PRISM is an AI-augmented translation framework developed by Logrus IT and based on a multi-agentic workflow. It combines Retrieval-Augmented Generation (RAG) with secure AI environments to ensure consistency, context awareness, process transparency, and data protection. Clients can choose between various tiers, from full PRISM with end-to-end human oversight to lite PRIMe version with human review reserved for final QA.
The PRISM framework is built on six core components that work together to deliver consistent and secure translations. From AI-powered retrieval to human-reviewed quality assurance, each stage plays a specific role in the workflow.
P — Preparation Stage
Well-prepared glossaries are essential for project consistency. While traditional term extraction requires considerable time and budget, AI can rapidly mine terms from existing monolingual or bilingual files. On top of that, Logrus IT proprietary AI-augmented glossary tool automatically filters out generic vocabulary and pulls industry-specific, context-aware definitions. It can also enrich the glossary with full source sentences to illustrate term usage and facilitate understanding. Human experts then review these AI-sourced glossaries. This makes the entire preparation phase significantly faster and more cost-effective.
P — Preparation Stage
An optional AI-based review focuses solely on potential major errors in earlier TM entries, so that they are revealed and fixed before translation begins. Context-sensitive AiLQA tools developed by Logrus IT for TM review can highlight erroneous translations, as well as eliminate older entries or translations contributed by specific individuals. Whether translation is performed by humans or through AI, Logrus IT prevents historical errors from entering the working TM or the vectorized LLM Embeddings database. In case a client does not have a TM but can provide older texts in source and target languages, Logrus IT proprietary AI-augmented aligner tool can use those to create a working asset that improves consistency. It handles imprecise matches and reordered paragraphs. The produced output is used for TM creation or the RAG vector database.
R — Retrieval-Augmented Generation Stage
The RAG approach to translation combines AI with retrieved, vectorized in-context data. Logrus IT RAG-based tools create an LLM Embeddings database from client-specific Translation Memories (TMs) Translation Memories (TMs). For each translation batch, the LLM also receives relevant glossary entries, project-specific style guides, and all available metadata. This ensures that translations are made with awareness both of the immediate context and the subject matter area. As a result, they provide significantly better consistency and a tailored tone of voice compared to standard neural machine translation (NMT) or publicly available free GenAI tools. Explore the detailed PRISM vs NMT breakdown here.
I — Improvement Stage
An integral part of Logrus IT workflow is context-aware AI improvement. Before a human expert even sees the text, a specialized AI agent checks the initial output (whether generated via RAG, MT, or legacy TMs) and reports errors based on a pre-set severity threshold. This step uses a different LLM model than the primary RAG engine. Since each LLM version works differently, this setup acts as an efficient independent review stage. It ensures accuracy, automatically unifies the style, and fixes baseline consistency issues. This allows human reviewers to focus entirely on nuance, cultural fit, and final polishing.
S — Specialist Review Stage
As an option to ensure peak human concentration during review and prevent reviewers from approving RAG-generated text passively, the RAG process can generate a separate TM rather than auto-translate the target content directly in the bilingual file. The system then marks all AI-generated segments as 95% TM matches, and the human reviewer has to evaluate and apply them manually and consciously. Given the high quality of these RAG TM segments, reviewers have no incentive to look for alternatives from public MT tools and remain fully engaged in the review process.
M — Metric-Based QA Stage
Human linguists can sometimes miss errors. The PRISM framework uses AI-backed LQA tools to evaluate translation adequacy, intelligibility, and style across the entire document. These are combined with traditional, rule-based QA software, which pinpoints formal errors like formatting or spacing. Together, this hybrid approach ensures a comprehensive, highly reliable quality evaluation of all generated content. Human experts manually review all potential issues highlighted by both AI-based and rule-based tools. True positives are fixed, while false positives are marked and ignored.
PRIMe is a streamlined version of the PRISM framework. It uses the same core technologies as PRISM, including RAG, multiple LLM models, and proprietary QA tools, but omits the Specialist Review stage. This makes it a cost-effective solution for high-volume projects where speed and consistency take priority over creative nuance. Learn more about the PRIMe framework here.
From RAG to QA, these building blocks ensure consistency, quality, and security at every stage of the PRISM and PRIMe workflows. Explore the framework in detail to see the problems it solves, its key benefits, and underlying principles.
