Developing an On-Premises AI Chatbot for Small and Mid-Sized Businesses

AI computing is changing the way we approach business. Organizations are able to operate with efficiency they could only dream of a few years ago, accessing new opportunities for growth and development.

The AI chatbot is one of the most exciting developments for small and mid-sized businesses. These smart solutions complement human customer service teams, handling a range of different customer queries and service requests, while human personnel focus on providing more in-depth support. Customer service is also available 24 hours a day, without increasing the cost of delivery.

In this article, we're going to be looking at how your small or mid-sized business can develop its own on-premises AI chatbot. With our solutions on your side, you can unlock the benefits of a self-trained, self-developed, self-managed solution.

For many small and mid-sized businesses, an on-premises AI chatbot can feel like it is out of reach. With so much AI computing power and so many resources needed to develop and operate this kind of solution, many business owners may not even consider this option. 

However, you might be surprised to discover that developing a chatbot on-premises is not only possible, it can even be relatively simple. 

Avalue provides small and mid-sized business owners with the On-Premises Generative AI Server solution. This is affordable yet powerful hardware that puts AI computing within reach of businesses of all sizes.

When deployed with the FineTune Expert software, this server becomes a valuable resource for training your own on-premises chatbot. The server hardware gives the bot access to the enormous amount of data needed for effective training, while the FineTune software makes training easy. The intuitive interface means you won't need a master's degree in data science or any specialized AI knowledge to achieve industry-leading results.

Developing an on-premises AI chatbot involves three distinct steps. This takes the bot from the initial stages of language processing and recognition to specialized training, eventually generating responses for itself.

Developing and training an AI chatbot is similar to hiring and onboarding a human member of staff. You wouldn't expect this member of staff to know the ropes straightaway, and you would anticipate a process of coaching.

At the same time, you'd expect them to have a solid foundation of background knowledge. For example, if you were to hire a chef for a Thai restaurant, this chef might not be familiar with the specifics of your kitchen. But they would be familiar with cooking techniques and the general requirements of their job.

This is why you don't start from scratch when you build an AI chatbot. Instead, you utilize a pre-trained large language model. This pre-trained model has already gained a strong understanding of how human language and interaction works after exposure to huge amounts of text-based data.

In other words, you're already working with a solution that understands language and how it is used. This is a solid starting point for your bot. 

Next comes the fine-tuning stage. Your on-premises AI chatbot will be exposed to further data that goes beyond typical and general human interaction and moves into more specialized territory.

For instance, your bot may now learn about the common questions your customers or other users are going to be asking. It will also gain an understanding of how your business operates and how to resolve the most common problems. The bot is in the process of developing its own language capabilities and adapting this in order to excel at customer service within your own organization.

To extend the metaphor of the chef in the Thai restaurant – the chef already understands how to cook high-quality food. Now they are learning how to provide your restaurant's specific Thai dishes.

The final step in the process is inference. This is where the 'AI' aspect of AI computing really comes into its own. The trained bot is now able to make predictions and formulate its own answers to customer queries and inputs.

Not only this, but the chatbot is able to generate a text response. Essentially, the artificially intelligent solution can deliver support to the human user in a way that replicates natural human text conversation.

This step is more sophisticated than simply reiterating a pre-programmed response; the chatbot is able to consider various cues to provide its own self-formulated response. Completing the earlier analogy – your chef is now able to take their pre-existing knowledge and direct training and create a new dish based on your recipes and menus.

When the AI computing revolution really began to gather pace a couple of years ago, everyone wanted to get in on the act. This meant small and medium-sized businesses wanted to leverage the benefits of smart technology and customer support chatbots. Typically, they achieved this by outsourcing development, or by purchasing a pre-developed chatbot solution.

So why should businesses bother developing their own bot? What makes an on-premises developed solution the superior option? 

One of the key advantages is that your chatbot is designed with your specific needs in mind. You're not having to fit a square peg into a round hole – you're working with a solution that's built for your exact purpose.

All of the data remains on-premises, on your own server and within your training loop. This is critical, as it effectively helps you guarantee compliance with data protection and privacy legislation.

The server and software is intended to offer an affordable solution for small and mid-sized businesses. As you will avoid paying the recurring subscription costs associated with outsourcing, you should find the expense more manageable in the long term.

To discover how our on-premises generative AI server can provide the boost your business needs, take a look at the product specs in more detail, or reach out directly to the Avalue team.