Acolad
BotiqueAI
AcoladRAG · LLMNLP · Translation · MLOps
Case Study

RAG Translation

40% quality improvement on post-translation editing, fully automated with agentic RAG.

Acolad is the #1 European leader and among the top 10 worldwide in language and content services. BotiqueAI deployed two AI solutions: an agentic RAG system for automatic translation post-editing, and an internal knowledge chatbot powered by company documents.

40%
translation quality gain
Automatic correction without human intervention
2
AI projects deployed
RAG translation + internal chatbot
POC→Prod
MLOps pipeline
Concept to production with best practices
LLM Judge
automated validation
LLM-as-a-Judge for precise result evaluation
Client
Acolad
#1 European language services
Industry
Language & Content
Top 10 worldwide
Project 1
Agentic RAG
Post-translation editing
Project 2
Internal Chatbot
Knowledge management
Stack
RAG · LLM · MLOps
Python · LangGraph · GPT-4
Outcome
40%
Quality improvement rate
The Challenge

AI precision for
language at scale.

Acolad delivers enterprise translation services trusted worldwide. AI-powered precision, human-verified accuracy. As the #1 European language services leader, scaling quality without scaling headcount required automation.

The core challenges were post-editing efficiency, knowledge accessibility, and bringing AI from concept to reliable production deployment.

Manual post-editing bottleneck
Human translators spent significant time manually correcting poorly translated sentences, a repetitive and time-consuming process that slowed delivery.
Fragmented internal knowledge
Employees had no fast way to access the company's internal documentation. Finding answers required navigating multiple disconnected systems.
No automated quality validation
Evaluating translation quality required manual review. There was no scalable system to automatically detect and score translation errors.
The Solution

Two AI systems.
One production-grade deployment.

BotiqueAI designed and deployed two core AI products: an agentic RAG for translation post-editing and an internal knowledge chatbot, with automated evaluation built in from the start. All delivered from POC to production using MLOps best practices.

①
Agentic RAG: Automatic Post-Translation Editing

An agentic RAG system that automatically detects and corrects poorly translated sentences. The agent retrieves relevant reference segments, generates corrected alternatives and applies them without human intervention, achieving a 40% improvement rate.

Agentic RAGLangGraphGPT-4PythonTranslation Memory
②
Internal Knowledge Chatbot

An intelligent chatbot that leverages the company's internal documents to provide fast, accurate answers to employees. Built on RAG with semantic search over internal knowledge bases, enabling efficient knowledge management across the organization.

RAGSemantic SearchGPT-4Document IngestionKnowledge Base
Impact

Measurable results
in production.

40%
Quality improvement
Automatic correction of poor translations
0
Human interventions
Post-editing corrections fully automated
Live
In production
POC to deployed product
LLM
Judge validation
Automated quality scoring
Tech Stack

RAG-first
AI stack

Built around LangGraph for agentic orchestration, GPT-4 for generation and evaluation, and semantic vector search for retrieval. Deployed end-to-end with MLOps best practices from POC to production.

RAG & Agents
🔗
LangGraph
Agentic RAG orchestration and workflow control
🔍
Vector Search
Semantic retrieval over translation memories and internal docs
LLM & NLP
🤖
GPT-4
Translation post-editing, chatbot responses and LLM-as-a-Judge
📝
Prompt Engineering
POC design and iterative prompt optimisation
MLOps
🚀
POC to Production
End-to-end deployment following MLOps best practices
⚖️
LLM-as-a-Judge
Automated evaluation and quality scoring at scale
Collaboration
🗺️
AI Roadmap
Co-designed with Head of AI and AI Product Manager
🐍
Python
Core implementation language across all projects
BotiqueAI
Custom AI and MLOps for enterprise clients
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