Choose AI candidates for a Russian-language product with a focused test

Start with the language behavior that matters

Name a narrow feature: classify a support request, extract a delivery address from permitted test data or draft an explanation from an approved help article. Then list the failures that would create extra work for your users. A polished paragraph may conceal an incorrect category, a changed product name or a date converted into the wrong format.

Build examples from authorized, de-identified material. Include your real abbreviations, mixed Russian and English product terms, incomplete sentences and common typing errors. Keep an untouched set of cases for checking a revised prompt; otherwise repeated editing can make a candidate look better only on the examples you have already seen.

Use a rubric tied to the feature

Define which dimensions are mandatory and which allow editorial correction. Grade separate answers before asking for a common conclusion. Use explicit candidates rather than Auto, preserve identical inputs and record the displayed model or agent name and date.

This is a proposed manual product check. It does not supply a language certification, a broad model ranking or an automated evaluation system. A failed or unavailable response belongs in the evidence log; quietly retrying until every candidate looks good hides operational friction.

Example: a request whose negation changes the task

Fictional Russian input: “Не отменяйте заказ. Хочу поменять адрес, но только если дата доставки останется прежней.” The customer does not want cancellation. The requested change is conditional on preserving the delivery date. A model that classifies the message as cancellation has missed the feature's central requirement even if its reply sounds considerate.

The expected artifact is a case record: intent = address change; condition = unchanged delivery date; cancellation requested = no; missing information = whether the change preserves that date. Use fictional identifiers if the output needs an order field. Do not invent a promise that the date can be preserved.

Classify this Russian customer message: [message]. Return intent, explicit conditions, actions the customer rejects and information still needed. Preserve negation. Use only the supplied message. Do not promise an operational action or infer account details. After the structured result, quote the short input fragment that supports each field.

Check deployment feasibility in a separate workstream

Resolve these questions with the current provider documentation, the actual account and responsible reviewers. The relevant answer depends on your organization and changes over time. This article does not give a legal conclusion, promise regional access or suggest bypassing service restrictions.

Colay removes provider-key setup from the end-user screening step. It does not give your product an API agreement, deployment entitlement or proof that its own route is the route your application will use. Check those separately before selecting a production dependency.

• Access: can your organization obtain and maintain the intended service under the actual account and regional conditions?
• Integration: does the intended endpoint provide the model, context and tools your feature needs?
• Data: is the planned handling of user information approved for this use and deployment?
• Operations: how will you handle timeouts, unavailable models, costs, model changes and a fallback?

Finish with two lists, not a premature winner

Your result should name candidates that pass the language gate and candidates whose integration is feasible. Only the overlap belongs in the next pilot. A strong language candidate with unresolved access remains provisional; an accessible candidate that loses negation is not rescued by convenience.

Record the critical failures and the next check beside each name. Colay credits and limits are part of the screening cost, while API workload costs need their own measurement. Start with one condition-heavy Russian example that your team can grade confidently, then test the shortlist in the actual product environment.

Questions, answered