[Webinar 11/19] Translation Transformation: A revenue game-changer for Global Business
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Build better models
Accelerate your model development with LILT's AI DataStudio
LILT’s AI DataStudio offers companies a scalable solution for creating high-quality data sets for multilingual natural language processing (NLP) model training.
Accelerate model development today using the same production infrastructure that Lilt uses for its own data creation and enterprise localization use cases. LILT's AI DataStudio is optimized for API-first workflows and large-scale data creation tasks in 60+ languages, including low-resource languages.
LILT offers the only data labeling platform that is specifically built for large-scale enterprise translation workflows.
Professional translation quality with per-word prices that average 50% less than data annotation providers and legacy language service providers, with further discounts possible based on volume and QA requirements.
The increasing prevalence of generative AI means that it is increasingly difficult to detect AI-generated text in data supply chains that can undermine the effectiveness of In-Context Learning. LILT AI DataStudio is the only data labeling solution with real-time annotator monitoring and fraud detection to ensure diversity from third-party machine translation.
Additional Features
Enterprise-grade quality
Using our proven enterprise-grade localization workflows, we guarantee high-quality data, labeled by skilled linguists.
An API-first workflow
Lilt offers a robust API infrastructure for streamlined data transfer and exchange at all workflow phases.
Custom annotation guidelines
We can with work with any specific style, terminology, and other textual requirements.
Straightforward SLAs
Clearly defined SLAs for volume and turnaround time.
TRUSTED BY THE MOST INNOVATIVE
ENTERPRISES
,
GOVERNMENT TEAMS
AND
STARTUPS
"We wanted a more automated, more streamlined solution where the [contextual AI] engine learns and improves right as we use it. This is what the LILT solution offered us."
