How to Develop an AI-powered SaaS Product in 6 Steps

SaaS as an approach to software delivery and AI as a technology for augmenting software product capabilities work effectively in tandem. According to the IBM survey, in 2022, 28% of the companies had an AI implementation strategy, and 37% were developing it. As we take as a fact that 70% of software was distributed as SaaS products in the same year, we can state that the SaaS market of AI-powered applications is becoming more competitive. 

If you’re planning to create your own SaaS product, you should  take a closer look at the need for AI implementation. To implement the technology effectively, you must be aware of the steps to follow.

Why artificial intelligence & SaaS is a strong combination

SaaS, as a model of software service distribution, was created with customer satisfaction in mind. A typical Saas solution is easily accessible, affordable, and rapidly scalable. AI solutions extend the ability of SaaS products to reach out to a large audience and retain customers by providing services of high quality for a considerably affordable price. Let’s consider how AI and ML tools amplify the power of SaaS approach in terms of customer success.

Automation

The term “automation” covers a wide range of tasks that SaaS software can perform, from sending emails and creating invoices to tracking user behavior. Strictly speaking, software is all about automation: applications help users succeed in as many tasks as possible without human’s effort. With AI SaaS solutions, automation reaches the new level of its might. 

One of the brightest examples of AI-driven automation is customer support service. An AI-powered chatbot “knows” everything about a SaaS company's services and advantages, as well as about the particular customer buying history and preferences, and can provide comprehensive answers to clients’ questions. Such a virtual assistant is at work 24/7; it follows the brand's tone of voice, and is always polite and attentive.

The result: By automating the routine in the support service, in particular, SaaS companies can reduce expenses by almost a third. Moreover, this ensures consistent, timely, and accurate responses, enhancing overall customer satisfaction and loyalty.

Personalization

Email automation, which we already mentioned in the previous paragraph, can be customized based on a particular customer's preferences  and intentions. There are a lot of things that can be customized withAI algorithms: sets of recommended products and services, blocks of content, dashboards, etc.

One of the prominent personalization examples is personalized website content. Machine learning algorithms ensure that a user sees the most desirable information. The web page content – search results, product listings, CTA buttons, navigation menus, articles, pop-up offers – align with particular user's interests and expectations. 

Content customization makes users feel that the AI SaaS product is specifically tailored to cater to their unique preferences.

The result: According to an Epsilon study, 80% of users would prefer to purchase products and services from the companies that provide their clients with personalized experience. Hence, personalization enhances customer engagement and helps build stronger relationships between a client and a SaaS company. 

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Predictive analytics

How does an AI-powered SaaS platform know what products or services to recommend on a website in a section with personalized content? This is how an AI system works: it analyzes customer data and makes predictions on the information it has learned using AI and machine learning technologies.

The term "predictive analytics" covers a number of data science concepts and techniques, such as data mining and statistical modeling. Luckily, all complex things happen under the hood of AI-powered tools' that make data analysis understandable even for non-tech users.

An individual end user deals with the results of data modeling, such as blocks of personalized content. An expert who uses data analysis outputs for business optimization deals with dashboards that reflect the results of calculations in an easily understandable, visual form. Such dashboards are an essential part of big SaaS businesses: enterprise AI platforms, business intelligence (BI) platforms, and customer relationship management (CRM) systems in particular.

The result: At least half of functions in strategic planning and predictive analytics could be automated. By implementing AI in data analysis, business leaders gain deeper insights, become more successful in decision-making, and can proactively address potential challenges.

Security

AI plays a crucial role in keeping your online tools and services secure. It works like a smart detective, constantly watching how people use the software and looking for any unusual behavior that might signal a security issue. This helps catch potential threats early on, preventing phishing attacks, unauthorized access, breaches, and other incidents before they happen. 

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Think of it like a security guard who learns what's normal and alerts you if anything seems out of the ordinary. AI also stays updated on the latest online threats, ensuring your software is protected against new dangers. Whether it's stopping fake emails, keeping your data safe in the cloud platform, isolating affected systems, and initiating backups, AI acts like a  digital security guard, working behind the scenes to keep everything safe and sound.

The result: The integration of AI in SaaS applications ensures a proactive defense. Additionally, AI-driven security models streamline operations by automating routine tasks, allowing for faster response times and reduced human error in threat mitigation.

Options for SaaS Artificial Intelligence implementation: from basic to more sophisticated

A decade ago, one should have had considerable budgets and hardware capacities to leverage AI for SaaS companies' needs. One could build a comprehensive SaaS solution by training an AI model, and it required knowledge in machine learning, deep learning, natural language processing (NLP), and other domains closely related to AI. Moreover, one needed to provide an enormous volume of data for training ML models and hardware for hosting and running these models. 

Today, you can use affordable, accessible, user-friendly AI tools, many of which don’t require either deep expertise in ML or impressive budgets. As for data sources, you can use pre-trained models that don't need proprietary data (owned by a particular company) or need considerably small datasets. 

In contrast to the highly affordable and easy-to-implement approaches, you can build a custom machine learning SaaS platform using fine-tuned AI models if you’re ready to invest more. 

Let’s look at accessible options, moving from the less complex and highly accessible to more custom and technically demanding ones.

Off-the-shelf Artificial Intelligence solutions

You can succeed in the usage of AI systems for your SaaS products, relying on the ready-to-use foundational model. It’s a “template”, all-purpose system that already has a general comprehension of different domains since it was pre-trained: it "gasped" plenty of data to be able to answer questions, summarize information, create texts and pictures, ideate, and even write programming code. A foundation model can be either leveraged intact or adjusted to the particular business needs through investing time, money, and effort.

One of the most in-demand pre-trained AI models is GPT-3.5 - a large language model (LLM), the prominent representative of a group of AI algorithms that are especially good at summarizing and generating text-based content. You can integrate GPT-3.5 with your SaaS application through an API, a set of rules and protocols that allows different software entities to communicate with each other, and use it for your business advantage. (To use GPT-4, the latest and the most anticipated version of the stellar OpenAI’s model, join its APIs wishlist here.)

Pre-trained models are usually used with plenty of other tools for extended SaaS model’s functionality and customized user experience. Let’s consider the most essential “extensions”, using building a chatbot as an example.

Plug-ins and embeddings

These tools exploit your app’s internal resources such as customer data, and are the pieces of software that can be added to an existing program to enrich its functionality. Connect the chatbot with your CRM system through plug-ins, providing the virtual assistant with access to your customers’ historical data – and the bot ensures more personalized answers to the queries. If you integrate a chatbot with your product documentation through embeddings, your AI-powered assistant will be able to answer product-related queries.

APIs

APIs make it possible to enhance your chatbot's capabilities by allowing external applications or services to interact with your AI-powered SaaS product, extending its functionality. Here are some examples of how you can use APIs to provide a richer user experience, delivered by a chatbot:

• with Microsoft Azure Text Analytics, a virtual assistant can analyze users’ sentiments and tailor responses based on user emotions;
• with Google Maps API, a chatbot can assist users with geographical queries;
• with Google Cloud Speech-to-Text, users can interact with a chatbot through voice input.

While such tools as AI model plug-ins, embeddings, and APIs ensure a more customized experience for users, they can still be based on pre-trained models that don’t require additional training on data; respectively, they are not so costly. However, you need machine learning and data engineers in your team to enrich your comprehensive SaaS solution’s capabilities. In addition, a cloud platform is required for storing and processing the data.

Fine-tuned and built from scratch foundation models

The pre-trained but not fine-tuned foundation model works good for performing typical tasks. However, its capabilities are not sufficient to provide a deeply personalized user experience since the model doesn’t have a deep knowledge of the business domain and doesn’t obtain the industry-specific vocabulary. 

Moreover, a model that only has a general understanding of different business realms can’t meet all legal and ethical requirements. While Saas product customization is vital for every business eager to succeed, some industries are more demanding than others.

For example, in healthcare, LLM should operate with such industry-specific documentation as electronic health records or physician notes. In this case, fine-tuning is required to provide SaaS platform’s users with customized and relevant information. Let's consider two ways of the foundation model's tailoring to a particular project's goals.

• Prompt tuning. The easiest and the cheapest way to provide a SaaS AI with the required “skills” is prompt engineering. Strictly speaking, it’s not fine-tuning in the conventional sense; it’s the lighter version of training that doesn’t require additional data input into the model and complex algorithms. 

To enhance the model's expertise, prompt engineers – professionals with basic understanding of AI, machine learning, NLP, and programming – feed the foundation model with prompts (queries) tailored to enhance the model's expertise. In result, AI gets an “ability”to give more informative answers to the most frequently asked questions. Prompt tuning works best on early project stages when short iteration cycles are expected, and cost-efficiency is more vital than high customization. (It takes eight times smaller budget to enhance the AI model's performance by prompt engineering compared to fine-tuning it.)

• Fine-tuning. To make your SaaS AI platform able to handle complex tasks – for example you need a chatbot that is able to analyze sentiment in customer reviews, or you want your app to generate code snippets for a particular programming language – you may need to fine-tune a foundation model. It means you need not only high-level expertise in ML, but also some amount of proprietary data and computational capabilities to succeed.

If you want to build a SaaS solution that solves domain-specific problems, or you need to integrate specific business rules and logic into the AI system, training a foundation model from scratch can be a solution. While considering this option, take into account that it’s the most cost and time-consuming one. In addition, building such a solution will require strong expertise in data science, software engineering, Machine Learning and Deep Learning, NLP, and other technologies closely related to the implementation of AI in SaaS.

Six steps to build an AI-powered SaaS product

Each project’s budget and timeline has its peculiarities since the amount of investments, time, expertise, and effort fluctuates depending on a SaaS solution’s tasks and complexity. Scope of work also changes in accordance with the specific features and functionalities required for implementation.

However, some steps are versatile for any SaaS project, including the AI-powered one, and you can use the following scenario for your software product development.

1. Planning and ideation

The initial stage of any successful project is planning and ideation. Have you ever tried building something without a blueprint? It resembles sailing without a compass; you might move forward, but chances are you'll be lost at sea.

When ideating for an AI-powered SaaS product, the first question that should pop into your mind is: What problem is this product aiming to solve? Understanding the problem is half the solution. Consider the market needs, potential competitors, and, most importantly, the unique value your solution brings to the table.

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Next, sketch a preliminary design of your product. Think about the user experience – what should be the journey of a user from landing on your platform to achieving their goal? Consider potential roadblocks and brainstorm ways to overcome them.

Remember, while technology is a crucial aspect, user-centricity should be the core of your ideation. It's not just about implementing AI; it's about implementing AI that adds tangible value.

2. Conducting discovery phase

Discovery is the bridge connecting a vague idea to a concrete plan. So, what does the discovery phase entail for an AI-powered SaaS project?

• Objective setting: What are the goals? Are you looking for process automation, better decision-making, or enhanced user interaction?
• Stakeholder interviews: What do people invest in the project's success? 
• Feasibility Study: With the available resources, can this project add value to business?

In essence, the discovery phase is your research powerhouse. It's where assumptions are challenged, risks are identified, and opportunities are spotted. It's where ideas meet reality.

3. Choosing tech stack

The technological backbone of your product is the tech stack. When we talk about AI-powered SaaS, we're diving deep into two realms: artificial intelligence and cloud-based software solutions. Both realms have their set of tools, frameworks, and platforms. Your choice will largely depend on the nature of your product and features or tools you want to provide for your application, relying on AI: a chatbot, a recommendation system, a predictive analysis tool, or others.

For AI, popular frameworks include TensorFlow, PyTorch, and Keras. But beyond choosing a framework, consider the model's requirements. Will it need GPUs for training? How frequently will it require updates?

On the SaaS side, think about the scalability of your product. Will it need to handle hundreds, thousands, or millions of users? Choices here range from cloud solutions like AWS, Google Cloud, and Azure to programming languages and databases that align with your AI needs.

Another pivotal decision is between going serverless or maintaining your servers. Serverless architectures, like AWS Lambda, allow for scalability and cost savings, but they might not be suitable for all applications.

Remember, the tech stack isn't just about what's trendy or popular. It's about what aligns with your product's needs, future scalability, and, of course, your budget.

4. Hiring a team

The team is the lifeblood of your project. You could have the best tech stack, a fantastic idea, but without the right people, turning that idea into reality becomes an unrealistic task.

First of all, determine the roles you need. What experts you need depends on your project scale, as we mentioned earlier. The more customization you need, the higher the projects’ complexity, the more extended team a project requires.

While hiring experts able to fulfill your expectations, you can choose among a few options:

• hire an in-house team;
• outsource project development to a dedicated team (onshore, nearshore, or offshore);
• augment your existing team with experts you lack (to develop an AI SaaS product, you may need additional expertise in machine learning and deep learning, data science and analytics, Natural Language Processing, and other conversational AI technologies).

How option to choose is determined by the project's urgency, the specific expertise needed, your company's operational dynamics, and the strategic direction you envision for your AI-powered SaaS product.

5. Building an MVP

You have the idea, the team, and the tools. Now, it's time to get your hands dirty. But before you dive deep, consider building a Minimal Viable Product (MVP).

It's a version with just enough features to make it viable for early users. The feedback from these users is invaluable; it provides insights that can steer the development of the full product.

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An MVP also serves another crucial function – it can attract investors. Showcasing a working model, no matter how basic, can instill confidence and open doors to additional resources.

So, when building an MVP, focus on core functionalities. Polish it, test it, and then release it to a select group. Gather feedback, iterate, and refine.

6. Scaling up a project

Once the MVP is out and has garnered a positive response, it's time to scale. This isn't just about adding features; it's about ensuring that the product can handle growth – in users, data, and functionalities.

Consider infrastructure, user support, and potential market expansions. Can the current tech stack handle an influx of users? Is the AI model equipped to process increased data? Addressing these questions is essential to ensure seamless scalability.

Conclusion

Merging AI with SaaS offers unparalleled advantages to AI SaaS companies, from automation to predictive insights. The journey from initial concept to a scalable product demands careful planning and a user-focused approach. As the digital frontier expands, AI-powered SaaS products are leading the charge, setting new benchmarks for innovation and efficiency.