Production Services

When you just need it done, this is the fastest route. Upload your files, indicate languages and timing, and we’ll respond with a clear quote and delivery plan. It’s ideal for small and/or large urgent projects that still require professional handling and reliable quality.

For ongoing translation needs, we act as a long-term production partner — with dedicated linguists, clear governance, and measurable performance. We build and refine stable teams for your domains and language pairs, using best practice Workflows, Vendor Vetting and Quality Control techniques.
Quality and cost are managed through defined workflows, transparent rates, and ongoing optimization rather than ad-hoc fixes.

Systemized Delivery is for teams where ongoing work spans multiple content streams, languages, tools, and stakeholders — and the internal coordination load is starting to create drift and rework. Instead of adding more effort to the same setup, we redesign the operating model so delivery becomes repeatable: workflows that teams actually follow, assets that stay governed, and feedback loops that fix issues at the source. This can start during our Involvement in Ongoing Deliveries or as a strategy engagement (audit + redesign) and evolve into a broader operational takeover if needed.

Human translation adds value where nuance matters most. Our translators catch tone shifts, resolve ambiguity, and adapt to cultural context—something machines often miss. Our Linguists understand intent, irony, and emotion, and can clarify unclear source text when needed. In complex, sensitive, or creative content, this human insight ensures your message lands with accuracy, empathy, and impact—wherever it goes.

Machine Translation is the process of pre-translating contents using artificial intelligence. Depending on the available quantity and quality of available training data the resulting outputs are of varying quality. Whenever the quality of machine translation is superior to the quality of existing Linguistic Resources, it is a means of producing more concise and consistent translations in a shorter time. Usually, Machine Translation (MT) is followed by the so called “Post Editing” step, during which a Linguist edits the raw MT output, in order to eliminate any remaining linguistic and stylistic errors.

During Edit a Linguist makes changes to a HT or MTPE output. The goal is to remove mistakes, increase fluency and consistency and to check that the rules of the style guide are adhered to. They have total authority and their corrections aren’t necessarily checked by another Linguist.

Review (Rew) is a superficial , broad and purely diagnostic step that aims to provide the Linguist who performed the HT or MTPE step with an additional document that guides them in identifying issues with their translation. It’s purely informative and carries no punitive weight.

(Self)-Revision is a corrective step often confused with the purely
diagnostic Review step.During Revision, the linguist takes s another careful look at their own translation to identify any remaining issues. They will usually have access to a Technical Quality Assurance (TQA) report, Language Quality Assurance (LQA) report and/or a Review Report.

After translations are imported back into the final file format (e.g., a website, app build, design file, or DTP layout), issues often appear that are impossible to detect in a standard CAT preview—truncation, broken line breaks, missing context, incorrect placeholders, tag behavior, or layout-driven meaning shifts. LSO is the in-context check that flags these risks and documents the required fixes. LIP is the controlled implementation of those fixes in the final environment—adjusting strings, resolving formatting and placeholder issues, and ensuring changes don’t introduce new inconsistencies.

Proof is the final polish step after all linguistic changes have been implemented and the content is in its final output format (e.g., live page, app build, or print-ready layout). Its purpose is to catch the last remaining issues that can be introduced during implementation—typos, punctuation, spacing, line breaks, truncation, broken tags/placeholders, formatting inconsistencies, or layout-related errors. Proof ensures that the delivered file is clean, consistent, and release-ready, with no avoidable defects left for users—or internal teams—to find.

Linguistic Quality Assurance (LQA) is a diagnostic and corrective multi-step process that contributes to professional growth and skill enhancement. During the diagnostic phase a Linguist aims to identify linguistic, stylistic and terminological inaccuracies. The changes whether they are preferential or factual, are recorded and labeled. The result is the LQA report (or scorecard), which is passed back to a linguist of the preceding workflow steps who will implement or reject the changes. A third Linguist arbitrates remaining conflicts and decides on the Final Language Quality Score. This score (measured over time) can be used to guide staffing choices. The scorecards themselves are an essential learning tool for any professional linguist.

During the TQA step the Computer Assisted Translation (CAT) tool automatically generates a report with potential quality issues. During a review step the linguist will accept or reject the findings of the TQA report. The positive impact of a TQA report heavily depends on the quality of the digital resources (TB, DNT, Regex Library, NLP Model) that have been used to configure the QA tool.

Desktop Publishing is the step where translated content is applied to the final layout and prepared for delivery in the exact file format you need. This matters because languages expand and contract, line breaks change, and typographic rules vary by locale. We adjust layout, styles, spacing, hyphenation, and text flow so the result looks intentional — not “squeezed in.” Typical outputs include print-ready PDFs and packaged source files (e.g., InDesign), with controlled formatting and a clean handoff for internal teams or printers.

Software Testing checks localized software in its actual environment. It goes beyond language correctness and focuses on functional and context-driven issues that only appear in the UI: truncation, overlaps, broken line breaks, incorrect placeholders/variables, wrong locale formats (dates, numbers, currencies), directionality, inconsistent terminology across screens, and strings that don’t match user flows. The goal is to protect release timelines by catching defects early, documenting them clearly, and supporting fast fixes without creating new inconsistencies.

In-Country Review adds a client-side or market-side check to confirm that terminology, tone, and domain conventions match how the target market actually operates. In regulated or high-stakes domains (legal, finance, STEM), this step is less about “style preference” and more about correctness in context: approved terms, established phrasing, local standards, and internal consistency with your existing documents. The key is structure. We set clear review criteria, capture feedback in a usable format, arbitrate conflicts, and implement changes without turning the project into an open-ended loop. Done well, ICR increases confidence and alignment — without slowing delivery or creating contradictory edits.

A Source Text Clarification Pass identifies issues in the source that reliably create translation risk: unclear phrasing, missing definitions, inconsistent references, contradictory passages, and unspoken assumptions. Instead of letting those problems surface late as “quality issues,” we flag them early, ask targeted questions, and document decisions. This is especially valuable in legal, finance, and STEM content, where precision matters and small ambiguities can change meaning or force costly revision loops later.

Terminology Validation is a focused check of key terms and do-not-use rules before they spread across a document set. It aligns translation decisions with your internal language (product names, legal concepts, financial terms, technical vocabulary) and reduces variance across linguists and projects. Done early, it prevents the most common “review noise”: repeated term-level edits that could have been resolved once and applied everywhere.

Back Translation recreates the target text back into the source language as a control step to spot meaning shifts in high-stakes passages. It’s most useful when accuracy must be demonstrable (e.g., critical clauses, safety instructions, regulated statements), and least useful as a blanket requirement. We apply it selectively, with clear scope and criteria, so it increases confidence without turning the workflow into a slow, expensive duplicate translation process.