Developing NLP Tools That Will Amplify Your Business

At Hekate, we have ample experience in developing bespoke natural language processing (NLP) tools and solutions for a broad range of businesses. We understand that each business has unique requirements when it comes to its data, and we are adept at devising NLP strategies that will help you get the most out of your data.

Our team of NLP experts will work with you to understand your needs and develop a tailored solution for your business. We have a proven track record in developing effective NLP solutions for businesses across a range of industries, and we are confident that we can help you achieve your desired results.

What we do?

We have extensive experience developing sentiment analysis tools that can help you understand the emotions and opinions expressed in text data. Understanding the sentiment in data can be used to monitor and analyze customer feedback, as well as to understand social media trends.

We can help you develop document processing solutions that can analyze text, provide semantic search or semantic reasoning, and automate the tedious and time-consuming tasks of manual data entry and processing. Our document processing tools can extract information from various document types, including PDFs, images, and scanned documents.

We can help you develop chatbots and virtual assistants to interact with your customers and provide them with the information they need. Chatbots and virtual assistants can be used to handle customer queries, solve support issues, provide product recommendations, and much more.

We can help you develop text classification and topic modeling solutions that automatically classify and organize your text data. Text classification is a process of automatically assigning texts to one or more predefined categories and can be used to sort emails, articles, customer reviews, and other types of text data.

We can help you develop entity recognition and entity extraction solutions to identify and extract entities from text data. Entity recognition is a process of identifying and classifying named entities in text and can be used for advanced text extraction and text mining tasks.

We can help you develop natural language generation solutions that can automatically generate text from data. Natural language generation is a process of generating texts from data and can be used to create reports, provide text summaries, descriptions, and other types of text data.

Our process

Text Preprocessing

In the first stage, we begin by collecting data from multiple sources and building a raw text corpus. Damaged, irrelevant or incomplete data is eliminated and useful text is normalized and prepared for further analysis.

Text Parsing and Exploratory Data Analysis

In this stage, the raw data is sifted and organized to do a more focused analysis with a smaller dataset. This involves identifying and removing irrelevant sections, extracting coded metadata and determining the format. By selecting the various intents and entities required for the predetermined tasks, a deep exploratory analysis helps establish a format for representation.

Text Representation and Transformation

Now that the datasets are categorized, we use various visualization techniques to represent the data in a meaningful format to retrieve useful insights. This includes a semantic, syntactic and pragmatic analysis of the text to get an overview of the interpretable content.


We now approach the most important Natural Language Processing discipline of modeling artificial neural networks (ANN) and training them to automate the learning of complex linguistic and behavioral models. Text mining at this stage helps to funnel down the data and do targeted information retrieval.

Evaluation and Deployment

At the final stage, the NLP model is tested for performance against a number of training parameters. The metrics are observed and corrective measures are taken where necessary. The successful model is then deployed in the execution environment.