Agree on an observable product outcome
Define what a useful answer, extraction or code suggestion looks like. Include cases where the feature should abstain or ask for more information.
Turn those requirements into a small evaluation set. The resulting acceptance criteria should guide model choice rather than a preference for a particular provider.
Build a stable application interface
Place the TextCortex client behind a server-side function that accepts your application’s task inputs. Keep keys, model identifiers and generation settings out of the browser.
This boundary lets developers switch the selected model while preserving the feature’s public interface. It also provides a place for limits, logging and error handling.
Compare models with shared evidence
Evaluate proprietary and open-weight candidates through the same TextCortex connection. Show the product team representative outputs, failure rates, latency and cost per accepted result.
Keep model names out of blind quality scoring when useful, then reconnect the scores to operational constraints such as EU hosting and usage cost.
Roll out a feature, then expand model choice
Start with a small, observable workload and a clear rollback route. Add new models when there is evidence that they improve the feature or enable a new one.
Use the software-vendor API page for integration structure and the rollout guide for production preparation.