Best On-Premise AI Content Detection Tools of 2024

AI assistant for customer-facing departments. Sapling works with messaging platforms and CRMs to assist agents in creating personalized responses. Managers can gain conversational insights that will help them coach and prepare their teams. Sapling offers functionality for autocomplete, spelling, grammar checking, as well as snippets/canned message/text expander. All services can be hosted on-premise or in the cloud to meet security and privacy requirements. The Grammar and Spelling APIs allow Sapling's language models to be integrated into custom applications and use-cases. Sapling Suggest pulls relevant answers from a team knowledge database and allows agents to respond faster to customer inquiries by clicking on the desired response. Instead of giving brittle, decision tree-based chatbots, empower agents.

Enago Reports is a collection of reports that utilises the capabilities of advanced artificial intelligent. These reports are crucial in assessing, enhancing, and improving the quality, integrity, impact, and accuracy of various categories of documents. They do so in a quick, accurate, and detailed way. Enago's extensive expertise, accumulated over 17 years of service, is used to enhance any document in order to achieve its maximum impact. It is tightly integrated to Trinka.ai - an AI-powered writing assistant. Together, they create a tech eco-system that allows users to work seamlessly with Enago Reports (a writing assistant powered by AI) and Trinka.ai in order to maximize productivity when writing and evaluating documents. Enago, a member of the Crimson Group, has been providing author services since 2005. This includes English editing, translation, transcription and more. Enago has helped 2 million+ authors from 125+ countries improve their research communication.

GLTR's goal is to use the same models that were used to generate fake text as a tool to detect it. GLTR has access the GPT-2 117M language modeling from OpenAI. This is one of the most widely available models. It can analyze any textual input to determine what GPT-2 would predict at each position. We can calculate how the following words rank because the output is a ranking all the words the model knows. This positional information is used to overlay a colored mask on the text that corresponds with the rank. The most likely words are highlighted in green (top 10) or yellow (top 100), while the rest are highlighted in purple. This gives us a visual indication of the likelihood that each word is under the model.