Agentic Translation

The future of translation:
‍AI Agents

Enterprise customers demand high-accuracy translation at scale. AI translation often falls short because it lacks:

Contextual understanding

Accurate translation requires domain-specific knowledge for correct terminology.

Reasoning ability

AI needs to discern tone and intent to avoid mistranslations.

Hallucination control

Current models can generate plausible but inaccurate outputs, compromising fidelity to the original text.

“I think AI agentic machine translation has huge potential for improving over traditional neural machine translation."

- Andrew NG, Founder of Deeplearning.ai

Agentic Translation Infrastructure

The agentic model utilizes multiple AI agents, each specializing in human-like analysis of translation aspects such as context and reasoning, to improve accuracy and maintain the original meaning. It combines the strengths of transformer-based models, SSMs, and RAG.

Transformers

Contextual Understanding

Transformers excel at capturing long-range dependencies in text, allowing them to understand the context of a sentence or phrase accurately. This leads to more nuanced and accurate translations.

‍Parallel Processing

Their architecture allows for efficient parallel processing, making them faster for training and inference, which is crucial for handling large volumes of translation data

‍Transfer Learning

Pre-trained transformer models can be fine-tuned on specific translation tasks with relatively small amounts of data, speeding up development and enabling adaptation to specialized domains.

SSM

Memory Efficiency

SSMs are more memory-efficient than transformers, allowing them to handle longer sequences of text without running into memory constraints. This is particularly beneficial for translating long documents or maintaining context across multiple sentences.

Linear Scaling

The computational complexity of SSMs scales linearly with the input length, making them faster and more efficient for processing long sequences compared to transformers, which have quadratic scaling.

Interpretability

SSMs offer better interpretability compared to transformers, potentially providing insights into the model's decision-making process and allowing for easier debugging and improvement.

RAG

Factual Accuracy

RAG models can access and leverage external knowledge sources, like databases or the internet, to improve the accuracy and relevance of translations. This is particularly useful for translating technical or specialized content that requires domain-specific knowledge.

Reduced Hallucinations

By relying on retrieved information, RAG models are less likely to generate factually incorrect or nonsensical translations, a common issue with purely generative models.

Adaptability

RAG models can be easily adapted to new domains or languages by providing them with relevant knowledge sources, making them more versatile and flexible than traditional translation models.

DISCOVER

Discover Agentic Translation
to revolutionize your global communication!

API Docs

AI translation so good, you'll think it's human

The future of translation:‍AI Agents

Agentic Translation Infrastructure

Transformers

SSM

RAG

Discover Agentic Translationto revolutionize your global communication!

The future of translation:
‍AI Agents

Discover Agentic Translation
to revolutionize your global communication!