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Build your AI Copilot
Build your no-code AI copilot in minutes!
An AI Copilot is a powerful conversational tool that harnesses the amazing abilities of contemporary Large Language Models like summarizing, decision-making and step-by-step analysis combined with Custom Knowledge Bases to provide context-aware information to enterprises and organizations.
AI copilots are crucial for brands, providing faster resolutions, boosting sales, handling 70% of self-serve queries, and scaling support operations intelligently, all while saving costs. AI chatbots can:
- provide faster resolutions and boost sales,
- provide instant and real-time personalized responses to customers, making them feel important and satisfied with the brand's customer experience efforts.
Gooey.AI's Copilot uses the latest orchestration capabilities for Large Language Models. The general ability of LLMs is to summarize, analyze, and make decisions but it is NOT trained on your enterprise data. To overcome this, we are moving with an innovative and simple solution called Retrieval Augmented Generation.
Retrieval Augmented Generation (RAG) allows users to search indexed Domain Specific Information to ensure that LLMs provide the most accurate and up-to-date information about your enterprise needs.
Your data is prepared and indexed and when the user queries the AI Copilot searches and filters your data to the most relevant snippets. With the snippets and the system prompt, the LLM analyzes and summarizes an appropriate response to the user query. This flow of information ensures that the AI Copilot:
- 1.Remains accurate and up-to-date
- 2.It avoids hallucinating for a response
- 3.Do not respond to queries beyond the scope of your knowledge (unless you want it to).
- 4.Mitigates risk to the organization
- 5.Builds users' trust by providing relevant information
Gooey.AI Copilot integrates:
- Selection of multiple Large Language Models (OpenAI GPT3.5 and GPT4, Meta's LLaMa2, Google PaLM2)
- Retrieval Augmented Generation (with a Google doc, PDF, or Excel.)
- Synthetic Data Extraction (e.g. to turn video transcripts into FAQs)
- Configurable conversation analysis
- Built-in Feedback buttons for every response
- Advanced RAG settings including:
- Dense Embeddings
- Keyword Search Extraction - useful for highly technical knowledge bases with lots of jargon
- Conversation summarization - to create better vector DB queries
- Bulk and Golden QnA testing tools - to evaluate how changes to your workflow affect bot performance
- Integration with Facebook, Slack, Whatsapp, Instagram, Websites (via landbot) and as an API.
- Automatic Speech Recognition - for incoming audio messages
- Language translations (including Google Translate, Bhashini, Seamless)
- Text-to-Speech from Google, Uberduck, and EvelenLabs (so the bot can send back audio responses)
- LipSync with Text-to-Speech
If you would still like a step-by-step guide, please read through the sub-sections of this guide.
This is the basic setup:
- 1.Relevant documents from your organization
- 2.A prompt script for the AI bot to follow
- 3.Hit Submit!
Last modified 26d ago