In its simplest of definitions, Generative AI is a field of AI that uses large language models (LLMs) to help users quickly and easily generate new content in the form of text, images or other media. These outputs are a function of large datasets inputted into the GenAI platform. Now, imagine an LLM applied to your research data but mitigating the risks associated with GenAI. Given that your business is information, the area of research & analysis has emerged as a fertile ground for GenAI use cases as it provides excellent returns in search, discovery, knowledge, and application, resulting in massive value generation from legacy information assets.

In this blog, I will shed light on how to leverage GenAI for research & analysis use cases and how to identify such use cases in your own company (even beyond research organisations).

The Role of AI in Research and Analysis

By now, business executives are well aware of the revolutionary role GenAI technology can play in the future of a company. However, there is always the question of which areas should be low risk and high reward testing grounds. With vast amounts of datasets, research and analysis use cases have successfully leveraged GenAI to accelerate the pace of innovation, improve efficiency, and save on time and cost.

The evolution of AI in research and analysis started several years ago with the transition from traditional statistical methods to incorporate supervised and unsupervised machine learning and AI. Moreover, there was an increasing reliance on AI for complex data analysis and prediction tasks. Most enterprise applications used in this field have ML and AI standard features today. It’s become the norm: Do you want “ML” with that?

Cometh ChatGPT, cometh the proliferation of Generative AI to the masses with the convenience of Question Answering systems overtaking the traditional world of search. From students, to office workers, academics, scientists, developers and researchers, it was a turning point on how knowledge and information will be acquired. We have now moved from search engines to answer engines and the future outlook is action engines. Stay tuned for a future article on action engines, but let’s stay with the art of the possible today: answer engines.

Generative AI has the ability to analyse and summarise large and complex datasets, uncover hidden patterns, retrieve and re-rank relevant information, and generate valuable insights that may have gone unnoticed by the human eye. This enables researchers to explore new opportunities, uncover related patterns, make more informed decisions, and share learnings. Just like that… GenAI has made data conversational and, as such, an active participant in the decision-making process.

GenAI: Faster Time to Value (TTV) from Your Existing Data

The benefits of using GenAI in research and analysis are becoming apparent from an efficiency, time-savings, and cost-savings point of view. But how about the results themselves? Relevance is a big-ticket item when it comes to GenAI-powered research & analysis. This is most significant in use cases where vast data is provided, and researchers are looking for the needle in a haystack. Sounds familiar? When was it ever so lexical?

A recent survey sent to a large number of our users confirmed that our GenAI platform delivered dramatically improved relevance to questions across specialized areas, easily outperforming traditional keyword searches. This is due to pioneering approaches to Natural Language Models (NLP) using zero-shot AI models. What it means is that you can expect the Vectara platform to understand the essence or the intent of your question for any industry, any language, in any field of specialization. Very empowering!

In a global community, it is very handy and quite essential to be language agnostic: Ask questions in your language and retrieve results from content written in any other language. Now, every research article ever written in the world is within use and comprehension. Cross-language search provides outstanding results in multinational organisations, let alone in industries like science, pharmaceutical, medical research, education, and legal.

GenAI in Research & Analysis Use Cases

Financial Analysis

An excellent use case in the financial markets sector is when financial analysts look at quarterly earnings calls to understand companies’ financial positions. Earnings call transcripts, along with other articles and data sets can be easily ingested for analysis. Unfortunately, a keyword search alone will not provide the best answers. It’s more of a hit-and-miss. Yet, by using  LLM-powered search, analysts can get matches based on meaning and intent. Moreover, the matches can understand human language, financial services terms, and acronyms (even colloquialisms), providing excellent answers to the analyst’s queries. Finally, a handy summarisation of large amounts of data can be helpful for executive briefings and may provide a simple answer to a complex question. Transformational? Not exactly. Revolutionary? More like… GenAI.

Biomedical Research Firm

Oncotelic Therapeutics has partnered with Vectara to utilise effective knowledge management in the pharmaceutical research and development industry. Scientists and researchers are usually challenged by the laborious and expensive process of reading massive volumes of public and private research papers and publications to find valuable statistics or data points.  Vectara’s LLM-powered hybrid search aids in the innovation and creation of new groundbreaking discoveries. Publicly available artificial intelligence (AI) tools lack the required depth to provide relevance to subject matter experts despite their breadth in searching a large volume of data. Vectara was built on two key tenets: best-in-class retrieval and ease of use.

Conclusion

Vectara is uniquely positioned to help companies unlock the power of GenAI in the areas of research and analysis in biomedical research by dramatically reducing the risks of false positives, result-bias, and hallucinations, making it one of the world’s most popular GenAI platforms for these (and more) use cases. 

Recently named as one the world’s most exciting GenAI startups in CBInsights GenAI 50, Vectara has coined the term grounded generation, which means your research results are grounded in facts as a function of the inputs you provided. Moreover, your summarisations have clear source references like any proper academic paper.

As an enterprise-grade GenAI platform, Vectara has adopted an ethical AI commitment by never training its LLMs on your data and keeping your proprietary data secure on both ingest and prompt. Beware of any LLMs training on your data. As an enterprise-ready GenAI Platform, we have addressed everything from startup to enterprise: security, privacy, scalability, availability, SLA support levels to easy commercial plans.

If you’d like to learn more about Vectara and test your research and analysis use case, feel free to connect with me on LinkedIn and message me or start your free trial of Vectara from our website.

Vectara: Hybrid Search and Beyond [PDF]In the AI era, how people interact with information has changed. Users expect relevant answers to questions in natural language, not a shopping list of hit or miss search results. They expect the best semantic or exact matches regardless of typos, colloquialisms, or context. Additionally, it is Vectara's mission to remove language as a barrier by allowing cross-language hybrid search that delivers summarized answers in the language of your choice. The Internet, mobile, and AI have made information accessible, now Vectara helps you find meaning quickly through the most relevant answers. Get to know Vectara, and if you have a question, just ask.Get Introduced to Vectara