Despite ongoing digital transformations, many organizations still manually process diverse document types. Extracting information is error-prone and costly, hindering scalability. Artificial Intelligence, particularly through intelligent document processing (IDP), addresses these challenges by automating data extraction from unstructured documents, significantly enhancing accuracy and efficiency. Shreekant Mandvikar an expert IPA Researcher, who has authored an insightful article titled Augmenting Intelligent Document Processing (IDP) Workflows with Contemporary Large Language Models (LLMs) informs that IDP comes under the umbrella of IPA. His article has attracted more than 600+ reads and been cited in 8 research papers.
Shreekant states, “AI-powered IDP extracts and validates crucial data from documents, using machine learning, deep learning, OCR, and NLP. Integrating Large Language Models in IDP enhances workflow, aiding in managing, analyzing, and extracting value from diverse documents.”
Shreekant’s insights hold significant depth and importance, as he is acknowledged as a reputable professional in his domain with more than 14+ year of experience in IPA domain, and holds affiliations as a distinguished member of the Intelligent Automation Congress and IEEE. Furthermore, he serves on the Review Panel Board of the International Journal of Science and Research.
He has conducted substantial research and co-authored a highly esteemed journal titled “Process Automation 2.0 with Generative AI Framework” in the International Journal of Science and Research (IJSR), alongside another luminary in the field of data science and engineering, Alekhya Achanta.
Shreekant has made significant contributions to academic research, with notable publications such as “Augmenting Intelligent Document Processing (IDP) Workflows with Contemporary Large Language Models (LLMs)”, which explores the integration of LLMs into IDP workflows. Additionally, his work includes “Process Automation 2.0 with Generative AI Framework”, presenting an advanced AI framework for improving organizational efficiency.
His one more publication, “Factors to Consider When Selecting a Large Language Model: A Comparative Analysis”, delves into the key considerations in choosing LLMs for organizational use. Lastly, his research covers “Indexing Robotic Process Automation Products”, discussing criteria for selecting optimal RPA products based on objective data and testing.
For his work and continuous contribution to Intelligent Process Automation(IPA) field, Shreekant is being honoured with “Golden Pinnacle Award” on 24th November 2023 by Achievers’ World at the prestigious Eurasian Business Summit. This event took place in the House of Commons in London, UK, with Mr. Virendra Sharma, Honourable Member of Parliament, UK, as the Chief Guest.
Driven and passionate about his profession, Shreekant’s commitment is evident through his mentoring of over 120+ associates in automation. He is now “Top 50 mentors in AI/ML Engineering” at ADPList. He is a sought-after participant in industry-specific podcasts conducted by Bot Nirvana, IRRPAAI, and AAITP. Additionally, Shreekant is an invited reviewer panel at “International Journal of Science and Research (IJSR) “, actively contributes as a judge in hackathons such as hackHARVARD and hackMIT.For his continuous contributions, he is being invited by threws.com as a Fellow Member.
Explaining more about Intelligent Document Processing (IDP), he mentions, “LLMs make IDP better by understanding the meaning of documents, making it easier to classify them without bias or technical problems. They also improve accuracy and allow information to be checked across different sources. During the review process, LLMs give instant suggestions, and in the enrichment phase, they add value by analyzing the context. When it comes to integrating data, LLMs make sure everything is consistent and even create automated summaries, making the whole IDP process more efficient.”
He informs about a few advantages of LLMs in IDP, “ Large Language Models (LLMs) efficiently extract crucial information from diverse documents, including unstructured text and images with text. Their multilingual capability streamlines document analysis for global businesses by eliminating the need for separate language models.”
He continues, “Apart from finding specific words, these language models (LLMs) also understand the context, which helps them do better in tasks like figuring out the feelings in a text. They give reliable results, minimize mistakes made by people, and can handle a large number of documents easily. LLMs also reveal important information and patterns in documents, helping businesses make better decisions based on data and improve their marketing plans.”
Shreekant sums up by explaining a few benefits of IDP stating “Through IDP, AI significantly enhances data extraction from diverse documents, automating conversion into structured data with high accuracy. This extends to automating validation and filing of data from various documents like expenses, insurance, accounting, and employee onboarding, handling different formats seamlessly. AI-powered document processing revolutionizes how organizations handle digital documents, offering novel ways to improve business practices.”
