Data Extraction and Document Parsing Software

At BroutonLab, we worked on several projects where we applied machine learning and computer vision techniques to develop smart monitoring software for solar panels.

computer vision

So why is it important for you?

Data extraction and parsing software use AI-powered NLP (Natural language processing) technology to analyze, understand, extract useful information, and act on it through natural human language input. NLP breaks down unstructured data into elemental pieces and obtains the information required to understand the relationship between the parts of data.


To get the most out of NLP, we use artificial intelligence and machine learning to understand the complexity of human language and offer appropriate actions.

Data Analytics and Data Science Use Cases

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Benefits of NLP and AI-Powered Parsing for Business

Get actionable insights. Gather the information you need automatically. Make better business decisions based on structured data that provides business intelligence. Boost revenue and improve business processes.

Save time and money. With AI-powered NLP, any user, regardless of technical skill, can perform complex queries. Collect, explore, and derive meaning from big data with the minimum efforts and resources.

Leverage competitor research. Automate competitor price change study and get the latest market information without having to spend hours hunting it down. Stay ahead of the market trends and grow your market share.

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Example of Facts &
Data Extraction
Software

Data Scientists at Broutonlab created a system that extracts texts from entities of different types. Such as artist names, style of artist, locations, and dates. It analyzes the pieces data and understands the relationship between them.

Problem

Automate extraction of structured data from unstructured text to save time and resources on systemizing and classifying the data.

Solution

We developed a neural network and augmented it with NLP to process the input text and classify every word (a NER system).
The training dataset was small, so we used unsupervised learning (e.g. Word2Vec) to achieve accurate results.