With an IDSS using language technologies on Amazon Web Services (AWS), companies can easily find relevant articles, industry reports, and other valuable information to help you make informed business decisions.
Crayon developed an IDSS tool for Savills Vietnam, enabling the leadership team to stay up-to-date on the latest real estate trends and opportunities in the region. Savills is a global real estate services company with offices around the world. As a real estate specialist, staying current is of paramount importance since information can help businesses identify new opportunities.
“As an organization that needs to sift through a lot of information from different sources in different languages, it was clear that having an intelligent decision support system was going to help my staff increase productivity.“
Matthew Powell, Director of Savills Hanoi
Crayon is an AWS Premier Tier Services Partner and AWS Marketplace Seller with Competencies in Machine Learning, DevOps, and other key areas. Crayon is a customer-centric innovation and IT services company that provides guidance on clients’ business needs and budgets with software, cloud, artificial intelligence (AI), and big data.
Components of an intelligent search engine
An IDSS tool is based on an intelligent search engine (ISE), which ISE offers natural language and semantic understanding of queries, going beyond simple keyword-based searching. Questions can be asked in natural language, and results can be more relevant to the user. Figure 1 shows a high-level diagram of an IDSS based on an intelligent search engine.
A high-level diagram of an IDSSFigure 1 – High-level diagram of an IDSS.
Data is ingested into the system by crawling various sources, such as news agencies, blogs, portals, reports, and social media. The ingested unstructured text is pre-processed and enriched to extract important information that assists the answering process later. This can include classifying documents into different categories and extracting relevant entities or metadata.
The enriched documents are then indexed into the ISE, and users can ask questions in natural language and receive direct answers or relevant excerpts that are related to their questions. Users can also provide feedback on the relevance of the answers (via thumbs up/down, for example) and allows the ISE to improve over time.
Solution requirements and approach
Following a similar approach to Figure 1, the Savills team needed to ingest daily CommSights news articles, in Vietnamese, covering different categories, including commercial leasing, industrial, residential, and disaster news.
The team needed to differentiate between categories and required a filtering mechanism that would enable users to quickly narrow down their search to any of these topics. They also required the search to be conducted using natural language to quickly and seamlessly provide the necessary insights to users.
To address these requirements, Crayon opted to base its IDSS on Amazon Kendra, an intelligent search service that uses natural language processing (NLP) and advanced machine learning (ML) algorithms to return specific answers to search questions from data. Unlike traditional keyword-based search, Amazon Kendra uses its semantic and contextual understanding capabilities to decide whether a document is relevant to a search query. It returns specific answers to questions, giving users an experience that’s close to interacting with a human expert.