The fantastic opportunity for Artificial Intelligence (A.I.) to better our business and personal lives is all the rage these days. With all this incredible innovation, there are terrific possibilities, for example, to greatly improve cash flow for your business by analyzing your accounts payables and receivables. Or you can use A.I. to crawl all your Office 365 documents to quickly and easily develop a business plan in minutes instead of the manual and time-consuming tasks of human interpretation then writing a traditional business plan that might take days, weeks or months. Or, the holy grail of all time, maybe the cure for dreaded diseases such as cancer can be found by using A.I. to analyze already available data.
The point is that no one disagrees on the great promise that A.I. can provide for positive outcomes, but A.I. is not magic (yet) and still relies on a lot of common sense to be useful and efficient. At the core of most A.I. systems are a Large Language Model, or LLM, that is basically a trained computer brain that understands the specific ‘language’ and vocabulary of your business. It’s very important that the LLM is fed data in real-time, at all-times, from all available data sources because to make good decisions based on data, you, obviously, need the most recent data.
Just as important, if not even more important than fast, real-time data ingestion, is the critically important need to ‘clean’ your data. This might sound simple enough because we’ve all heard the old phase of ‘garbage in, is garbage out’ and this couldn’t be truer, and with A.I. especially. With A.I.’s machine learning capability, these systems not only can get smarter much quicker, but they can also get dumber just as fast and start making questionable decisions, provide biased outcomes and deliver wacky ‘hallucinations’ that we are hearing about in the news so much recently.
Collect data in real-time with distributed cloud capture capability
As mentioned above, your LLM should be constantly evolving and learning to provide the greatest value. And the way that you allow your LLM to evolve in the most effective manner is by providing current, real-time data available from all your business processes.
Creating smart LLM's with real-time data capture
For example, in the hospitality industry you would want to scan and import all the packing lists for inventory delivered by your vendors. Imagine that you have order a new item such as Hamburger Buns that is not in your inventory control system and it’s abbreviated on the packing list as “HMBGR BN”. As you can tell this is not actual words and needs to be input your line of business system.
So, therefore, with a centralized digital capture workflow solution your hospitality business can now easily capture this data and automatically add “HMBGR BN” into their LLM so that your A.I. becomes smarter by now knowing this is Hamburger Buns. And because the system knows Hamburger Buns with real-time understanding then immediately, we can make intelligent decisions on best pricing among vendors, or better discounts with bulk purchasing, as well as managing the proper inventory. As you can see illustrated in this one simple example, getting real-time data automatically into your LLM provides a lot of great outcomes for business efficiency.
Ingest, validate and provide ‘clean’ data to LLM’s through the capture process
About a decade or so ago, the term ‘big data’ was all the rage much like A.I. and LLM’s are today. Big Data is still a very real thing but in some people’s zest for collecting ‘volumes’ of data, instead of ‘clean’ data, they didn’t solve any sort of problem with providing better data analysis. In fact, many Big Data systems failed because they made a huge problem by introducing inaccurate, wrong or the incorrect types of data into their system which made a huge problem that most of the time was never fixed, and these projects were simply abandoned.
In these cases, many decades of experience in the document capture industry have taught us the skills to ‘clean’ our data within the capture process BEFORE exporting to a document management storage system, for example. Now, instead of a document management system for storage, we want to feed the LLM, and we can still use the same logic, plus experience, expertise and technology tools to ‘clean’ our data.
Data Cleansing process with Data Capture for LLM's
Some of these techniques include data field constraints such as numbers only, letters only or alphanumeric. Or a regular expression such as a pattern for a social security number such as xxx-xx-xxxx. Or, probably the best of data validation techniques, is use a database lookup and match an OCR extracted field with a value in a database to keep things consistent within your overall data corpus.
With a creative cloud architecture and leveraging a centralized workflow design, harmonizing your end-point image devices fully supports the integration of any capture software or over 300+ Intelligent Document Processing (IDP) systems. For this reason, adding automatic document classification and metadata extraction to your cloud capture solution provides not only the ‘clean’ data that your LLM requires, but also the added cost justification by greatly reducing the manual labor and inconsistency with using traditional human data entry.
Onramp for multiple data sources, more than just scanners
Using modern connectivity protocols such as TWAIN Direct from the TWAIN Working Group provides the great ability to collect images from other ‘data sources’ more than just document scanners, such as smartphone cameras and existing file share folders.
TWAIN protocol collects data from all input sources, not just scanners
Therefore, you can use a cloud capture solution using TWAIN Direct to easily, securely and effectively collect information from all the data sources within your organization. You can think this solution as sort of an air traffic control system that is a centralized digital workflow capture system that connects devices and data sources to efficiently feed real-time data into your LLM/AI systems.
Seize the opportunity of A.I. by using capture as a strategy to create smart LLM’s
In summary, I hope I have provided some basic considerations that real-time, clean data from any data source helps you, as well as your organization, to seize on the incredibly powerful opportunities with Artificial Intelligence.