AI in Production Enablement
Stabilizing, optimizing, and governing AI solutions already in production: operations, cost control, monitoring, and long-term reliability
Deploying AI to production is only the beginning. Keeping it reliable, cost-sustainable, and under control is the real work. The model works and people use it. But after go-live, problems emerge that differ from those of development: unpredictable costs, degrading performance, and quality that drops silently. We take over an AI solution that is already live and work on operations, costs, monitoring, performance, reliability, and security.
The service in brief
- Service
- Stabilization, optimization, and management of an AI solution already in operation.
- When you need it
- After go-live, when cost, performance, quality, or monitoring issues emerge.
- R.O.C.E. phase
- Control: the third phase of the Miriade R.O.C.E. methodology (Recover · Optimize · Control · Evolve), with a strong Optimize component.
- Scope
- Operations, cost control (AI FinOps), performance, monitoring, reliability, and security.
- Works on
- Solutions built by Miriade, by your team, or by third parties.
- Takes over from
- AI-Ready Data & RAG Enablement (build/MVP).
- Continues with
- MirAI (monthly managed service) and AI Context & LLM (in-depth optimization).
The question is no longer “How do we build it?” but “How do we keep it under control?”
The development phase is behind you. In production, under real load, the problems change: spending grows without anyone knowing which component weighs most, and quality can drop without any obvious failure.
How it works
An AI solution in production cannot be optimized by trial and error on the prompt. The starting point is the actual state of the system and how costs, performance, and quality behave over time.
Triage and scoping
We identify which AI solutions are in production, which problems are emerging, and which objectives and constraints apply: costs, SLAs, security.
Assessment
We analyze architecture, models, agents, tools, dependencies, logs, metrics, costs, and incidents, and deliver a map, issues, priorities, and quick wins.
Designing the changes
We define optimizations, monitoring, operational processes, and hardening, with priorities and the expected impact on costs and reliability.
Progressive implementation
We proceed through controlled changes, starting from the points with the greatest impact on costs and reliability.
Measurement
We verify the results on costs, performance, quality, and stability, comparing before and after.
Ongoing operations
We define how to keep the solution reliable and efficient over time: runbooks, incident management, periodic reviews.
We work on costs, performance, monitoring, and security
The scope adapts to the solution and its objectives. The main areas we work on:
AI Production Assessment
Map of architecture, models, agents, and tools; analysis of costs, performance, and reliability; issues, risks, quick wins, and roadmap.
AI Cost & FinOps Optimization
Cost allocation by model, agent, and feature; waste detection; actions on tokens, context, and tools; budgets and estimated savings.
Monitoring & Observability Setup
End-to-end tracing (e.g., LangSmith), technical and quality metrics, alerts and thresholds, operational dashboards, structured logging.
Performance & Reliability Optimization
Bottleneck analysis, caching and parallelization, retry and timeout handling, fallbacks, and optimization of external dependencies.
Model Lifecycle & Provider Management
Testing and comparison of new versions, deprecation management, multi-model strategies, reduced lock-in, and criteria for promotion to production.
AI Operations Model
Runbooks and procedures, incident management, rollback and versioning, escalation and responsibilities, reports, and periodic reviews.
AI Security Hardening
Assessment of exposure to prompt injection and abuse, access control, protection of sensitive data, security auditing, and logging.
Success metrics
The work is measured with concrete indicators, comparing before and after the changes.
What makes the Miriade approach different
Miriade does not treat AI in production as a simple model-tuning problem. We connect it to the real ecosystem it runs in — infrastructure, costs, data, security, processes, and teams — and combine expertise in cloud, development, DataOps, security, FinOps, and AI.
Many AI solutions do not fail because of the chosen model, but because they are not governed in production.
Where it fits in the AI path
AI in Production takes over from the build (AI-Ready Data & RAG) and continues toward the monthly managed service (MirAI) or in-depth optimization (AI Context & LLM).
AI-Ready Data & RAG
We prepare data and sources for AI and build the first solution.
AI in Production
We stabilize and govern AI that is already in production.
You are hereAI Context & LLM
We optimize the context, tools, and costs of agents and LLMs.
MirAI
Continuous monthly managed service for your AI system.
Frequently asked questions
Do you only work on solutions you built?
No. We can work on AI solutions already in production that were built by internal teams or by third parties.
Can you really reduce costs?
In most cases, spending is concentrated in a few inefficient points. Finding and fixing them often leads to significant savings without degrading quality.
How is it different from MirAI?
AI in Production Enablement is a targeted stabilization and optimization engagement. MirAI is a continuous monthly managed service for AI in operation.
Do you use specific monitoring tools?
Yes. We can use tools such as LangSmith and observability platforms, and integrate them with the tools your organization already uses.
What happens when the provider updates the model?
We manage the lifecycle of models and providers: testing and comparison of new versions, deprecation management, and criteria for promotion to production, to avoid uncontrolled changes in behavior.
How does it start in practice?
With an assessment of the solution in production: costs, performance, monitoring, and risks. From there, we define the changes and the roadmap.
The technologies we use
Some of the technologies and platforms we work with on this service.
Do you want your AI in production to be stable, efficient, and under control?
We start from your actual costs, performance, monitoring, and reliability, and build a path to optimize the solution and manage it over time — even if we did not build it.
Tell us about your AI in production
A few details about the solution in operation are enough. We will contact you for a call and define the first step of the assessment together.
Privacy and security note. Do not enter credentials, passwords, or sensitive data in the form. Access, logs, and technical information will be handled later through agreed secure channels.
