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CONSTRUCTION |
AI SERVICE
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Instant Access to Information Needed to Optimize Construction Bids
Discover how Synaptiq helped a client extract and leverage knowledge accumulated through thousands of past projects and locked away in unstructured documents.
Problem:
Our client, a construction company, had accumulated knowledge over the course of thousands of projects. This knowledge was locked away in reports, training materials, and other unstructured documents, making it challenging for employees to leverage for decision-making support. To solve this problem, our client needed to transform their past project documents into a queryable knowledge base.
How can AI in Construction spark ideas for your business?
Many organizations can't take advantage of accumulated internal knowledge because it's hidden away in unstructured data sources such as documents, images, and audio recordings. As a result, valuable insights remain untapped, limiting employee’s ability to make informed decisions. Technologies like natural language processing and large language models present an opportunity to transform unstructured data into a structured database of knowledge. By harnessing these tools, organizations can learn from the past to improve in the present.
Solution:
Synaptiq conducted a two-stage, proof-of-concept project that transformed a large volume of our client’s unstructured documents into a queryable knowledge base. In the first stage, we used a branch of AI called natural language processing to extract units of knowledge, called "entities," from our client’s documents. Our client’s domain experts labeled a small portion of these entities, which we used to train classifiers to label the remaining entities.
In the second stage, we developed statistical models to find connections between entities. Using these models, we built a "knowledge graph," which is a database of knowledge organized according to the connections between entities in a web-like graph structure.
Our client's employees could then use this knowledge graph to search for specific knowledge related to problems, industries, or other categories of information.
Outcome:
We validated the concept of using natural language processing to transform our client’s unstructured documents into a centralized database of knowledge. We delivered a queryable knowledge graph, which allowed our client’s employees to support their decision-making with wisdom acquired from thousands of past projects.
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