How We’re Exploring Natural Language Processing at Fyonda with NLP.js

As part of our internal journey into artificial intelligence, we’re diving deeper into a key technology that plays a vital role in making human-machine interactions smoother and more intuitive: Natural Language Processing.

One of the tools we’re currently testing is NLP.js, a JavaScript library designed to streamline natural language processing—without relying on external services.

In this article, we’ll share what we’ve discovered so far, which features we’re experimenting with, and where we’re heading next.

_Why NLP.js Is an Interesting Tool for Natural Language Processing

NLP.js stands out for its lightweight, local-first, and secure approach. Entirely written in JavaScript, it integrates seamlessly into web environments and runs entirely offline, without sending any data to third-party services. This brings several advantages:

• Increased control over data privacy
• High performance in response times
• No additional costs for external APIs

We’re experimenting with several core features offered by the library, all essential to building advanced AI applications that rely on Natural Language Processing.

_Key Features: Language Detection, Levenshtein, and Intent Classification

Here are some of the most valuable features we’re currently testing:

• Automatic language detection: great for multilingual applications, it identifies the language of the input text autonomously.
• Levenshtein algorithm: measures the distance between strings to intelligently manage typos and variations.
• Intent classification: categorizes sentences by meaning, such as identifying a greeting or a request for information.

Together, these features enable the creation of smarter, more responsive conversational interfaces, paving the way for increasingly sophisticated Natural Language Processing use cases.

_Intents and Entities: The Core of Natural Language Understanding

At Fyonda, we’re focusing on two fundamental concepts for building chatbots and interactive systems:

• Intents: represent the goal or purpose behind a user’s sentence (e.g., asking for the time, saying hello, making a reservation).
• Entities: the specific information contained in a sentence (e.g., a location, a date, an object).

Combining intents and entities allows systems to generate more complex and personalized responses, significantly improving the quality of interactions.

_Practical Experiments: Greetings, Reservations, and Smart Replies

Through a series of tests, we’ve demonstrated how NLP.js can:

• Detect greetings and respond coherently
• Handle linguistic variations through intent classification
• Extract relevant entities from complex sentences (e.g., flight bookings)
• Evaluate the confidence level in each response—useful for improving accuracy

What excites us most is the direct control we have over the model’s behavior, giving us the ability to fine-tune responses and better meet real user needs.

_Toward Real-World Applications: Custom AI and Smart Systems

Our goal is to turn these experiments into real solutions, starting with:

• Intelligent auto-responses: systems that can understand and answer common questions without human intervention.
• Pre-processing for advanced models: using NLP.js as a foundational layer for more complex systems, including future integration with models like those from OpenAI.

These use cases are already in active exploration and form part of our ongoing journey of internal innovation and learning.

_Conclusion

Natural Language Processing is becoming an increasingly central part of our experimentation, and tools like NLP.js allow us to explore its potential in a practical, flexible, and secure way. We’re working to integrate these capabilities into our future projects, aiming to make every interaction more natural and personalized.