Generative AI is taking the business world by storm thanks to its invaluable use cases and revolutionary capabilities. Generative AI is a rapidly evolving space – within a week after GPT4’s release on March 14, 2023, there were 200 new generative AI applications with a wide swath of use cases. Large tech firms are racing to introduce generative AI engines and produce value from them.
Domain-Specific Generative AI
Generative AI-led technologies have democratizing powers, which means outcomes are widely available, free to access, and equip one user as much as the next. The differentiation is in the personalization or specificity of the models to suit a particular domain. This inevitably depends on the promptness brought about by domain experts and the application of domain-specific organizational knowledge to a use case. How the technology is applied to particular use cases will separate successful generative AI use cases from unsuccessful ones, something we call domain-specific AI.
Using supervised learning, domain-specific AI is trained for its specific industry, use case, and function. Evalueserve was an early adopter of domain-specific AI, and our generative AI also uses the approach. Domain-specific AI comes with myriad benefits, including more reliable outcomes, easier scaling across the business, and a shorter time to impact. You can learn more about domain-specific AI in this eBook.
Our Approach to Implementing Generative AI
Our approach to choosing and implementing a generative AI use case is both clearly defined and straightforward:
- Identify a high Return-on-Investment (RoI) use case.
- Create an AI algorithm by leveraging foundation models such as BERT, GPT, etc.
- Train AI with domain-specific data, using supervised learning to fine-tune the model.
- Deploy and integrate the use case, whether that’s through a custom microservice or embedding it in an existing platform or one of our products, like Insightsfirst.
- Fine-tune using domain experts’ feedback.
Evalueserve already offers a few products enriched by generative AI – with more to come. We are also looking at ways to use generative AI within the company to improve our speed and efficiency for clients without compromising the quality we’re known for. Following are some use cases Evalueserve has integrated into its products, platforms, and stand-alone solutions.
Evalueserve's Generative AI Use Cases
Here are a few examples of Evalueserve’s generative AI applications. All of these use a modular microservices architecture and are used in our products for clients, as a plugin in partner products, and as internal accelerators.
1. Summary generation
We have several summary generation models that generate summaries based on raw data, which could take various forms, from long audio recordings to transcripts. The modules have significant time-saving benefits. Additionally, models can be trained on particular datasets, giving them domain- and industry-specific knowledge in industries such as life sciences or automotive. These modules can be deployed in clients’ microservices and API architectures, used by our analysts, or included in Insightsfirst subscriptions.
2. Conversational Chatbot
We’ve developed a conversational chatbot that uses generative AI to respond to unstructured queries. Benefits of the Conversational Chatbot include:
- The ability to be domain-specific and trained on specific datasets (such as ESG, cybersecurity, etc.),
- A quick turnaround of data into insights that guide strategic decisions,
- Provides references and links to cite its sources,
- Refines responses based on themes, competitors, timeframes, etc.
- Can provide meta-analysis by clustering data to identify and summarize broad themes.
We are integrating the Conversational Chatbot with Insightfirst, our competitive intelligence platform, to provide insights even faster.
3. Synthetic data generation
As explained in detail in this blog post, domain-specific AI models often require massive amounts of data. However, organizations commonly struggle with data scarcity, which is an obstacle to training and deploying AI. However, generative AI can solve data scarcity issues with synthetic data generation. We’ve used GANs (Generative Adversarial Networks) on behalf of clients to generate synthetic data to expand training datasets and enhance the accuracy of our retail solutions, such as the Intel smart scale.
4. Reporting chatbot
First launched for a client in late 2022, our reporting chatbot uses GPT algorithms. The reporting chatbot helps with data democratization, which makes data accessible to every person in an organization, regardless of their technical knowledge, giving everyone the power to make data-driven strategic decisions. Users can pull any KPI through the reporting chatbot without needing technical skills such as coding or Power BI.
Tools in Development
Of course, we have many other foundational tools in development, including long document summarization, analysis of non-text data, and structured report generation.
Publishwise, one of our knowledge management platforms, stores and manages knowledge assets, including proposals, case studies, project briefs, collaterals, and more. We envision leveraging generative AI in Publishwise to summarize content from multiple knowledge assets simultaneously. Receiving machine-generated, logically summarized content from multiple assets will be a significant step up for clients using Publishwise to fulfill their knowledge management goals.
We also want to create an offering to analyze Excel sheets and create representative charts and graphs.
Advantages (and Disadvantages) of This Revolutionary Technology
There are countless advantages to using generative AI, including:
- Bringing efficiency to work that previously had to be done by people from start to finish
- New options for solutions and benchmarking
- Can allow for less training data
- A new user experience. Generative AI will shift how people use technology and platforms, similar to how Google upended the market many years ago.
However, there are also some risks to be aware of when it comes to generative AI. These potential issues include data privacy and information security risks, perpetuating inherent biases, and ethical issues. With the pace of development around generative AI, there will be legislation that provides guardrails around the way it is used and consumed.
Evalueserve is constantly monitoring this space and how we leverage OpenAI and other third-party tools. We are highly cautious with our clients’ data. We have safeguards in place, both for ourselves and our clients.
Generative AI is here to stay, and we believe it will be a co-pilot for our workforce and clients.
