🔆Instinct AI

Instinct AI empowers users to build generative AI chatbots for businesses by leveraging training data from documents and notes. This document provides a comprehensive overview of Instinct AI's functionalities, including training, customization, data management, and agent creation.

Key Features

1. Train AI: Train custom AI models on uploaded business documents and notes.

   • Document Loader: Upload various document formats (.pdf, .doc, .docx, .txt, .csv, .md) for training.

   • Crawl website: Extract data from websites to feed your AI model.

   • Notes: Create custom FAQs and articles to guide training.

   • Google Drive: Seamlessly import data from Google Drive.

   • Amazon S3: Integrate with Amazon S3 for secure data access and many more.

   • Data Bank: Manage and access your ingested data for training purposes.

2. Customize Instinct AI:

   • AI Agents: Train agents to perform specific tasks.

   • Notes: Add business notes to enrich training data.

   • Feedback: Manage user feedback to improve AI performance.

3. Insights: Gain quick insights into the performance of your trained generative AI models.

How it Works

1. Data Acquisition:

• Users can upload various document formats through the Document Loader.

• Alternatively, Instinct AI can crawl data from provided website URLs.

• For specific knowledge bases, users can create custom FAQs and articles within Notes.

• Integration with Google Drive and Amazon S3 allows for efficient data import.

2. Data Management:

• The Data Bank provides a central location to manage all ingested data for training purposes.

• Users can view, categorize, and organize data as needed.

3. Training:

• Using the Train AI feature, users select the desired data from the Data Bank to train their AI model.

• Instinct AI utilizes this data to build a generative AI model capable of chatbot interactions.

4. Customization:

• Users can define specific tasks or functionalities for their AI agents within the AI Agents section.

• Business notes can be added to provide context and enhance training effectiveness.

• User-provided feedback helps refine the AI model and improve chatbot performance over time.

5. Insights:

• The Insights section provides quick visualizations and summaries of the trained generative AI model's effectiveness.

• This allows users to gauge the performance of their AI and make informed decisions.