How to Build Your Own AI Copilot: The Future of Conversational Interfaces

In a world where technology is rapidly evolving, one thing is becoming increasingly clear: AI-powered copilots are the future of human-computer interaction. These intelligent assistants are revolutionizing how we approach personalized learning, customer service, and productivity. Today, I want to share the process of building Mydra’s AI Career Copilot and explore the technical and strategic challenges we encountered. Whether you're a developer, an entrepreneur, innovation director, or just curious about AI, this guide will walk you through the key elements of building your own AI copilot.

What is an AI Copilot?

An AI copilot is more than just a chatbot—it’s a highly intelligent assistant that helps users navigate complex tasks with ease. What makes it stand out is its ability to provide personalized recommendations, interact with external systems, and even automate specific actions. The copilot doesn’t just answer questions; it works alongside you, adapting to your needs and continuously learning from your interactions.

The beauty of an AI copilot is in its simplicity: you just chat with it. There’s no need to navigate complex dashboards or worry about hidden features. You simply tell the copilot what you need, and it takes care of the rest. But behind this simplicity is a powerful system designed to assist with specific use cases, delivering meaningful outcomes by interacting with APIs, databases, and external tools.

At Mydra, our Career Copilot is specifically designed to help users define their career goals, analyze professional profiles to identify skill gaps, and generate personalized learning paths. It doesn't just provide suggestions—it interacts with multiple systems to retrieve relevant data, perform complex analysis, and deliver recommendations that are customized for each user’s journey. Whether it's suggesting the right courses to upskill or helping you plan your next career move, the copilot integrates seamlessly with the systems you need to get actionable, personalized results.

Conversational Interfaces: The Future of Interaction

Conversational interfaces—powered by AI—are becoming the go-to method for interacting with platforms. Users want natural, seamless experiences, and conversational AI can provide that. Whether it’s asking an AI copilot for course recommendations or having it analyze your skills from your LinkedIn profile, conversation is quickly becoming the default interface.

That said, conversational interfaces are not a one-size-fits-all solution. There are still use cases where traditional dashboards, buttons, and menus are more effective, especially when dealing with complex user interactions that require detailed input. A well-designed copilot integrates conversational AI where it adds value but also provides traditional UI elements when needed.

Hyperpersonalization at Scale

Traditionally, delivering a hyperpersonalized experience meant one-on-one interactions between a user and a human expert. While effective, this approach is time-consuming, resource-intensive, and ultimately unscalable. With the advent of AI copilots, we can now achieve the same level of personalization at scale. By leveraging AI’s ability to process vast amounts of data and interact naturally with users, hyperpersonalization becomes not only possible but highly efficient.

In the context of an AI career copilot, for example, the system can analyze a user’s career objectives, work history, and even their professional profile (via LinkedIn or resume data). From there, the copilot generates tailored learning paths, recommending courses or resources specifically designed to fill individual skill gaps. Instead of a generalized, one-size-fits-all solution, the copilot adapts its guidance based on the unique needs of each user—whether they’re looking to transition into a new career or upskill for a promotion.

The real power of conversational copilots lies in their ability to continuously learn from and adjust to each user’s evolving journey, delivering deeply personalized recommendations in real-time, without the need for manual intervention. This is what makes hyperpersonalization at scale possible—turning what used to require human interaction into a seamless, automated process driven by AI.

Tech Stack: The Building Blocks

Building an AI copilot involves carefully selecting the right tools and technologies, depending on the complexity of your use case and the level of customization required. At Mydra, we built our copilot using a combination of powerful language models, specialized frameworks, and custom-built components to create a truly personalized and scalable solution.

1. Large Language Models (LLMs)

At the core of our copilot are models from OpenAI and Google's Gemini, which power the natural language understanding (NLU) and reasoning capabilities. These models enable the copilot to interact with users intelligently, process complex queries, and deliver personalized responses. LLMs are essential for interpreting user inputs, extracting insights from vast data, and making the copilot "smart."

• How we use them: The copilot leverages these models for all conversational interactions, interpreting the user’s career goals and turning that into actionable insights, such as skill gap analysis or personalized learning paths. These models allow the copilot to learn from each interaction, ensuring continuous improvement in responses and recommendations.

2. Frameworks: Langchain, CrewAI, and More

For building an AI copilot, you can take two main approaches:

1. Build from Scratch: You can create a fully custom system where your AI integrates with external tools and APIs directly. This gives you complete control over how the system functions, from natural language processing to memory management and API interactions. However, this can be complex and time-consuming.
2. Use Frameworks: Alternatively, there are frameworks like Langchain and CrewAI that simplify much of the integration process. These frameworks provide pre-built modules that abstract some of the complexity involved in managing multiple systems, APIs, and data flows, while interacting with the LLM. They allow you to focus more on building a high-quality experience without worrying about the lower-level intricacies of handling conversational agents.

At Mydra, we used a combination of Langchain to handle much of the heavy lifting with API interactions and tool integration, and we customized certain elements, such as memory management, to ensure the copilot delivers a seamless user experience. Langchain allowed us to efficiently orchestrate communication between the copilot and various external services, while still giving us the flexibility to build more specialized components where needed.

Building Custom Elements: While frameworks like Langchain handle many aspects efficiently, we built custom systems to manage memory and context retention. This ensures that as users interact with the copilot, it can recall previous interactions or summarize conversations in a way that remains meaningful over time.

3. Function Calling: Augmenting AI with Action

One of the standout features of advanced LLMs is its ability to perform function calling. This allows the Copilot to go beyond just answering questions—it can take action, run analysis, or retrieve data by calling specific APIs or tools based on the user’s input.

For Example: Our copilot can analyze a LinkedIn profile to detect skills gaps, generate a personalized learning path by interacting with a learning engine, or send personalized course recommendations directly to a user’s email.

This capability gives the copilot the power to:

• Decide which tools or APIs to use depending on the task at hand.
• Trigger actions like retrieving data from a database, running complex algorithms, or interacting with external services to provide meaningful results.
• How it Works: Function calling relies on the copilot’s ability to “reason” and decide which tool is most appropriate for the task at hand. For example, when a user asks for a skills gap analysis, the copilot chooses the tool responsible for scanning the user’s LinkedIn profile, retrieves the necessary data, and runs an analysis using an API designed for skill-matching. Once the analysis is complete, the copilot generates a personalized learning path and recommends specific courses to address those gaps.

Recommendation: For those interested in learning how to build AI systems that can handle both conversational AI and complex tool interactions, check out the Langchain for LLM Application Development, AI Agents in Langraph, Multi AI Agent Systems with CrewAI, Functions, Tools and Agents with Langchain course from DeepLearning.AI on Mydra, which cover everything from working with LLMs to implementing function calling and system integration.

Challenges in Building an AI Copilot

1. Memory Management

One of the biggest challenges when working with Large Language Models (LLMs) is memory. By default, AI models do not have an inherent memory of past interactions. You can fine-tune models to provide more specialized knowledge, but this only helps in making the AI smarter for specific tasks. It does not enable the AI to remember individual interactions or conversations.

To maintain context over a user’s multiple interactions, we rely on what’s known as the context window—a space where we can “stuff” relevant data for the AI to process in real-time. However, context windows come with their own limitations:

• Limited Size: There’s a cap on how much information you can include, and filling in too much data makes the model slower and more expensive to run.
• Loss of Focus: Overstuffing the context window can also cause the model to lose focus, decreasing the quality of responses.

At Mydra, we developed a system that intelligently manages memory through summarization and structured data extraction. Here's how:

• Summarization: As the conversation with the user grows, the system begins to summarize older messages automatically, extracting key points while trimming unnecessary details.
• User State: We create a structured user state that stores essential user information—like career goals, preferences, and previous actions. This “user state” becomes a compact reference that we pass to the AI as context, as well as to specialized APIs that generate outputs like personalized course recommendations.

This way, the AI retains the most crucial data without overwhelming the context window, ensuring it delivers relevant, informed responses while maintaining efficiency.

2. Accuracy in Function Calling

Another significant challenge is ensuring that the AI reliably calls the correct service or API in response to a user’s input. While LLMs are highly capable, they are not perfect and can sometimes misinterpret intent, leading to incorrect actions. This can be problematic, especially when the copilot is tasked with performing sensitive or complex actions such as analyzing a profile or recommending learning paths.

To address this challenge, we implemented a few key solutions:

• Safeguards: Before triggering an API or service, the AI validates the user’s request, ensuring that it has gathered the necessary data and understands the task clearly.
• Fallback Mechanisms: For critical tasks, we built fallback processes where the AI double-checks the request. This ensures that, even if the AI’s initial understanding is off, the system can catch and correct the error before proceeding.

For example, when a user asks for their LinkedIn profile to be analyzed, the AI first confirms that all the necessary data points (such as skills and job history) are available before running the analysis engine. This step significantly reduces errors and ensures high accuracy in function calling.

• Course Recommendation: To learn how to build systems that manage function calling accurately, check out our Functions, Tools and Agents with Langchain course on Mydra.

3. Multi-Language Handling

Supporting multiple languages is a powerful feature, but it comes with its own set of complexities. The AI must accurately recognize which language a user is communicating in, ensure consistent processing, and correctly pass that language to the APIs and tools it interacts with.

At Mydra, we developed systems to handle this by:

• Automatic Language Detection: The copilot automatically detects the language the user is using and adapts its responses accordingly.
• Language-Specific API Handling: Different APIs may have varying language capabilities, so we pass the appropriate language to the tools that generate specific outputs. This ensures consistent and accurate results, regardless of the language the user speaks.

4. Multi-Agent Systems: Directing Queries to Specialized Agents

Another advanced challenge is building a system that can manage multiple independent AI agents. In a multi-agent system, instead of relying on a single AI model to handle all requests, the copilot directs specific queries to specialized agents trained for particular tasks. This makes the system more efficient and scalable, as each agent can specialize in its domain and deliver better results.

At Mydra, we use multi-agent systems to enhance the copilot’s capabilities:

• Specialized Agents: Each agent is designed to handle specific tasks—whether it’s analyzing LinkedIn profiles, generating learning paths, or providing skill recommendations. The copilot acts as a conductor, directing user queries to the appropriate agent based on the request.
• Dynamic Routing: The copilot intelligently routes queries to agents that are specialized in a given domain. For instance, when a user asks for career guidance, the copilot sends that query to the career path agent, while a course recommendation request would be directed to the learning path agent.

Challenges:

• Coordination: Ensuring that each agent has the correct context and that the responses from multiple agents are coherent and relevant.
• Latency: Having multiple agents interact can introduce delays, so optimizing how and when queries are routed is key to maintaining a seamless user experience.

Functionality in Practice: Mydra’s Career Copilot

Let’s take a look at how these challenges and solutions come together in Mydra’s AI Career Copilot. Here’s what happens when a user interacts with it:

1. Step 1: Goal Identification The copilot starts by understanding the user’s career goals through natural language interaction.
2. Step 2: Profile Analysis The user shares their LinkedIn profile, and the copilot analyzes their skills, highlighting areas for improvement.
3. Step 3: Learning Path Creation Based on this analysis, the copilot generates a personalized learning path designed to help the user achieve their career objectives.
4. Step 4: Course Recommendations For each skill gap identified, the copilot recommends three specific courses that are tailored to the user’s needs, from foundational to advanced learning.

Conclusion: The Future of AI Copilots

Building an AI copilot like Mydra’s requires a combination of innovative thinking, technical know-how, and a focus on delivering real value to users. By leveraging LLMs, frameworks, and function calling, you can create a hyperpersonalized experience that adapts to each user’s needs.

Interested in learning how to build your own AI systems? Explore the Build Multi-Agent LLM Products or the Generative AI with LLMs course on Mydra to start your journey.

The future of AI Copilots is here, and with the right tools and skills, you can build it yourself.