--- title: "Managed AI Services | Margins" description: "Keep your AI working after it goes live — monitoring performance, reliability, cost, usage and business impact in production." url: https://margins.agency/services/managed-ai --- Managed AI # Keep your AI working after it goes live. Production AI needs more than hosting and technical support. It needs to be monitored, evaluated, maintained and continuously improved as models, data, users and the business around it change. Margins operates the AI systems we put into production — monitoring performance, reliability, cost, usage and business impact so your team doesn’t have to build an AI operations function internally. - Talk to our AI team - See how it works ↓ From go-live to everyday operation. Go-live isn’t the finish line ## It’s when the real work starts. Before production, an AI system operates against assumptions and test cases. After production, it encounters reality. - New data. - New questions. - New edge cases. - Different user behaviour. - Changing business conditions. - New models. - Changing costs. - Systems going offline. - Processes evolving. - Thousands of interactions nobody could predict. AI doesn’t become static when you deploy it. Neither should the team responsible for it. Why Managed AI ## Traditional monitoring tells you whether the system is running. AI monitoring needs to tell you whether it’s working. Conventional application ● Running - Is the server up? - Did the API respond? - How long did the request take? - Did an error occur? Those things still matter. AI system? Working - Was the answer good? - Did the model use the right information? - Is output quality changing? - Are users actually adopting it? - Are costs increasing unexpectedly? - Are certain requests repeatedly failing? - Should a human have been involved? - Is the system producing the business outcome it was built for? Healthy infrastructure does not necessarily mean healthy AI. Managed AI operations ## One operating layer across the production system. Six dimensions. The last one is what makes this Margins Managed AI rather than DevOps for AI. 01 / 06 ### Is the AI performing as intended? - Model outputs - Response quality - Retrieval quality - Accuracy - Failure patterns - Evaluation metrics 02 / 06 ### Can the business depend on it? - Availability - Latency - API failures - Workflow failures - Integration health - Infrastructure 03 / 06 ### Is it operating efficiently? - Model usage - Token consumption - Infrastructure costs - API costs - Cost per workflow - Cost trends 04 / 06 ### Is the organization actually using it? - Active users - Workflow adoption - Feature usage - Drop-off - Interaction patterns - Human overrides 05 / 06 ### Where is the system getting things wrong? - Edge cases - Low-confidence outputs - Hallucinations - Incorrect retrieval - Unexpected behaviour - Recurring exceptions 06 / 06 · what makes this Margins Managed AI ### Is it changing what it was built to change? - Time saved - Work automated - Revenue protected - Errors reduced - Capacity created - Decisions improved The operating loop ## Don’t just observe the system. Improve it. 1. 01 — **Monitor** — Continuously observe the technical and AI-specific signals that matter. 2. 02 — **Detect** — Identify degradation, failures, anomalies, unusual cost or recurring problem patterns. 3. 03 — **Investigate** — Determine whether the root cause sits in the model, data, retrieval, prompt, workflow, integration, infrastructure or user behaviour. 4. 04 — **Act** — Correct the problem or route it to the engineering discipline required. 5. 05 — **Verify** — Confirm that the change actually improved system behaviour. 6. 06 — **Improve** — Use what production teaches us to make the system better. Monitoring without an improvement loop is just a dashboard. Production never stays still ## Imagine a customer-facing AI system has been running successfully for six months. Then something changes. Traditional support waits for something to break. Managed AI asks: what changed, what does it affect, and what should we do about it? End-to-end operations ## AI is a system, not a model. A failure anywhere in the stack can become an AI problem for the user. Margins can investigate across the complete system rather than treating each component as somebody else’s responsibility. 1. 01 **Application** Interfaces · Users · Workflows 2. 02 **AI** Models · Agents · Prompts · Tools · Evaluation 3. 03 **Knowledge** RAG · Vector stores · Documents · Enterprise knowledge 4. 04 **Data** Databases · Pipelines · Operational data 5. 05 **Integrations** ERP · CRM · APIs · Internal systems 6. 06 **Infrastructure** Cloud · Compute · Networking · Storage Powered by Rivermind ## The operational layer behind Managed AI. We repeatedly encountered the same problem: once AI enters production, enterprises need a way to connect models, data, tools and workflows while maintaining visibility and control over what is happening. So we built Rivermind — the orchestration, integration and operational control layer behind our Managed AI service. [Explore Rivermind (opens in a new tab)](https://getrivermind.com/) 1. 01 **Orchestrate** — Models, agents and workflows. 2. 02 **Connect** — Enterprise data, applications and tools. 3. 03 **Observe** — Execution, performance and operational behaviour. 4. 04 **Control** — Rules, routing, access and human intervention. 5. 05 **Improve** — Use production information to evolve the system. Deployed around your business ## Managed doesn’t have to mean handing over your intelligence. Margins can operate AI inside the technology environment appropriate for the customer. ### Your cloud AI operating within the customer’s existing cloud environment. ### Private environment Dedicated architecture with controlled access and data boundaries. ### On-premises AI deployed locally where business, operational or regulatory requirements demand it. We manage the system. You retain control of the business assets behind it. Business observability ## The most important AI metric may not be an AI metric. Suppose a predictive system has **95%** technical accuracy. Is that good? It depends 1. 01 If nobody acts on its predictions, perhaps not. 2. 02 If it generates too many alerts, perhaps not. 3. 03 If users don’t trust it, perhaps not. 4. 04 If it identifies problems after employees already know about them, perhaps not. 5. 05 If it costs more to operate than the value it creates, definitely not. AI metrics - Quality - Failures - Cost Operational metrics - Adoption - Response time - Actions taken - Exceptions Business metrics - Revenue - Capacity - Risk - Speed We don’t only ask whether the AI is performing. We ask whether the business is performing differently because of it. Real production AI ## What it actually takes to operate. [All client outcomes →](https://margins.agency/client-outcomes) Marikomerc · Predictive AI in distribution ### A production system analyzes purchasing behaviour to identify customers showing early signs of revenue decline. Operating it means more than keeping a model online. It means ensuring new transaction data continues flowing correctly, seasonal behaviour remains accounted for, detections remain useful, and commercial teams receive signals early enough to act. 94.7% · 43-day early warning [Explore the outcome →](https://margins.agency/client-outcomes/marikomerc) CIAK · AI inside field sales ### An AI mobile cockpit combining purchasing patterns, ML detection, customer briefings, spoken reporting and an AI assistant inside the customer’s cloud. Operating a system like this means monitoring not one model, but the complete flow between data, AI, enterprise systems and field users. [Explore the outcome →](https://margins.agency/client-outcomes/ciak) Unija · AI inside payroll operations ### An AI operations layer works across documents, portals, CRM and ERP workflows while humans handle exceptions requiring judgment. Here, successful operation depends on continuously understanding where automation succeeds and where work is still falling back to people. [Explore the outcome →](https://margins.agency/client-outcomes/unija) Operate + evolve ## Keep the system healthy. Keep the system moving forward. ### Operate the system. - Monitor performance. - Maintain reliability. - Control cost. - Track quality. - Observe adoption. - Identify problems. - Maintain production health. Forward Deployed Engineering ### Evolve the system. - Work with users. - Investigate operational friction. - Implement improvements. - Adapt workflows. - Handle new requirements. - Solve emerging problems. - Extend capabilities. [Explore Forward Deployed Engineering →](https://margins.agency/services/forward-deployed-engineering) Managed AI keeps the system healthy. Forward Deployed Engineering keeps it moving forward. AI without building an AI operations team ## Your company should get the value of AI without becoming an AI company. Most traditional businesses don’t need full-time specialists in every one of these areas. They need access to the capability when the system requires it. Margins provides the operating layer and specialist engineering organization behind the AI while your company stays focused on its own business. - AI engineering - Machine learning - Software engineering - Data - Cloud infrastructure - MLOps - Evaluation - Observability - Security - Enterprise integrations Instead of ten hires **One partner** Own the capability. Not the overhead. The service ## Ongoing responsibility for production AI. Every engagement is shaped around the system and the service level it needs. It can include: 01 ### Continuous monitoring Observe system health, model performance, workflows and integrations. 02 ### AI evaluation Measure output quality and identify degradation or recurring failure patterns. 03 ### Incident investigation Investigate technical and AI-specific production issues. 04 ### Cost management Track and optimize infrastructure, model and API usage. 05 ### Usage & adoption Understand how people interact with the system and where adoption is changing. 06 ### Performance reporting Provide regular visibility into system performance and business KPIs. 07 ### Maintenance Keep dependencies, integrations and operational components healthy. 08 ### Optimization Identify opportunities to improve quality, reliability and efficiency. 09 ### Specialist escalation Bring the relevant Margins engineering discipline into problems that require deeper intervention. From model metrics to management visibility ## Know what your AI is doing for the business. One system, three perspectives. CEO → business impact. COO → operational impact. CTO → system performance. Managed AI · reporting Illustrative UI — not client results **Business** CEO sees business impact **€1.2M** Revenue exposure identified **1,840 h** Manual work avoided **68%** Workflow automated **Operation** COO sees operational impact **87%** User adoption **14,320** AI-assisted workflows **6.4%** Human escalation **Technology** CTO sees system performance **98.7%** Successful execution **1.4 s** Median latency **€0.17** Cost per workflow Getting started ## From production system to managed operation. If Margins built the original implementation, much of the first four steps already exist — **another benefit of working end to end.** 1. 01 — Understand — Review the system, architecture, models, integrations and business objectives. 2. 02 — Baseline — Establish the technical, AI, operational and business metrics that matter. 3. 03 — Instrument — Put the required monitoring, evaluation and operational visibility in place. 4. 04 — Transition — Establish ownership, escalation paths and operating procedures. 5. 05 — Operate — Continuously monitor the system and respond when something changes. 6. 06 — Improve — Use production information to identify where the system can perform better. End-to-end by design ## The best time to think about AI operations is before deployment. If Margins is involved from the beginning, we can design observability, evaluation, cost visibility and business measurement into the system itself. 1. 01 **Discover** Define the outcome. 2. 02 **Design** Define how it will be measured. 3. 03 **Build** Instrument the system. 4. 04 **Deploy** Establish production visibility. 5. 05 **Operate** Manage performance. 6. 06 **Improve** Use what production teaches us. Production operations shouldn’t be an afterthought. They should be part of the architecture. Why Margins ## The team that understands what was built can keep it working. 01 ### AI engineering depth Understand the models, agents, retrieval and intelligence inside the system. 02 ### Full-stack capability Investigate problems across software, data, integrations and infrastructure. 03 ### Proprietary operations technology Rivermind provides an operational foundation for enterprise AI. 04 ### Business visibility Connect technical performance to operational and business metrics. 05 ### Forward Deployed Engineering Bring engineering directly into the operation when the system needs to evolve. 06 ### End-to-end ownership The same partner can identify the opportunity, build it, deploy it and stay responsible in production. **40+** Skilled professionals **60+** Projects delivered **$100M+** Measured client impact **#11** Deloitte Technology Fast 50 Central Europe ## Related capabilities ### [AI Engineering](https://margins.agency/services/ai-engineering) Design and build production AI systems. Explore AI Engineering → ### [Forward Deployed Engineering](https://margins.agency/services/forward-deployed-engineering) Put senior engineering close to the operation to continuously adapt the system. Explore FDE → ### [Rivermind](https://getrivermind.com/) The operational layer behind enterprise AI. Explore Rivermind ↗ (opens in a new tab) ### [AI Transformation](https://margins.agency/services/ai-transformation) Build the organizational capability to deploy AI systematically across the business. Explore AI Transformation → AI in production ## Your AI is live. Now make sure it keeps working. Margins can take ongoing responsibility for monitoring, operating and continuously improving production AI — without requiring you to build an entire AI operations organization internally. [Talk to our AI team](https://margins.agency/contact) Build it once. Operate it every day. Improve it continuously.