REI Insights

Being Responsive to Citizens’ Needs with AI/ML-Powered Search
March 13, 2023
Image of a man holding a digital magnifying glass with AI in the center

With the recent release of ChatGPT, Artificial Intelligence and Machine Learning (AI/ML) people can’t stop talking about the implications of this emerging technology now crowd-sourced to the entire world. AI/ML technology advances are growing at a rapid pace, and nowhere is that more evident than AI/ML powered search.

AI/ML powered search technologies can be leveraged by government agencies to engage citizens with quality services that build public trust. For example, using this type of technology can save time and money in mining USPTO and the US Courts public databases during a patent lawsuit for similar patents and legal precedents. AI/ML Powered Search can process billions of text pages, images, PDFs, and videos in a fraction of a second. There are more than 2.2 billion textual pages of court materials and millions of patent/trademark documents and images to search. AI/ML powered search can not only ensure better accuracy in search but save enormous amounts of time and resources versus the manual search done today.

Before providing more examples of how AI/ML powered search capabilities are helping agencies, it’s important to understand the technology and its capabilities.

What Is AI/ML-Powered Search?

Search engines powered by AI/ML learn to generate the most relevant and accurate results for the specific user or query. AI teaches a computer or system to think, and ML uses complex algorithms and data to train machines to learn and solve problems. Search engines use the following AI/ML capabilities:

  • Detecting patterns in behavior, text, speech, images, and search parameters to draw conclusions, make suggestions, rank results, and filter out information.
  • Identifying user intent by looking for and identifying input parameters that signal user intent.
  • Analyzing text through natural language processing (NLP) looks for relationships in word combinations and determines intent.
  • Analyzing text in photos or videos and other file formats that lack “readability,” such as text contents that appear as images in some PDFs.

Applications of AI/ML-Powered Search

AI/ML powered search goes beyond searching over the contents retrieved from websites and ranking the results. Here are a few examples of AI/ML powered search features:

Document Search

Traditionally, when users ask a question, the search engine responds with results from indexed data. It will rank the results by predetermined criteria. This is referred to as Basic Conventional Search Processes.

AI/ML powered search takes this a step further by discerning the actual intent of the search and adjusting the results and rankings accordingly. Results can include findings based on synonyms and alternate spellings. AI/ML technologies can examine and detect any personally identifiable information (PII), with the option to remove it to avoid exposing it publicly.

Optical Character Recognition (OCR)

AI/ML equip search engines to “read” text in other formats, such as PDFs, images (JPEG, PNG, etc.), video, or handwritten text images, and convert that content into a machine-readable format, and make it able to be indexed, analyzed, and searched.

Fuzzy Search Logic

Fuzzy logic is a way to determine what might be meant in an imperfect search input, like when a user misspells a word. Fuzzy logic attempts to understand by auto-correcting the text, comparing it to frequent similar searches, or factoring in recent search trends.


AI/ML can also prompt a user with suggestions for search text. When a user begins to type a search query, AI/ML search engines can phrase with suggestions based on dictionaries, trending topics, and other criteria. AI/ML can enable more accurate and NLP for voice search, allowing users to interact with search engines in a more conversational and intuitive manner.

Neural Search

Neural search leverages pre-trained neural networks/deep learning algorithms to expand the learning and response abilities of the AI/ML search engines. It has the ability to analyze and “think,” meaning that it can “read,” interpret, and index unstructured data. Because it is always continuously learning with more data and search results from the past, the search results are more accurate, reflecting user intent and context, and well ranked, thus increasing the output quality and user satisfaction.

Going back to our patent lawsuit use case, neural search will be able to identify “like” patents or trademarks from images with nuanced model training so more accurate information can be brought forth in a case to determine if patents are too similar. This capability can also be used to prevent lawsuits by comparing new applications with current patents or trademarks.

What Are the Benefits of AI/ML Powered Search?

AI/ML powered search improves both search results and the search experience for users. It consumes vast amounts of data, evolves and learns, anticipating a user’s intent and needs, but even more than that, it can now index data that was previously “unreadable,” and deliver to citizens what they need, when they need it, and at an extremely accelerated pace.

In our scenario of a patent/trademark lawsuit, using AI/ML powered search can: 1) speed up the research time and reduce human labor hours, 2) ensure searches are more accurate, smart, and user-centric, 3) search through billions of pieces of content including text, images, and video in seconds, 4) reduce the risk of similar patents getting approved which also minimizes litigations.

Helping Federal Agencies with AI/ML-Powered

REI Systems is helping transform federal agencies’ search capabilities for greater speed, efficiency, insights, and citizen/user experience.

SBIR.gov was difficult for users to search and find critical information. We organized and structured vast amounts of data with Elasticsearch to drastically improve search. Our solution helped users find a given page with 80% success on the first try as opposed to 35.3% on the legacy website.

HHS ACF Shepherd Case Management System was built by REI Systems to replace a manual process with digital services to increase the efficiency of victim identification, expedite victims’ services, and strengthen data management and analytics for anti-trafficking programs. REI indexed, in an AWS cloud-structured database, thousands of case documents (PDF, Word, Excel, and more) for an advanced search capability utilized across numerous federal agencies so victims could receive a spectrum of benefits 50% faster.

If you are looking to offer advanced AI/ML powered search to your agency’s data for greater citizen responsiveness, contact us today at


Munish Satia, Enterprise Architect, REI Systems.

Saravanakumar Ramakrishnan, Senior Technical Director at i-Link Solutions