Large Language Model
A deep learning model trained on vast text corpora that can perform tasks like text generation, translation, and summarization, often requiring governance around bias and misuse.
Transformer-based architectures (e.g. GPT, BERT) with hundreds of millions to trillions of parameters, pre-trained on diverse Internet-scale data. They excel at few-shot learning but can propagate societal biases or generate harmful content. Governance must include bias audits, usage monitoring, content filtering, and clear policies for allowable vs. prohibited prompts to mitigate misuse.
A marketing team uses GPT-style models to draft ad copy. Before deployment, they run bias checks on generated text (e.g., gendered language), enforce profanity filters, and implement usage quotas to prevent confidential data leaks - ensuring the model enhances creativity without introducing reputational or legal risk.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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What problem does Enzai solve?
Enzai provides enterprise-grade infrastructure to manage AI risk and compliance. It creates a centralized system of record where AI systems, models, datasets, and governance decisions are documented, assessed, and auditable.
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