Overview
At House Edge I worked on integrating AI capabilities into production applications. The interesting part was never the model — it was turning an AI capability into a feature that behaves well inside a real product, for real users, without lowering the engineering bar.
Challenge
A lot of “AI features” work in a demo and fall apart in production. LLM calls are nondeterministic, they fail and time out, they cost money per request, and they touch real user data. Wiring an API call into a UI is trivial; making that call reliable, observable, testable, and maintainable is the work.
Constraints
The features had to hold to the same production standards as the rest of the platform — no separate, lower bar because “it’s AI.” That meant treating the LLM as what it is: a nondeterministic external dependency with real failure modes, real latency, and real cost per request, sitting in the path of real user data.
Architecture
The integration lived at the application layer: services wrapping the Claude API, the Model Context Protocol (MCP) for tool and context integration, and the frontend and backend surfaces needed to expose LLM-powered features to users. Keeping the integration clean and extensible was a design goal in itself — models and requirements change, and a brittle one-off would have to be rebuilt.
Implementation
The production discipline was the same as for any other part of the system:
- Error handling and fallbacks for a nondeterministic dependency that can fail or time out.
- Validation of inputs and outputs on both sides of the model call.
- Logging and observability, so the feature’s behavior in production is inspectable.
- A testing approach that accounts for variability rather than assuming deterministic responses.
Working alongside AI specialists meant being clear about the boundary: they shaped model behavior; I made sure the surrounding software was production-grade.
Lessons
The principle I hold now: AI has to be integrated as a product capability, not bolted on as a novelty. Observability, error handling, security, maintainability, and UX apply to an AI feature exactly as they do to anything else. And this wasn’t my first exposure to AI — I’d worked on AI chatbot projects years earlier at Softwow — but it’s where I learned that the hard, valuable part is the engineering around the model, not the call to it.