The Challenge: Too Many AI Tools, Too Little Productivity

Businesses, marketers, developers, researchers, and customer support teams are increasingly using AI as part of their day-to-day operations. As the number of organizations implementing AI continues to grow, so has a new challenge encountered by these organizations.

Many people have come to depend on a variety of different AI platforms to accomplish their tasks. For example, one platform may be best suited for generating written content, another might produce code more easily, and yet another can perform data analysis or produce creative content. As a result, users repeatedly switch between multiple applications to complete one single workflow.

Wasting time is not the only issue. Each platform operates as its own separate entity, therefore, none of them remember anything about the user or company they belong to. Therefore, users often receive generic or even inaccurate responses from the various AI platforms they have used.

The organization in this case was looking for a smarter solution: a single location where all users could access all of the different AI models, easily compare their responses, and get responses that were based on the company-specific knowledge that their companies use.

Thus, the goal of the project was identified.

Understanding the Real Problem

The challenge was not only about providing users with multiple AI options, but also about finding a way to make those options truly useful for users on a daily basis.

There were several pain points that became evident:

• Users copied and pasted the same prompts from AI to AI repetitively.
• Users spent excessive time comparing responses between AIs.
• There were public AIs that did not have the ability to connect with internal documents and company experience.
• Users had to manually refine their prompts to obtain the best results.
• Audio recordings had to be transcribed separately from the AI tool.
• Image generation was done through an additional service/platform.

As a result of these challenges, instead of simplifying the work of users, the AI models were making them do more work than before they were available.

Because of this, the focus was shifted from developing a new chatbot to creating a robust AI productivity platform.

The Solution: One Platform, Multiple AI Models

We created a unified SaaS (Software as a Service) platform that integrated different leading natural language processing (NLP) models accessed via the same user interface (UI).

Instead of having to pick one NLP model to start with, users interacted with all of the available NLP models through one dashboard.

More importantly, users compared responses from all of the available NLP models at once.

Users no longer needed to keep multiple browsers open to compare responses or enter the same prompt into multiple places.

It allowed users to quickly and easily determine which NLP model generated the best output no matter whether they were creating marketing copy, coding, researching, or creating any type of business documentation.

The process was significantly faster and much more effective.

Making AI Smarter with Retrieval-Augmented Generation (RAG)

Even though general AI models have vast amounts of information at their disposal, they do not naturally have access to a company’s internal information. The platform addressed this challenge by using a technique called Retrieval-Augmented Generation (RAG).

With RAG, the platform identified relevant business documents that could assist in answering user queries before generating a response instead of relying solely on the general AI model’s existing knowledge of a topic. These sources of information were varied and included knowledge bases, PDF documents, technical product manuals, internal policies and procedures, product documentation, frequently-asked questions (FAQs), and any custom data sets created by the organization.

When a user asked a question of the system, the platform would first conduct a search of the organization’s data to identify the most relevant information before providing that piece of context to the AI model, thus allowing it to provide an AI-generated response based on the organization’s verified knowledge.

Consequently, the user received a more accurate, more relevant, and generally more useful response in terms of supporting day-to-day business processes than they would have otherwise received. As a further benefit, users no longer had to spend time manually searching through multiple documents to find the information they needed. Instead, the information was provided to them in conversational format.

Beyond Chat: Building an AI Workspace

More than just chat functionality, the platform has significantly expanded the ability for users to do more with other tools in addition to communicating (using chat). For example, enhanced productivity was achieved by adding multiple productivity tools into one (1) environment to eliminate the need for multiple external resources by employees.

Prompt Optimization

For many users, formulating prompts that provide good output has been a challenge. The platform has a prompt optimizer that automatically adjusts the prompts created by users prior to being submitted to the AI model. As a result, users who are non-tech-savvy now have an opportunity to produce better output without having to learn any of the skills required to be a prompt engineer.

Audio Transcription

When meetings, interviews, voice memos, or any other audible conversation occur between individuals that needs to be analyzed after the fact, the first step is to have them transcribed into text format prior to reviewing them. The transcription process generally required the user to upload their audio recording file through a different application for a third-party provider (i.e. transcription services). The platform does not require an additional application, the user uploads the audio file directly to the platform, and the transcribing is done by the AI. Users can now find the documented data much quicker and easier than working through an audio recording.

AI Image Generation

Creative teams often need to produce images for illustrations, concept art, marketing materials, and social media. Therefore, the ability to create images was created within the platform. Users can now create images in their workspace as opposed to requiring them to leave that workspace and go to a different application to create their images, thereby streamlining their creative processes.

Creating a Better User Experience

Simply having technical abilities isn’t enough to achieve success.

We paid particular attention during the development of our platform to ensure that it would be user friendly for people with different technical backgrounds.

The interface will be very clean, organized, and easy to navigate, no matter who you are.

Users have a single point of access to select models, compare output, upload files, ask follow-up questions, create images and transcribe audio.

Instead of having to learn multiple systems, they now only have to learn one.

This simplicity has had a significant impact on the level of adoption of the platform between teams.

The Business Impact

With the finished platform, users will now be able to interact with AI in new ways within their daily tasks.

The platform facilitates collaboration among users through various interfaces and applications. It streamlines the process of switching between AI applications by enabling users to complete tasks within one application rather than having to go back-and-forth between multiple AI applications.

The ability for users to compare models side-by-side gives them the opportunity to make quicker decisions because they no longer have to re-enter prompts many times into several AI applications.

The integration of RAG results increases accuracy and reliability of AI responses based on established business intelligence rather than depending on what is available in general literature.

Users have the capability of creating quality outputs by optimizing their prompts, and built-in transcription and image generation will eliminate the need for multiple 3rd party applications.

These benefits combine to help users create an easier and more productive workflow.

Key Outcomes

This project created value in many categories:

• Merged multiple types of Artificial Intelligence into one easy-to-use software as a service (SaaS) platform.
• Allowed you to easily compare how multiple large language models (LLM) answered the same question in real time.
• Improved your chances of getting an accurate answer by using retrieval augmented generation (RAG).
• Decreased your reliance on using multiple independent AI applications and other third-party tools to create and develop LLM-based solutions.
• Made it easier to write prompts for LLMs via intelligent prompt optimization techniques.
• Combined audio transcription and AI-generated image services into one workspace.
• Increased productivity due to a reduction in repetitive manual work that had to be performed to create an LLM-based solution.
• Enabled users to have better quality, more timely, and context-sensitive AI-generated responses to help them make quicker and better informed decisions.
  

Conclusion

We at Riaur AI are confident that the biggest benefit of AI is its ability to streamline how people work on a day-to-day basis.

The objective of this project was not to develop yet another chatbot typical of those available today, but instead to develop an integrated AI platform that would resolve real productivity issues. In order to accomplish this, we combined several technologies including different natural language models, real-time comparison of models, retrieval-augmented generation (RAG), optimization of prompts, audio transcriptions and image generation to deliver a single cohesive session or use case for individuals to complete their work in less time and more effectively.

The result is a smarter and more efficient use of AI; reducing the amount of switching between systems and providing users with contextually relevant answers while enabling teams to accomplish much more with less effort.

If you would like assistance with developing a custom AI solution for your business, please reach out to Riaur AI to discuss the opportunities that are available.