Introduction
Many companies want to use the incredible power of generative AI as quickly as possible. The break-thru of generative AI captures the imagination, and many are excited to incorporate it into their workflows.
But how should an enterprise adopt generative AI?
Companies are racing to get the right computer power (GPUs) and hire smart people who know a lot about machine learning.
Finding these experts and resources is tough. Companies will find it increasingly difficult to bring generative AI into their own operations.
Many companies are using AI SaaS instead of building generative AI applications from scratch.
What is AI SaaS?
SaaS, also known as Software as a Service, has existed for decades. As compute and storage costs have gone down with cloud computing, AI SaaS and White Label AI SaaS has become a growing industry.
Now, with the rise of generative AI, a new category of SaaS solutions has emerged: Generative AI SaaS.
Generative AI SaaS companies provide a software to its customers without them needing to be generative AI experts. GenAI SaaS is user-friendly. Customers don't need to deal with complex issues like hosting their own Large Language Model (LLM) or fine-tuning an LLM.
Generative AI SaaS customers don't need to write and maintain large amounts of code. Generative AI SaaS systems can access other enterprise systems via straightforward APIs.
Let's explore the alternatives to using an GenAI SaaS platform.
The Unattractive Alternatives to Generative AI SaaS
Build Your Own Custom LLM
Creating a custom LLM from the ground up might seem tempting, but it's an enormous investment.
You'd need to buy a lot of GPUs, hire machine learning experts, and manage huge datasets. Many experts agree that it takes millions of dollars to build your own custom LLM.
For most, building a customer LLM is an unrealistic option.
Worse yet, even if you were to create your own customer LLM, even custom LLMs are prone to hallucination.
Fine-tune a Pre-Trained LLM Model
Fine-tuning a pre-trained LLM might seem easier, but that also has its challenges. Fine-tuning involves machine learning expertise, effort, and costs.
Fine-tuning can also compromise content safety guardrails built into the pre-trained LLM.
Like pre-trained LLMs and custom LLMs, fine-tuned LLMs will also hallucinate.
Build Your Own Generative AI Application using Pre-Trained LLMs
Companies can choose to build their own generative AI application and use a pre-trained LLM, like GPT-3.5 or GPT-4.
Building your own GenAI application requires a skilled engineering team to develop and maintain code against OpenAI's APIs.
In addition, your engineering team will need to solve the hallucination problem.
Furthermore, keeping these applications running is an ongoing operations challenge.
Use OpenAI's Custom GPTs
Building a custom GPT seems simple, but there are many issues with GPTs.
- You can only upload a limited amount of proprietary data into a GPT.
- GPTs have a tendency to leak private data and face hacking risks.
- GPTs provide limited visibility into the internal operations of the application. You won't be able to supervise the chatbot's responses, give feedback, or improve the chatbot's responses over time.
GenAI SaaS: The Faster, More Attractive Path to Generative AI
Instead of building their own GenAI solutions, companies can save valuable time and money by using GenAI SaaS.
Here's a checklist of the top 10 things to look for in a generative AI SaaS vendor.
- Free Trial Offerings
Start by looking for providers that offer a risk-free trial, similar to Alhena AI. This lets you test the capabilities before making commitments.
During the trial, businesses can explore the promised features of the Generative AI solution.
This hands-on experience helps you evaluate how well the AI fits your specific needs and functional requirements.
Request a demo of Alhena AI or create a generative AI chatbot for free with Alhena AI.
2. Seamless Knowledge Ingestion
Find a solution that gathers information from various sources such as product documents, FAQs, wikis, forums, and support tickets.
Knowledge ingestions should not require much preparation on your part.
3. Customization of Tone and Personality
You should be able to specify the tone and personality of the generative AI to align with your brand. For more on this topic, read How Generative AI Improves Call Center Automation.
This will help make your generative AI interactive, authentic, and engaging. Users are more likely to connect with an generative AI that speaks in a manner compatible with the brand they know and trust.
4. No Hallucination
Hallucination undermines the accuracy of most generative AI chatbots. It also takes away the credibility of generative AI chatbots. Inaccurate or misleading responses undermine customer trust.
Prioritize solutions that have strong mechanisms to prevent hallucination, providing reliable and accurate responses.
For example, Alhena AI specializes in avoiding hallucination. Over 80% of Alhena's technology focuses on preventing, detecting, and suppressing hallucination.
Watch a video that shows Alhena AI and a custom GPT trained on the same knowledge. The GPT hallucinates, but Alhena AI doesn't:
5. User-Friendly Interface with KPIs
A good generative AI SaaS solution should have have an intuitive UX, even for non-technical personnel. The UX should allow administrators to monitor the performance of the generative AI and update it as needed.
This allows for fine-tuning of its performance. Gaining understanding through KPIs is crucial because it improves control over AI capabilities.
6. Flexible Integration Across Platforms
A good generative AI SaaS solution should integrate with multiple channels, like email, Discord, Slack, and web chat. This guarantees a connected user experience, making it easy to blend generative AI into various workflows.
7. Compatibility with Existing IT Stack
Choose an GenAI SaaS solution that works well with your current systems, such as your help desk, chat window, and CRM.
This guarantees a seamless transition and maximizes the value of your current investments.
Integration with your IT stack streamlines processes, making it easy to incorporate generative AI without disrupting your established workflows.
This compatibility ensures efficiency and minimizes the effort needed for a successful implementation.
8. Ability to Take Action
Beyond providing answers, seek a generative AI SaaS solution that can take action.
GenAI SaaS can help customers and employees self-serve, making everything more efficient.
9. LLM Agnostic
Any generative AI SaaS solution will have interface with an LLM on the back-end.
Given how fast the LLM market is changing, be sure select a generative AI SaaS solution that is LLM-agnostic.
Today's best-in-class LLM might transform into a laggard LLM within a few month's time. You want a GenAI SaaS solution that can switch LLMs when needed.
10. Data Security and Privacy
Prioritize GenAI SaaS providers who are SOC 2 Type 2 compliant. SOC 2 Type 2 compliance ensures the that the SaaS provider adh
Conclusion
With SaaS AI, the above checklist helps businesses maximize ROI, mimimize time to market, and manage risk.
Alhena AI, with a risk-free trial, strong functionality, and a proactive approach to data security and privacy, make it the ideal GenAI SaaS solution.
Elevate your AI SaaS experience today with a Alhena AI. Request a demo of Alhena AI or create a generative AI chatbot for free with Alhena AI.
