In today’s fast-paced business world, staying ahead of the competition requires embracing emerging technologies. Two such hot technologies these days are AI (Artificial Intelligence) and SaaS (Software as a Service). When combined, AI-SaaS integration offers numerous benefits for businesses across many industries.
Recently, we integrated artificial intelligence (AI) into a client’s SaaS ecommerce analytics application.
Here is how we collaborated with this client:
- We brought to our customer’s attention some innovative integration ideas for Open AI (Chat GPT) with their platform.
- We presented our ideas for their feedback.
- These discussions developed into brainstorming sessions about other potential use cases of Open AI in their application.
- We finalized an AI implementation roadmap and priorities.
- We started developing against the agreed upon roadmap and timelines.
We love this type of ideation. This is how we engage together with non-technical SaaS startup founders as their technical leadership team.
We realized with so much buzz about ChatGPT and other forms of AI, it gets quite confusing to a non-technical user on what integration options exist.
To help other startups, we put together a high-level guide on the primary AI integration for SaaS applications.
This serves as a starting point for discussions and collaboration with your technical team. It’s important to ensure the integration aligns with your business objectives and maximizes the value AI can bring to your business and users.
Artificial Intelligence (AI) is a vast and diverse field with multiple sub-disciplines and branches, including:
- Machine Learning (ML)
- Natural Language Processing (NLP)
- Computer Vision
- Cognitive Computing
- Expert Systems
This article covers the primary AI sub-disciplines so you can make a more informed decision about what you need to implement to your platform.
Integration of Open AI (ChatGPT) into startup applications
Often when startups consider AI integration for software solutions, one popular choice that comes to mind is OpenAI’s ChatGPT. And how to incorporate it into your platform or application.
ChatGPT falls under the category of Natural Language Processing (NLP) within AI.
It can be a valuable option for certain applications. However, it’s important to note that ChatGPT has its limitations. It has been trained on specific data and is designed for specific tasks. Like humans, it can make mistakes and may not always provide the exact responses you expect.
Nonetheless, for many use cases, ChatGPT can be a great fit and offer valuable functionalities.
In a recent AI Integration for a SaaS ecommerce analytics platform, we integrated Open AI (ChatGPT Generative AI) to enhance the user experience.
“Our customers – are e-commerce businesses, and the Open AI Chat GPT product can really help them save time, while they’re creating text-based content for their business.”
Charles, founder of ecommerce SaaS company from United States
Large Language Model (LLM) for your business
Incorporating a Large Language Model (LLM) into your SaaS business can bring significant value and enhance its capabilities in many ways.
You have the option to train your own Large Language Model (LLM) using your custom dataset.
Large Language Model (LLM) is a type of Natural Language Processing (NLP) technology.
To illustrate, consider a scenario where you’re developing an application that analyzes X-ray images to identify potential signs of a specific type of cancer in patients. You can train the LLM on specific X-ray data to detect relevant indications of cancer. You can integrate this intelligence into your SaaS application, providing it with unique intelligence and enhanced functionality. This integration allows your application to leverage the power of trained LLMs, improving the accuracy and efficiency of the diagnostic process.
Benefits of integrating an LLM into your application include:
- enhanced user experiences
- increased customer satisfaction
- automation of manual processes
- powerful data analysis
- and many more benefits…
It's important to note that integrating an LLM into your SaaS application requires careful consideration of user privacy, data security, regulatory compliance, and ethical use. The user data should be properly processed and stored, and you should address any potential biases in the LLM's outputs.
Custom Machine Learning (ML) Algorithm for your SaaS application
You could also create a custom Machine Learning (ML) algorithm. ML is a branch of Artificial Intelligence (AI) that allows machines to learn from data without any explicit programming.
This approach allows you to create tailored solutions for your specific needs. For instance, let’s consider implementing a Recommendation Engine for your SaaS application.
Suppose you’re building an app that suggests the most suitable doctors for patients to receive treatment from. To achieve this, you can utilize various data points about the patient and the doctors to generate personalized recommendations. While the concept may sound simple, it can become increasingly complex as you accumulate and analyze numerous data points to build accurate recommendations.
For example, imagine you notice that users in New York often prefer multilingual doctors, even if they haven’t explicitly filtered for this criteria. By leveraging this data, you can provide enhanced advice to users by taking their preferences into account. This demonstrates how additional data points can improve the recommendation engine’s effectiveness.
The ability to infer user preferences and offer relevant recommendations without explicit filtering can greatly enhance the user experience.
However, it’s essential to strike a balance between providing valuable recommendations and avoiding excessive intrusiveness, ensuring the user’s privacy and comfort.
Using ML for SaaS applications offers flexibility and control in recommendations, enabling personalized user experiences while respecting privacy, preferences, and regulatory compliance.
Third-Party ML Algorithm
Creating a custom ML (Machine Learning) algorithm may not always be necessary for your needs. In many cases, using a third-party ML algorithm can be a suitable solution.
For example, platforms such as Amazon Personalize and Azure Personalize provide ready-to-use recommendation engines. When integrated correctly, these engines can be highly effective by providing sophisticated algorithms that can cater to your requirements.
Another option to explore is purchasing a pre-built algorithm. However, it’s important to proceed with caution and conduct a Proof of Concept (POC) before investing money into such solutions.
A POC allows you to test the algorithm’s suitability and effectiveness within your specific context. Ensure it meets your expectations and aligns with your business objectives.
By considering third-party ML algorithms or pre-built solutions, you can leverage existing technologies and save time and resources on algorithm development.
Selecting the correct approach for incorporating AI into your product depends on your specific business situation. It’s important to ensure the integration adds value to the end user and aligns with your business objectives.
Working with experienced technical leadership is vital to make informed decisions and avoid wasting time and resources going in the wrong direction.
Keep in mind whichever option you choose, there will still be ongoing maintenance and support required to keep it functional, up to date and secure.
If you have any questions or need further guidance, feel free to reach out. We’re always interested in exploring new opportunities for AI to increase the value of SaaS applications.