Use case and success criteria
Tasks, data boundaries, expected output and measurable acceptance criteria are defined.
AI model selection identifies the model and serving approach that best fit the real workload by measuring task quality, Turkish-language performance, privacy, latency, hardware requirements and total cost.
The largest model is not automatically the best operational choice. Quality must be evaluated alongside concurrency, context size, response time, GPU memory and operating cost.
Candidate models are tested on representative tasks under equal conditions, and the recommendation includes a fallback path rather than depending on one model.
Tasks, data boundaries, expected output and measurable acceptance criteria are defined.
Models are filtered by language quality, context, license, security and hardware constraints.
Representative prompts and datasets are used to compare quality, latency and resource use.
Concurrency, GPU/CPU, memory, storage and operating cost are modeled.
The selected model, serving approach, risks and fallback options are documented.
Define the business scenario, data boundaries and measurable success criteria
Test candidate models under equal conditions with representative data and real tasks
Report the recommended model, hardware, capacity, cost and fallback path
No. Local, private-cloud and managed models are compared against data boundaries, quality, latency and cost.
Yes. Representative Turkish documents, terminology and real tasks are included in the evaluation.
We review your current environment, target and technical requirements in a 20–30 minute call. Scope, assumptions, deliverables and pricing are documented before work begins.
Request an assessment →