Start with the published use case
The published atares customer story is useful context for understanding enterprise AI needs. Read the original story for customer-specific details rather than treating a customer logo as evidence for an API benchmark.
The architecture below is guidance for your own application. It does not describe an atares model deployment, measured API performance or migration.
Turn the business task into a model request
Define the input, the permitted information sources and the expected output. Add acceptance criteria that can be checked by the people who use the result.
Build retrieval and access controls in your application. Send only the necessary authorized context to the selected TextCortex model, and validate its output before passing it onward.
Keep the model choice replaceable
Use a common TextCortex API client and store the selected model identifier in configuration. Compare proprietary and open-weight models against the same task.
Choose EU-hosted routes where the workload requires them. Keep a record of the model and route behind each production configuration.
Measure the outcome your application delivers
Track accepted results, failures, time and API usage. Use these observations to decide whether a new model improves the application rather than changing models only because a new version is available.
Follow the multi-model rollout guide and model selection guide to make the next step concrete.