Multi-agent orchestration, tool use, and task delegation patterns for systems where an AI agent takes action, not just generates text. Designed with defined boundaries on what an agent can and cannot do.
Model Context Protocol (MCP) Integration
We build and operate MCP servers that connect AI agents to your internal systems and data sources through a structured, auditable interface, rather than ad hoc API glue code.
Retrieval and Grounding
Retrieval pipeline design and grounding strategy so agent output stays tied to your actual data, reducing hallucination risk. Vector store and API infrastructure is provisioned through Managed AI Infrastructure.
Evaluation and Guardrails
Output evaluation frameworks, prompt-injection defenses, and guardrail configuration use defined permissions, evaluation gates, monitoring, and escalation paths. These controls reduce risk but do not guarantee agent behaviour.
AI Cost and Usage Governance
Token usage monitoring, model routing by cost and quality tradeoff, and inference cost controls from day one, since AI compute spend behaves differently from traditional cloud spend.
Is This Right For You?
Built for teams past the prototype stage
- →Product teams with a GenAI feature stuck in prototype because no one owns production deployment, monitoring, or cost control for it.
- →Companies that want an agentic AI system connected to internal tools and data, built on a structured integration layer instead of one-off scripts.
- →Engineering teams that have a model working in a notebook and need the platform, guardrails, and operations around it before it reaches customers.
- →Leadership teams that need AI cost exposure understood and governed before scaling a GenAI feature to full production traffic.
Process
How the engagement works
Use Case Scoping
We define the specific problem the AI system needs to solve, the data and systems it needs access to, and the production requirements: latency, cost ceiling, and accuracy bar.
Platform and Agent Build
We architect and build the platform: model selection, retrieval and grounding pipeline, agent orchestration, and MCP integrations to the systems the agent needs to act on.
Guardrails and Production Deployment
Evaluation framework, guardrail configuration, and a staged rollout to production, with monitoring and cost controls active before general availability.
Ongoing AI Operations
The platform runs under the same operational discipline as the rest of your cloud: monitored, patched, cost-governed, and reviewed on a regular cadence.
Delivered, Not Theoretical
Production controls and operating boundaries
The engagement defines permissions, evaluation gates, monitoring, cost allocation, escalation paths, and the ownership boundary after launch. Any ongoing operations are scoped separately around the platform and application responsibilities CirOps is authorized to own.
Common questions
Related services
You might also need.
Managed AI Infrastructure
The GPU compute, model serving, and vector database infrastructure the platform runs on.
MLOps
Model delivery pipelines, registry, and monitoring where the use case owns a model lifecycle.
Cloud Architecture Review
A no-cost review of the infrastructure your AI platform will run on.
Ready to move your GenAI platform from prototype to production?
Request a no-cost Cloud Architecture Review. We assess the infrastructure your AI system will run on and give you a clear picture of what production deployment, guardrails, and ongoing operations would look like.