Building AI is one thing. Keeping it working is another.
An AI feature has to keep working after the first demonstration. Models change, data changes, users find unexpected cases, and operating costs become real. We bring software engineering discipline to AI systems, from testing and deployment to monitoring, maintenance and controlled updates.
A model is one part of a running system.
Traditional MLOps is mostly about training, versioning and deploying machine-learning models, and it remains important. Many current applications also depend on externally hosted language models, retrieval, agent orchestration and ordinary application services.
Engineering those systems means looking after the model’s behavior and the application around it together: how outputs are validated, how failures are handled, and what happens when either side changes.
Six areas of ongoing care.
Testing and evaluation
Checking behavior on representative inputs and on the cases that have gone wrong before.
Deployment and integration
Fitting AI components into application releases that are reliable and repeatable.
Observability
Tracking requests, failures, latency, usage and operational outcomes, so problems can be found and traced.
Model and data changes
Managing changes that can alter behavior, such as a new model version or a shift in the data.
Cost and performance
Understanding token consumption, compute use and response time before they surprise anyone.
Operational ownership
Troubleshooting, maintaining and updating the system, with rollback or fallback behavior when something fails.
A general engineering lifecycle, repeated for as long as the feature is in use. It does not describe any particular customer’s implementation.
Operational controls in a production AI application.
In the finance operations platform Founding Minds engineered for Medpanel, a language-model planner proposes structured plans over 35 registered application capabilities. The model produces a proposal. The application decides what happens to it: it validates the plan, applies authorization, previews consequential changes for confirmation, and carries the work out through its own services.
The platform also tracks AI usage, response latency, token consumption and execution outcomes, with configurable usage limits. That gives the team visibility into how the AI functionality behaves and what it costs to operate.
The same thinking applies elsewhere. A model can return an answer, a classification or a proposed action, and the application has to decide how to check it, show it, record it and recover when it fails.
Agile engineering for ML-enabled systems.
Machine learning strains the assumptions agile development rests on. A task whose outcome depends on whether the data supports it is hard to estimate, and “done” stops being a yes-or-no question when the deliverable is a model. We wrote up how we handle that in an industrial experience report published in IEEE Software, co-authored with a university software-architecture group.
What goes wrong, and what we do about it.
Most AI projects that disappoint fail in operation, not in the first demonstration. The usual causes, and the practices we apply:
The most common failure is organizational: monitoring nobody reads, retraining nobody owns, a model drifting while the dashboard stays green. If nobody will own the system after launch, a simpler one that degrades visibly is usually better than a cleverer one that degrades quietly.
Engineering depth behind both offerings.
Application Modernization
Introduce AI into established applications with testing, release management, monitoring and clear operational ownership.
Application Modernization →MVP in a Box
Build AI-enabled products on the engineering foundations they need to operate and evolve beyond the first version.
MVP in a Box →Have an AI engineering challenge?
Whether you’re modernizing an existing application or engineering a new AI-native product, we can help determine the right approach.