Since the launch of ChatGPT, hundreds of companies have added AI features to their tools. The chat powered by an LLM is the most common feature. It bears great promise for being able to interact in natural language with any software, to search in a more precise way, to streamline workflows, and more. However, not all AI features are the same, and the names of chats powered by AI can serve as clues to understand the kind of service they deliver. The objective of this blog is to help you distinguish at a glance what to expect from them.
To kick things off, let's first differentiate between AI chat, AI assistant, AI copilot, and AI sidekick:
- AI Chat, or Chatbots: Primarily designed for interaction and communication. These entities answer queries, provide support, and maintain casual conversations, often through textual and sometimes voice-based interactions.
- AI Assistant: Task management and personal organization are their purposes. Think of them as tools that help with setting reminders, automating mundane tasks, and voice-activated actions like Siri or Alexa.
- AI Copilot: These are collaborative and decision-support tools. They assist in technical tasks, suggest best practices, and often work alongside the user to achieve specialized tasks.
- AI Sidekick: Imagine a blend between an assistant and a copilot. They're not just about tasks or collaboration, but more about companionship in digital processes. They anticipate needs, provide proactive suggestions, and create a more personalized AI experience.
At Blobr, we see these different chats as different elements that can be used in the ways described below.
Level One - AI Chatbots
Primary Use Case: Support Questions
Technology Base: Vectorized databases from existing documentation.
The first level of AI chat is primarily focused on providing answers based on existing documentation. If you've ever interacted with a customer service chatbot that pulls up relevant information from a user manual or a FAQ section, you've experienced this first level. By converting PDFs and other documentation into a vectorized database, these chats can quickly scan and present relevant data.
- Efficiently handles high volumes of common queries.
- Reduces the need for human intervention for basic questions.
- Instant access to vast amounts of documentation.
AI Chat Example
Fin is Intercom’s AI feature. It pushes the chatbot idea further, thanks to an OpenAI integration. Where the chatbot would have only used pre-defined response schemes, Fin crunches the documentation to deliver answers by itself.
Level Two - AI Assistant
Primary Use Case: Analytics Questions
Technology Base: SQL databases containing numerical values + Vectorized database
Ever wondered how certain chats can provide you with real-time analytics, like "How many products did we sell last month?" or "What was the traffic spike on our website?" That's level two for you! These chats are connected to SQL databases and are designed to analyze and report numerical data. With a simple query, you can have insights without diving into complex analytic tools.
- Immediate data-driven insights.
- Reduction in analytics turnaround time.
- Facilitates data-driven decision-making.
AI Assistant Example
Spark uses account data to generate reports based on natural language requests. Here, the combination of AI and large amounts of data collected by Mixpanel brings to life an AI assistant able to seriously reduce the time it takes to generate simple reports, enabling every profile in a company to get answers fast.
Level Three - AI Copilot
Primary Use Case: Executing Actions
Technology Base: Real-time communication with APIs + SQL databases + Vectorized database
The third level is where AI chats truly shine, showcasing their power to not just inform, but also to act. Whether you want to schedule a meeting, adjust your smart home's thermostat, or initiate a complex business process, these AI copilots communicate directly with APIs in real time to execute actions on your behalf. Think of them as your digital sidekicks, ready to assist in a myriad of tasks.
- Seamless integration with various tools and platforms.
- Automates and simplifies complex tasks.
- Enhances productivity by reducing manual input.
AI Copilot Example
ChatSpot acts as a marketing assistant connected to your CRM’s data. It's a multi-task tool in the sense that you can accomplish tasks on your CRM in a conversational way, obtain summaries of your interactions with a prospect, or get insights into your sales performance.
Level Four - AI Sidekick
Primary Use Case: Advisory and Action Execution
Technology Base: Real-time communication with internal and external APIs, SQL databases, and vectorized databases.
Venturing into the most advanced tier of AI chat, we encounter the AI Sidekick. It's where the line between reactive response and proactive intuition blurs. This level is not just about answering queries or executing tasks, but about understanding context, predicting user needs, and providing advisory insights.
- Deep integration across various data platforms and sources.
- Proactive suggestions and strategic advisory.
- Comprehensive action capabilities, from simple tasks to complex decision-making processes.
AI Sidekick Example
A prime example is Shopify's AI sidekick, which does more than just integrate with Shopify APIs. It advises users on optimizing business strategies, scaling effectively, and identifying growth opportunities. The combination of various technology bases makes this level exceptionally versatile, enabling it to tap into vast data sources and execute multifaceted actions seamlessly.
Each new level brings its share of complexity in what the AI companion can achieve. From the advanced chatbot to the sidekick able to perform actionable decisions, each brings nuance to how you can leverage AI in your company. What you want to achieve and the resources you have at hand will determine your need for one of them.
Blobr can help you build your future AI companion based on your APIs and your needs.
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