How does ModelFront ensure that the AI-checked and fixed translations truly maintain 'human quality'?
ModelFront ensures human quality through a multi-faceted approach. It provides transparent monitoring, tracking human and AI agreement across production. Customers confirm quality with their own human evaluation before going to production, with ModelFront offering guidance for efficient evaluation. Furthermore, all AI models are customized for each client based on their human translation data, glossaries, style guides, and content types, rather than using generic models.
What specific types of data are used to customize ModelFront's AI models for a client?
ModelFront customizes its AI models using a client's specific human translation data, glossaries, style guides, and content types. This ensures that the AI adapts to the client's unique style, terminology, and input requirements, enabling it to pass the client's quality bar.
Can ModelFront be integrated with any Translation Management System (TMS), or are there specific systems it supports?
ModelFront is designed to work right inside traditional TMS setups and is also accessible via API, indicating broad compatibility. The platform emphasizes 'no engineering' and 'no workflow change' for integration, suggesting a flexible approach to connecting with various TMS environments.
How does the 'savings-based pricing' model work in practice, and what factors influence the per-word cost?
The savings-based pricing model means customers only pay for 'saved words' – words in segments that were successfully verified by ModelFront's AI. They do not pay for segments that were not verified. The exact price per saved word is dynamic and depends on several factors, including the customer's translation volume, content types, languages involved, specific integration requirements, and deployment specifics.
Beyond just identifying errors, what does ModelFront's 'automatic post-editing' capability entail?
ModelFront's automatic post-editing goes beyond simple error identification by actively fixing issues in machine translations. This capability is integrated with its quality prediction, allowing the system to not only flag potential problems but also to apply corrections to ensure the output meets the required human quality standards, effectively reducing the need for manual intervention.