The Vital Role of AI in Data Processing

Anyone who deals with RFPs on a day-to-day basis knows that the sheer volume of information can often feel like sailing through uncharted waters. Navigating that sea of data is a tricky task that requires both skill and precision. Here, too, AI is your (best) friend. Thanks to certain models, filtering and processing that information becomes a lot easier and you focus on delivering quality. 

What can you expect?

In this blog, we delve into:

• The difficult task of processing vast amounts of data and information. 
• The significant help AI offers in overcoming challenges 
• How nuances and criteria can complicate the process
• The importance of fine-tuning AI models

Mastering the Data Deluge

Do you know what the real challenge is? Most organisations simply have too much data, too many files, stored in too many locations. Maintaining an overview of the relevant information while handling vast amounts of data and information without an adequate tool simply is becoming impossible. 

Those large amounts of documents and data, even when they are brought together in a well structured library, easily overwhelm you.  It has become impossible, across teams, to come to an optimised library. Leaving you with the massive challenge that you simply cannot find what you need in a timely manner.

Frequently time gets wasted on topics that one of your colleagues worked on very recently,  while you’re not aware of that information. The lack of a “collective memory” leads to a multitude of inefficiencies, delays and errors in the final outcome. 

In this context, an intelligent, pro-active Collective Memory is the solution to your problem. A Collective Memory that collects and stores information from different sources and delivers the relevant information to the user in an automatic and proactive manner. Ofcourse, a central warehouse alone is not enough, it needs to be equiped with the right tooling to support your RFP processes you currently have in place.

The Evolution of AI Models

Artificial intelligence has experienced remarkable growth in the past few years. Not only does it support us in everyday life, but it also does so when processing large amounts of information and data. Three different AI models have emerged so far, each with its own unique evolution and strengths. 

First, there are the “classical” machine-learning models. They include a set of powerful algorithms that include classification, clustering, regression and natural language processing. Over time, large datasets were used to refine these models.

Additionally, there are neural networks. Think for example of frameworks such as TensorFlow. They are inspired by the neural structure of your brain. Therefore, they are adept at learning difficult patterns and representations from data. 

Finally, there are also generative AI models, for example language models such as ChatGPT. They use techniques such as prompt engineering and fine-tuning with custom data. This allows you to generate human-like responses and creative outputs.

 

Here, in itself, it does not matter what company you have or what sector you operate in. But the question every company should be asking is, “What impact will it have on our processes to use generative AI?”. Do the exercise beforehand to explore which model fits best, how you’ll apply deep learning, what you’ll obtain, etc. 

Unravelling Complex Information through Document Analysis

Dealing with extensive files, such as RFPs, can be quite daunting. Especially when they reach a page count of 60 and are filled with questions, requirements, tasks and attachments.

With this in mind, we at SEQUESTO wanted to make sure this first step was simplified and more manageable. We came up with a solution that automatically analyses the tender documents and then detects and categorises different types of information. This allows your team to quickly extract essential elements from documents. Precious time and effort saved, in other words. 

Efficient Information Retrieval

When it comes to information retrieval, the platform offers multiple options to choose from. 

On the one hand, one can take full control of the search by using a very enhanced keyword search, which automatically includes synonyms and also does translations to detect content stored in different languages. This advanced search capability greatly increases accuracy. 

On the other hand, you can fully rely on the AI capabilities of the platform to detect and retrieve the most relevant information from the collective memory.  In that regard the algorithms are even capable of matching and retrieving information stored in other languages and translating that information on-the-fly. 

These capabilities are not only available to retrieve information coming from similar Question & Answer pairs, but also from relevant text blocks. Out of the thousands of documents stored in the intelligent library, powerful AI algorithms identify the appropriate sections and present the most relevant paragraphs to the user. That way all needed information is delivered at your fingertips whenever needed. 

Crafting Tailored Responses 

This is where we present the generative model. It allows you to rewrite, summarise, expand or simplify previously generated answers. You thus create a tailor-made answer that meets the specific requirements. 

An example: 

You find a previously generated answer intended for customer X. Now you want to apply this answer to customer Y’s question, only this one emphasises ecological and environmental considerations. The model then provides the best possible, rewritten answer with customer Y’s values and norms in mind.

What used to take hours, can now be delivered in a matter of minutes. The generative AI model does the hard work and you focus on the fine-tuning only.  

Navigating Nuances in Data

When it comes to data management analysis, you come across a huge number of nuances in customer data. There are exclusion criteria and red flags to consider in RFPs. A generic model can distinguish the subtleties between different, closely matched categories, but that too requires fine-tuning. Moreover, different clients may assign different labels to the same element, making it even more complicated for your team. 

While AI models today have considerable knowledge, they may sometimes lack additional expertise that is vital. This is why we stress the importance of fine-tuning. By leveraging the latent knowledge in the documents you receive, tasks can be automated and you will discover new opportunities. 

Give superpowers to your team by using AI powered RFP Automation tools to find, retrieve and create the right information when handling RFPs, RFIs, Proposals & Questionnaires, …