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General capabilities do not meet domain accuracy, field conditions, latency or edge deployment requirements.
Build task-specific capability for professional documents, industrial vision, action sequences, equipment time series and quality prediction, from data standards through independent evaluation and deployment.
General capabilities do not meet domain accuracy, field conditions, latency or edge deployment requirements.
Define labels and data standards; separate training, development and test data; choose vision, time-series, traditional ML or fine-tuning methods; train, independently evaluate and deploy.
Data specifications, annotation or training assets, weights, adapters or an inference service, model cards, configurations, evaluation and deployment documentation.
Describe input types, correct outcomes, the current system, main problem, deployment constraints and expected deliverables. Sample transfer follows confirmation of authorization and scope.
Input types, current outcomes and metrics to improve.
Cloud, private environment or edge devices, with latency, compute and budget constraints.
Agree on base-model licensing and rights to customer-specific outputs and general methods.
Quality, stability, latency, cost per valid result and deployment fit determine the solution. Evaluation and training data are managed separately; expert review and regression gates control changes.