At Logrus IT, we deeply value the proven traditions of expert linguistic work, while continuously integrating the latest technological innovations. Today, Artificial Intelligence is becoming instrumental in numerous industries, and localization is no exception. (Read our thoughts on AI and the service industry to learn more about our overarching vision).
However, we understand that AI use raises concerns for some clients. The primary concerns are data safety (the risk of confidential information leaking into public AI domains) and output quality (unpredictable "hallucinations" or glitches that often occur when raw AI-generated texts are not properly reviewed by human experts).
To mitigate these risks and ensure that our clients receive the best value for their investment, we’ve developed a new translation framework, PRISM based on a multi-agentic workflow. While super-sensitive data or highly creative content, such as marketing copywriting and video game content, may still require a more human-centered approach, PRISM provides a secure, flexible, and highly efficient alternative for a vast array of translation scenarios. This multi-stage framework is focused on maximum transparency, enforcing strict checks and balances, optimal efficiency, and maximizing the value of human expertise in the AI era.
The Foundation: PRISM Building Blocks
- Retrieval-Augmented Generation (RAG)
Our RAG approach combines AI with retrieved, vectorized in-context data. We add client-specific Translation Memories (TMs) directly to the LLM Embeddings database. Apart from that, we also send the relevant Glossary entries & project-specific short Style Guide with all available metadata with each translation batch to the LLM. This ensures all translations consider both the immediate context and subject matter area, providing significantly better consistency and tailored tone of voice compared to standard neural machine translation (NMT).
- AI-Backed Translation Improvement
An integral part of our workflow is context-aware AI improvement. Before a human expert even sees the text, specialized AI tools clean up the initial output (whether generated via RAG, MT, or legacy TMs). This step uses a different LLM model (vs. RAG). As far as each LLM engine version works differently, this can be described as a “second pair of robotic eyes looking at the materials”. It automatically aligns terminology, unifies the style, and fixes baseline consistency issues, allowing our human reviewers to focus entirely on nuance, cultural fit, and final polishing.
All AI-generated changes are saved as suggestions that are later reviewed/approved by a human.
- AI-Backed Language Quality Assurance (LQA)
Human eyes can sometimes miss errors. We use AI-backed LQA tools to evaluate translation adequacy, intelligibility, and style across the entire document. When combined with traditional, rule-based QA software, which pinpoints other error types, like formatting or spacing, this hybrid approach ensures a comprehensive, highly reliable quality evaluation of all generated content.
Humans manually go through all potential issues highlighted during the QA stage by AI-based or rule-based tools and resolve them during the review stage. (True Positives are fixed; False Positives are marked and ignored).
- AI-Backed Glossary Creation and Translation memory Cleaning
Well-prepared glossaries are essential for project consistency. Traditional term extraction requires significant time and budget. We utilize AI to rapidly mine terms from existing monolingual or bilingual files, automatically filtering out generic vocabulary and pulling industry-specific and context-aware definitions. Human experts then review these AI-sourced glossaries, making the entire preparation phase vastly faster and more cost-effective.
The Pre-Translation / Preparation stage optionally includes a quick, AI-based Translation Memory (TM) review. This review only concentrates on potential major errors that may be retained in earlier TM entries. These errors need to be revealed and fixed before the translation part starts.
Our context-sensitive tools make it possible to highlight erroneous translations as well as ignore or eliminate outdated translations (older than a certain age), translations contributed by a particular person, etc.
Irrespective of the translation approach used (human or AI-based), we do not want to include known major translation errors in the TM used by translators or to add them to the vectorized LLM Embeddings database used in the RAG process.
This TM verification step also helps to reveal issues early in cases when existing or provided TMs are of substandard quality.
The PRISM Framework Breakdown
The PRISM process does not eliminate human translation; it either fortifies it with automated QA steps or provides a highly reliable, AI-augmented alternative. Here is how it breaks down:
- P — Preparation / Pre-translation. We establish a solid foundation by reviewing Translation Memories (TMs) and then recycling previously approved texts from these TMs and populating/updating project glossaries.
- R — Retrieval-Augmented Generation (RAG). For segments with low or no TM matches, we utilize secure RAG technology (Vectorized TMs + Glossary + Style Guides + Secure AI) to generate context-accurate drafts. (Alternatively, Raw MT can be used if requested).
- I — Improvement (part of the RAG process). We apply a different LLM model (a “second pair of robotic eyes”) to clean up the text, align terminology, and fix inconsistencies before human intervention.
- S — Specialist Review. The critical human touch. Professional linguists are elevated to expert reviewers, ensuring final editing, nuance, emotional tone, and cultural appropriateness.
- M — Metric-Based QA. A customized combination of AI evaluation and traditional software quality checks is applied to catch hidden issues, followed by a final human overview of the error logs.
Why Choose the PRISM Process?
Total Transparency & Security. Guesswork is taken out of the equation: the client knows exactly what is being delivered. The PRISM process only uses secure, professional, closed AI environments, eliminating the risk of translators exposing sensitive materials to public AI engines.
Guaranteed Process Integrity. In standard workflows, crucial steps like editing are sometimes skipped. With PRISM, all automated preparatory steps are strictly enforced and run via our in-house pipelines. This means the text reaches the human reviewer perfectly formatted and terminology-aligned, making the critical Human Review stage targeted, tangible, and highly effective.
Proven Efficiency & Adaptability. Our RAG framework generates far better context-aware drafts than regular Raw MT models. The process is highly adaptable: custom quality metrics, glossaries, and tone-of-voice instructions are seamlessly integrated into the pipeline for each client or project line.
Low Risk & Scalability. It's easy to evaluate PRISM efficiency on a small trial batch (e.g., 3-5K words). If specific content doesn't align with the RAG approach, we can easily pivot to a traditional, human-centric workflow without wasting time.
The PRIMe Variation: High Volume, Tight Budget
For massive projects with incredibly short timeframes and limited budgets, clients historically requested "Lite MTPE" (Machine Translation + Light Post-Editing). However, superficial human review is often inconsistent while still taking up considerable time.
To address this, we offer the PRIMe variation of our PRISM process. By intentionally omitting the Specialist Review stage (S), we run content through the structured P-R-I-Me automated stages. This allows us to offer significantly lower per-word rates while still providing a structured, verifiable workflow that delivers higher baseline quality than raw NMT due to well-tailored prompts and settings and multiple automated checks.
(PRISM is a cornerstone of our localization technology, but our innovations extend further. Discover how we integrate AI across all Logrus IT services, from multimedia to content creation).
