Map the experience
Inventory user journeys, content surfaces, locale risks, ownership, and the points where context disappears.
Continuous localization for interfaces, help content, release notes, and product journeys.
We connect linguists to the product itself: screens, flows, constraints, user intent, and release cadence. That context produces language that fits the interaction instead of merely matching the source string.
The engagement can cover a single launch or operate as an ongoing localization lane alongside product and engineering teams.
We adapt the depth and sequence to your product or model stage, modalities, language scope, and internal team.
Inventory user journeys, content surfaces, locale risks, ownership, and the points where context disappears.
Create terminology, voice guidance, contextual notes, review paths, and locale-specific rules.
Run in-product review, linguistic QA, release triage, and a feedback loop for future builds.
What the work means, where people and AI fit, how quality is judged, and what changes the estimate.
Continuous localization for interfaces, help content, release notes, and product journeys. In practice, the work is bounded by a defined product or model decision, named audiences and locales, representative inputs, and acceptance criteria that can be reviewed.
A translated interface can still feel foreign. Users notice when labels ignore context, terminology shifts, or layouts fail under real language pressure. The useful starting point is the smallest representative flow that can expose the cause, impact, and ownership of the problem.
Map the experience: Inventory user journeys, content surfaces, locale risks, ownership, and the points where context disappears. Build the language system: Create terminology, voice guidance, contextual notes, review paths, and locale-specific rules. Ship with confidence: Run in-product review, linguistic QA, release triage, and a feedback loop for future builds.
The most useful inputs are representative source content or builds, target locales and audiences, brand and terminology guidance, release timing, known legal, safety, and and accessibility constraints. Admas can begin with a partial package, but missing context, rights, access, owners, or acceptance criteria will be made visible in the plan rather than treated as harmless assumptions.
Typical outputs include localized product strings and supporting content, terminology base and style guidance, in-context review and issue log, and release qa and handoff documentation. Deliverables are adapted to the team that must use them, with decisions, evidence, limitations, owners, and next actions made explicit.
Quality is measured against the real task and risk. Relevant evidence can include meaning and terminology accuracy, task completion in context, linguistic and functional defect severity, market acceptance, and rework and turnaround. Sampling, severity rules, reviewers, adjudication, and pass or fail thresholds should be agreed before the result is used as a release decision.
Automation can prepare files, pretranslate suitable content, enforce terminology, and flag mechanical defects. Translators, editors, market reviewers, localization engineers, testers, and program owners remain responsible for meaning, audience fit, product behavior, exceptions, and release decisions. The right allocation depends on consequence, content stability, available references, language coverage, reversibility, and the cost of a plausible but wrong result.
The estimate changes with word or asset volume, content type and risk, language pairs, workflow and file condition, review depth, turnaround, and engineering and project-management effort. Pricing should distinguish setup and discovery, repeatable units, specialist or engineering time, independent review, management, and external costs. A low unit price is not comparable if it excludes the QA cycle or shifts rework back to the buyer.
Tell us what you are building, which modalities and languages matter, and where progress is blocked.
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