Natural language is the most natural (pun intended) way to store and share information for humans. Software solutions that can understand, analyze and even use it for communication are becoming the key to success in many industries, with the recent rise of Large Language Models (LLMs) and AI chatbots being yet another proof of this. Stick with us and see how It-Jim’s expertise in Natural Language Processing (NLP) can boost your business to the next level and bring to life your most daring project ideas.
Data Analysis
Analyzing data and making correct conclusions is an extremely valuable decision-making tool for both well-established companies and emerging startups. It can also be a successful product on its own, providing users with a concise summary of what they were looking for just in one click.
Yet, the data one might be interested in doesn’t always come in nice structured tables. A corpus of unstructured text might have different origins: various documents, e-mails, product descriptions or reviews, and so on, but for all of them, we’ve got all the tools needed to extract the information you’re actually looking for.
Our expertise in Information Extraction from text includes
- Named Entity Recognition
- Sentiment Analysis
- Topic Modelling
- Building Knowledge Graphs
- Text Summarization
- Question Answering.
We utilize major NLP libraries (SpaCy, NLTK, Gensim) as well as Deep Learning models (BERT, RoBERTa, BART, T5). For our DL solutions, we use both PyTorch and TensorFlow and of course, as NLP enthusiasts, we couldn’t have missed the HuggingFace Transformers framework.
One of our core competencies is fine-tuning these models for particular tasks, with all techniques of efficient data engineering and model training being at our disposal. If there is not enough data to train on, we offer one- and few-shot solutions that require only a couple of examples to learn how to complete certain tasks.
Content Сreation
Working with texts isn’t limited to analyzing them; there is also great potential in automatically generating new texts for your needs. From creative and persuasive ads based on a list of keywords to a touching personalized letter given just a couple of sentences – all that is perfectly achievable with proper NLP tools and corresponding expertise. Depending on how many examples are available, we can offer either fine-tuning a language model or using it with just a few examples (or even without them at all) through careful prompt engineering. This is achievable with open-source models like T5, as well as with GPT 3&4 – all depending on what suits your project best.
We also don’t restrict ourselves to generating just text. Modern text-to-image models like DALL-E and StableDiffusion are capable of creating wonderful art, limited mostly by how well one designs a text prompt for it. But if you want to bring their image creation capabilities to your users without making them go through a crash course on prompt engineering, we are here to help. By applying proper NLP techniques, we can turn an unstructured heap of ideas or even just an arbitrary piece of text into a well-designed prompt that will make generated images surpass your expectation. To see how this works in practice, check out our project for generating illustrations for poems.
Conversational AI
Systems that can keep up a conversation with a user come in different forms: customer support chatbots, AI assistants, educational roleplay solutions, and many more. If you’re looking to build a similar system of your own, we’ve got you covered.
Customer support is probably the most common example of conversational AI right now. Building a proper customer support chatbot requires forming a good understanding of the domain and analyzing typical scenarios that need to be automated. We always take a close look at the historical record of customer inquiries to uncover common patterns and obtain necessary data for training the chatbot. We then design a conversation flow to be clear and unambiguous for a user, ensuring that the chatbot would really be a helpful component rather than just an annoying step before getting to a human operator. We use dedicated chatbot frameworks (DialogFlow and Rasa), which allow for rapid development and easy integration with all major messengers and platforms.
For tasks that require more human-like conversations, we use LLM-based chatbots, namely ChatGPT, as well as solutions built on top of it, like AutoGPT and LangChain. Proper application of these tools allows us to provide users with a much more personalized and engaging conversation experience, which is simply impossible to achieve with classic approaches to building chatbots.
NLP on the Edge
Cutting-edge LLMs that run on extremely powerful servers are incredible, yet there are kinds of data that users don’t want to leave their device at all, let alone to be sent to a third-party API for processing. Understanding this challenge, we’re constantly improving our expertise in deploying AI solutions on edge devices (be it a smartphone or embedded systems). Our engineers have a unique skill set for solving any compatibility issues and converting Deep Learning models, including LLMs, to CoreML, TensorFlow Lite, and TensorRT. Through techniques like knowledge distillation, quantization, and pruning, we make sure that the performance of our Deep Learning solutions on mobile devices meets the highest expectations of our customers.
Yet, it is always better to solve problems before they happen. While it is common nowadays to solve any complicated task just by plugging in more and more capable (and heavier) LLMs, we leverage our long experience in NLP to achieve the same quality of results with classic algorithmic and ML solutions or by using much lighter DL models. We also optimize our code specifically for the target platform (including both iOS and Android), ensuring that our solutions always run as fast as possible on the given hardware.
