AI assistants have become an inseparable part of our daily routine, from using home assistants for recipes to checking nearby restaurants on our smartphones.
In fact, Over 8.4 billion AI assistants are projected to be in use by the end of 2024—more than the world population!
Today, these assistants do much more than just answer questions. They help with content creation, workflow automation, and much more. But there’s a catch: publicly available chat assistants aren’t great for your specific requirements.
That’s why it’s more vital than ever to know how to make your own AI assistant. And we’re here to guide you!
At the end of this blog, you’ll not only know how to build your own chat assistant for your specific requirements, but we’ll also provide you with a smarter, easier alternative that eliminates all the guesswork!
How to Make Your Own AI Assistant (Use Cases & Tools)
⏰60-Second Summary
- An AI assistant is a chat-based AI application designed to perform work-related and personal tasks
- It utilizes Machine Learning and Natural Language Processing to understand queries in plain language and can be leveraged to perform specific functions
- You can build your own AI assistant with careful planning and model training
- As the everything app for work, offers a built-AI assistant that is directly integrated into your workspace
- You can utilize Brain’s functionality to easily perform project-specific actions such as creating documents, checking progress, etc.
What is An AI Assistant?
An AI assistant is a computer program designed to automatically perform actions that would otherwise require human effort and intelligence. By using AI to automate tasks, you can spend your time on more complex projects.
These digital helpers understand queries in human language through the power of Natural Language Processing (NLP) and Machine Learning (ML). This helps them perform specific actions requested by you, the user.
While the capabilities of an AI Assistant depend on what it has been programmed to do, examples of some tasks they can do include:
- Answering your questions based on their knowledge or information sourced from the internet
- Setting and executing reminders at a time of our choice
- Writing emails or text messages
- Controlling the smart gadgets in our homes
- Generating images and graphics
- Summarizing a web page (or any other information)
Examples of some popular AI virtual assistants include Google Assistant, Siri, Alexa, and ChatGPT.
Benefits of Building Your Own AI Assistant
While you can use personal AI assistants for a great number of things and integrate them using APIs, building an AI assistant offers four unique advantages.
- Customization: When you build your AI assistant, you can customize its UI or any other features just as you like, something you can’t do with third-party AI assistants
- Efficiency: A third-party assistant often processes the queries of a million users at any given time. Your own AI assistant would be processing only your and your organization’s queries, ensuring efficiency
- Privacy: Your personal AI assistant gives you better data privacy and security than a third-party assistant. Data remains private within your organization, and you decide how it’s used
- Scalability: A custom AI assistant for your organization is incredibly scalable and flexible. You can integrate it with any of your custom applications and add new features or capabilities with ease
How to Make Your Own AI Assistant: A Step-by-Step Guide
Step 1: Define the use case and capabilities
The first thing you want to do is define the use case of your AI assistant—its capabilities and limitations.
If this planning isn’t done carefully, it’ll leave you with a build that can’t perform the intended tasks in a desired manner. Here are the things you must clearly define and note about your AI assistant before you start working on it:
Type of assistant
Are you building a personal AI assistant? Or an assistant that would be used for cases like customer support or business operations? While a general-purpose AI personal assistant has multiple capabilities that allow it to perform many kinds of actions, case-specific assistants require special training.
Target users and their skill set
Who are the target users of your AI assistant, and what is their skill set? Understand how familiar they are with AI apps, what kind of language they use to define a problem, and how they prefer to use a virtual assistant (i.e., by sending commands or using their voice as an app or on the web).
Problems to solve
Consider the AI assistant’s capabilities and what you want it to solve. For instance, a productivity assistant needs the ability to manage your calendar, email, and to-do list at a minimum. Or, you may also want it to summarize your meetings and write emails for you.
Limitations
Developers often set some limitations on the AI assistant to ensure that it doesn’t lead to any unfavorable outcomes. So, define those limits well in advance. For instance, how long should it save user data? What type of actions should it not perform? Define and document these and any other limitations you want in your assistant.
💡Pro Tip: While setting limitations, create a “Will Do / Won’t Do” list. For example:
✅ Will Do: Answer customer FAQs, provide order tracking info
❌ Won’t Do: Process payments, store sensitive user data
Step 2: Identify the tech stack
Once you have documented the capabilities and limitations of your AI assistant, you can think of the right technology stack to build it. This includes all the libraries and frameworks you’ll use to fast-track the development of your assistant and other infrastructure, such as:
- The programming language you’ll use (i.e., Python, Java, C++, etc.)
- Package installers for the language you plan to use (i.e., a Python package manager for Python)
- Hosting environment (i.e., self-hosting, cloud hosting, etc.)
- NLP Library or framework (i.e., NLTK, spaCy, Gensim, etc.)
- ML libraries and frameworks (SciPy, TensorFlow, NumPy, etc.)
- Voice recognition libraries (if you want your assistant to have speech-based capabilities)
💡Pro Tip: Choose a tech stack based on scalability and ease of integration.
✅ Avoid overengineering in the early stages
Step 3: Find training data
Now, you need to find the data to train your personal AI assistant. You can source this data from many places, such as third-party data sites, user-generated data sources, and your organization’s activity logs or customer data.
As far as data types are concerned, you’ll need three types of training data for your AI assistant:
- Human language data for NLP training
- Speech data for speech-related training (if you want to integrate speech-related features)
- Task-specific data to train the assistant on tasks it will perform
💡Pro Tip: Regardless of the dataset you choose, make sure that it’s large enough to train an AI model. A good starting point is the 10x rule, which suggests having a dataset at least 10 times larger than the number of parameters in your model.
Step 4: Clean and prepare training data
Once you have your data, it’s time to clean, label, and prepare it for training the assistant’s model. This is a crucial step because it will define how the model interprets your data, so don’t rush this step. Here’s how you’d perform each of them:
- Cleaning: In this process, you remove all the errors and anomalies from your data, such as empty rows, outliers, duplicate values, etc. This is done to ensure that the data your model will train on is accurate and free from any kind of misrepresentations
- Labeling: This is the process of correctly tagging, categorizing, and labeling the data in your dataset to ensure that the model can interpret it correctly during training. The relations your model will establish between different data points depend on this process
💡Pro Tip: Once you’ve cleaned and labeled your data, divide it into two datasets—one each for training and testing purposes. Keep 70% of the dataset for training and 30% for testing.
Step 5: Train your assistant
Your data is now ready, and your tech stack is in place. It’s time to start training your AI assistant. Install and launch the necessary tools in your hosting environment and feed them with your training dataset. Tweak training parameters like training rate and batch size, and start the training process.
The exact steps for this process vary depending on the NLP and ML libraries you choose, so refer to the manuals of your tech stack. To reduce errors, monitor the training process continuously.
💡Pro Tip: If the training speed is slow, adjust the learning speed and batch size parameters and restart the process. If you face any errors, refer to the troubleshooting tips of your libraries.
Step 6: Test the assistant
Once your AI assistant has been trained, test it on the testing dataset. Check the accuracy of its performance. At this stage, you may face two kinds of problems:
- Overfitting: This happens when the training model memorizes training data instead of generalizing from it. As a result, it performs accurately when you test with the training dataset but poorly when tested with new data. Techniques you can follow to fix this problem include regularization, ensembling, etc.
- Underfitting: This happens when the model doesn’t build relationships between user input and output parameters, ultimately failing to work on both training and testing datasets. Generally, you can fix this by extending the training time or using a larger/more complex dataset. If it doesn’t work, you can try advanced techniques like feature engineering or switching to a more complex model architecture
Retrain your AI assistant model with the solutions provided above to fine-tune its functionality. Once it starts generating accurate results with the testing dataset, move to the next step.
💡Pro Tip: Create scenarios that push the boundaries of your assistant’s capabilities, including long/short inputs, inputs in different languages, inputs with special characters or unusual formatting, and incomplete or ambiguous requests.
Step 7: Design the user interface (UI)
When your AI assistant starts to work as expected, you can shift the focus to its UI. Ultimately, a chat assistant’s personality is as good as its user experience (UX)—nobody wants to use one that looks and feels choppy. So, you must design a user-friendly UI for it. If you’ve never designed one yourself, hire a UX designer for the job!
Once the UI is designed, combine it with the Assistant and deploy it in your hosting environment to conduct your final testing and debugging.
💡Pro Tip: Add smart UI features, like auto-suggestions and quick replies, to speed up interactions with smart predictions.
Step 8: Conduct final testing and debugging
It’s time to conduct your final testing on the AI assistant you built. Ensure that the UX, the assistant’s AI model, and all other elements are working as intended. Send the prompts to perform your desired tasks and see how accurate the outcomes are. Also, test the speech-based functionality.
To do this, invite some users from the assistant’s target user base to try it. See how they frame their queries and how well the assistant responds to them. If something is not working as intended, debug and fix it.
💡Pro Tip: Invite some users from the assistant’s target user base to try it. See how they frame their queries and how well the assistant responds to them. If something is not working as intended, debug and fix it.
Step 9: Launch and monitor
Finally, you can make it accessible to the target users within or outside your organization. Monitor how well it performs in the real world and analyze user feedback. Based on the feedback, improve it as required.
💡Pro Tip: Help your AI assistant continuously improve by feeding it with new data. Add real-world interactions to improve accuracy and adjust language models to understand user intent better.
Don’t want to start from scratch? Take your lead from the power players!
One popular approach is using OpenAI’s powerful language models.
You can access these models in a couple of ways: directly through their API (which requires an API key) or, more conveniently, using their Python library, which makes things much smoother. A key tip for any AI assistant is managing the conversation history. It’s like giving your assistant a good memory!
You’ll need to store past interactions, whether in your code for quick chats, a file for more involved conversations, or a database for complex projects. When you ask your AI something, include the relevant history in your “prompt” so it understands the context. If you’re using OpenAI, their Python library is your best friend, handling all the technical details of communicating with their servers.
Challenges of Building Your Own AI Assistant
Despite all the libraries, frameworks, and community support available, creating your own AI personal assistant isn’t easy. You’ll face challenges, including:
- Technical complexity: The process of building an AI assistant is complex. We’ve presented it in a simplified form here, but in reality, it’s technically difficult (especially if you’re not a software developer or engineer)
- Cost: The cost of building, maintaining, and continuously improving a custom AI assistant is pretty high. UI design, server cost, and development costs can easily run into thousands of dollars if you want to get the most out of your AI assistant
- Privacy issues: While a custom AI assistant can give you more control over your data privacy, that control also comes with greater responsibility. When all user data resides on your server, the responsibility to ensure its security is yours. In case of any data breach, you and your organization are held accountable
Why Brain is a Smarter Alternative to Your Own AI Assistant
If you want to avoid the challenges involved with building your own AI assistant but still use one for your organization, there’s a hassle-free solution available already!
—the everything app for work—comes with its own AI assistant, Brain, which teams can use across a variety of use cases.
Thanks to its deep integration with ’s project management features, Brain helps you find information, create content, and perform other project-specific actions with a simple text prompt.
Let’s explore what it is and how it can help you get more done at your workplace.
What is Brain?
Brain is the AI functionality built into ’s project management platform. It’s tightly integrated with all features in your workspace, including its internal communication feature, Chat. You can find out more about Brain here.
Due to its deep integration into your workflows and any documentation available within the workspace, Brain is always context-aware. Whether you use it to draft a message to your colleagues about projects or while searching for a document, it always finds the right information from your workspace.
📮 Insight: 37% of workers send follow-up notes or meeting minutes to track action items, but 36% still rely on other, fragmented methods.
Without a unified system for capturing decisions, key insights you need may get buried in chats, emails, or spreadsheets. With , you can instantly turn conversations into actionable tasks across all your tasks, chats, and docs—ensuring nothing falls through the cracks.
Benefits of using Brain
Brain allows you to use AI’s strengths without facing the challenge of building your own assistant. Here are four of its major benefits:
- Higher productivity: It allows you to easily implement AI productivity hacks that integrate deeply with all your projects and their files, tasks, whiteboards, etc. This eliminates the need to jump between different tools for your workflow
- Ease of setup and use: Brain is also significantly easier to set up and use than a custom AI assistant. Simply sign up for a account, and you can start using Brain right away
- Affordability: Brain costs only $7 a month, which is significantly lower than the cost of building and operating your custom AI assistant. It’s also much cheaper than other AI assistants with similar capabilities (i.e., ChatGPT Plus and Gemini Advanced)
- Better security and privacy: Finally, Brain also offers better security and privacy for your data as it’s complaints with data security standards like GDPR, HIPAA, and AICPA SOC2
How to use Brain as your AI Assistant
There are many ways you can use Brain as your AI assistant of choice for your work. You can ask it about project and task statuses, and it will give you an up-to-date answer.
And you can also ask it to summarize documents, meetings, chat threads, transcribe recorded Clips, or any other bit of information you found elsewhere. brain doesn’t skimp on important details!
It also comes with a “catch-me-up” feature to easily pull updates for specific periods while you are away. Just ask AI the question, and it will tell you what happened while you were away, including completed tasks, delayed tasks, updates from team members, documents created, etc.
Last, but certainly not least, it creates documents, mind maps, emails, and more. Brain integrates with Docs, adding generative AI functionality to the document editing feature of .
So whenever you’re creating a document, you can tap into its power to generate a quick Outline or even your entire document, depending on your requirements.
Boost Your Productivity With ’s AI Assistant
AI isn’t just a buzzword anymore—it’s redefining how we work and live. It can create your emails, help you with research, and automate mundane tasks. So, if you want to beat the competition, integrating AI into your workflows is no longer optional!
And as we’ve shown here, building your own AI assistant is complex and costly. However, with , you get a versatile, affordable solution through Brain.
Its powerful features fetch project details, summarize chats, create mind maps, and so much more—all for your project management needs!
So what are you waiting for? Sign up to , and experience the power of AI for yourself!
Everything you need to stay organized and get work done.
