Local AI Corpus

A local AI corpus is a private collection of data (e.g. text, audio, and video) that is used to train and evaluate AI models. It is often used when an entity has specific requirements or preferences that necessitate the use of a private collection of data rather than a broad collection of data as is the case with a public corpus. Additionally, a local corpus has the advantage of being secure and confidential, unlike a public corpus which is widely available and accessible to many users.

There are three main advantages to using a local corpus instead of a public corpus when it comes to leveraging the power of AI to improve products, services, and processes in business, namely:

Data Quality

The dataset contained in a local corpus is decided by the entity that curates it. This ensures quality, relevance, accuracy, and control over the information an AI model takes in as well as outputs. This allows businesses to avoid the noise and errors that may arise when using a public corpus which can include data from unreliable sources. The use of a local corpus also enables AI models to capture characteristics unique to businesses (e.g. terminology, jargon, and style).

Data Privacy

A local corpus can be stored and processed on-premise or on a private cloud. This ensures that all proprietary data remain in-house, keeping them secure from unauthorized access. Moreover, because a local corpus is fully customizable, entities can ensure full adherence to the data protection regulations and ethical standards relevant to their business sector, country, or region. This reduces the risks and liabilities that may otherwise arise from using a public corpus which may contain identifiable confidential data that can be exploited by hackers and/or competitors.

Data Control

A local corpus allows an entity to tailor their AI model to suit its specific goals and objectives (e.g. revenue growth). This enables the generation of tailored data sets that explore new opportunities and challenges unique to an entity’s domain, offering a competitive edge over other entities which rely on a generic and standardized public corpus. A local corpus can also be updated and expanded to reflect an entity’s growth or change. This grants entities with more scalability and customization as opposed to a public corpus which is often outdated or limited.

In conclusion, the use of a local AI corpus grants an entity complete control over the quality, relevance, security, and privacy of the dataset which trains their AI model. It is a valuable asset for entities that want to leverage the power of AI effectively and responsibly without taking on the level of risk and liability associated with using an open and general-purpose public corpus.

Possess your own fully customizable private corpus with a local data model that delivers on quality, accuracy, and scalability with Kenja. We secure your local corpus behind enterprise-grade security that’s NIST 1.1 Cybersecurity Framework compliant, ISO 27001 certified, and ANSI National Accreditation Board (ANAB) ISO/IEC 17021 accredited. Contact us here today to learn more about how you can own private and secure corpus with Kenja.