In this post you'll learn:
  • How Natural Language Processing (NLP) is being used across industries (it's not just chatbots)
  • 3 real-life examples of companies that worked with Tribe to leverage NLP and automate manual processes to improve accuracy, team efficiency, and customer experience.

Natural Language Processing (NLP) is a branch of artificial intelligence that allows computers to understand, analyze, and generate human speech and text. Public awareness of NLP has been fueled by models like ChatGPT, but its potential applications go far beyond chatbots. 

With NLP, companies can automate repetitive tasks, gain insights faster and with more accuracy, and improve communication within their teams. In this post, we'll delve into some of the ways organizations can leverage the power of NLP to enhance their operations and gain a competitive advantage– including real life examples from Tribe customers. 

An overview of NLP use cases

While many people are familiar with the use of NLP in chatbots and customer service in particular, applications of this technology are used across industries to streamline a vast array of text-based operations. These applications include:

  • Text classification – Assigning predefined categories or labels to large amounts of text data. This can be used in a variety of scenarios, such as automated transaction categorization for FinTech company
  • Named entity recognition (NER) – This involves identifying and extracting specific elements such as names, organizations, locations, and other entities from unstructured text data. This can be used for tasks such as analysis of a large volume of contracts for a law firm. 
  • Sentiment analysis – Automatically determining the sentiment or emotion expressed in a piece of text. This could be used by a company looking to better understand the opinions of a particular market to adjust their marketing strategy accordingly. 
  • Text summarization – Reducing a long document or piece of text to its most important points. This can be used to generate concise summaries and surface key themes from customer feedback. 

Tribe has worked with a number of companies to incorporate NLP into a live business environment. One area where we’ve seen this technology really drive impact is by automating manual processes that use text data. This allows companies to: 

  • Improve accuracy and consistency
  • Increase worker efficiency and productivity
  • Free up resources to higher value areas of the businesses 
  • Have a significant advantage over competition

Here are three examples of companies that have worked with Tribe to automate manual processes using NLP.

Automated the detection and categorization of business transactions

A cloud-based financial platform that erases all the worst parts of freelancing - taxes, benefits, accounting, invoice and expense tracking

The company was unable to automate the detection of business expenses and categorize their customers’ transactions, significantly reducing the usefulness of their product.

In one month, Tribe built a PoC of a predictive model to classify transactions into corresponding  categories with a baseline accuracy high enough to green-light further development. The performance of the PoC model exceeded client expectations and made a strong business case for ongoing investment in machine learning (ML).

Automated a security compliance company’s due diligence process

A growth stage security compliance company that wanted to move away from a manual survey data process.

The company was overly reliant on a manual, human-centric process to collect security survey data. This caused bottlenecks and inefficiencies.

Tribe built a Question-Matching model that automated large parts of their diligence process. The model was adopted by internal teams to drive greater team efficiency. Model increased team efficiency by 55%. As part of this project, Tribe also developed a strategic technical roadmap so the company could continue to drive additional model gains in the future. Read more about the project here.

Built a PoC model to serve as a foundation of fully automated expense categorization

A software company that provides financial solutions designed for self-employed business owners.

The company set out to automate a highly manual process: tagging transactions as personal or business-related expenses. However, they did not have the technical expertise to build and implement a customized ML architecture and model.

Tribe built, trained, and deployed a classifier predictive model. The team also built the data and ML Ops infrastructure to support and optimize ongoing training and productionisation. You can read a deep dive on this project here.

NLP as a game changer

At Tribe, we’ve seen how automating manual processes can be a game-changer for companies looking to improve accuracy, increase efficiency, and stay ahead of the competition. These are just a few examples of companies we’ve seen streamline their operations and free up resources for more valuable areas of their business.

And NLP is not just a tool for startups and tech companies. Legacy companies or organizations without a technical team can benefit hugely from this technology. Any business that has text based data can use NLP to streamline their operations and remain competitive in the ever-evolving business landscape.

Written with support from OpenAI ChatGPT
All images generated by Midjourney