We provide NLP services and build turnkey AI models that help businesses use textual data to make smarter decisions more quickly.
One of the key benefits provided by machine learning is the ability to process unstructured data, such as speech and text, to extract facts and ideas without relying on inaccurate keyword searches or laborious audio recording processing.
we provide our clients with custom AI and machine learning solutions to help them unlock the value of their data, automate routine tasks, and make data-driven decisions.
While it is easy for us to read a sentence or a paragraph and figure out whether it’s about ordering pizza, the weather, or the news, doing so on a large scale can be very time-consuming.
AI-based natural language understanding (NLU) solutions allow machines to perform these tasks at a speed that is unmatched by humans. This allows us to classify entire documents, divide them into sections by topic, understand the user’s intent in a conversation, or extract pieces of information from long texts.
These features serve as the foundation for conversational interfaces and allow for use cases like topic-based news article filtering, information extraction from CVs, and spam email detection.
It takes a lot of effort to determine what a text document is truly about, even when it is well-organized and searchable, and the majority of longer texts don’t come with a convenient TL;DR or summary. Fortunately, AI is rapidly improving at understanding the key ideas the author was attempting to convey and at generating a summary of the text itself.
The classification of the text by topic allows us to quickly search through the documents, while summarization allows us to quickly grasp the information contained in the text. Everyone who has to deal with large volumes of text, from analysts to executives, will benefit greatly from this.
Languages are very nuanced, so while some of the information is factual, like a judgement that the restaurant’s food was bad, other parts are emotive, like the man’s outrage about the food’s quality.
Machines are generally not very good at reading emotions, but with the aid of AI, they can pick up cues that indicate the sentiment attached to a segment of text, showing whether the content is happy, sad, or evokes any other sentiment the model is trained to recognize.
This allows our clients to better understand their customer feedback, adjust chatbot responses, and react to prevailing sentiments regarding specific topics reflected in the comments. It has also been used to understand sentiment concerning specific topics in the media and on social media. In this case, NLU and sentiment analysis are used in tandem to recognize topics of interest and subsequently interpret sentiments about them.
When communicating information, people typically speak rather than write. This is especially true for certain forms of communication, such as meetings, hearings, calls, etc. Some of these can be processed as raw audio, but for best results, speech must be converted to text to facilitate navigation, analysis, and post-processing using NLP methods.
we’ve spent years developing speech recognition solutions for both widely spoken languages like English and lesser-known ones like Estonian. We also specialize in working with compromised audio, such as phone calls and noisy surroundings or phone calls made in noisy surroundings.
We help our clients extract information from their speech data, either by using our existing speech recognition solutions or by developing custom models for their specific use cases, such as a specific recording environment, domain-specific vocabulary, regional accents, etc.
Speech recognition allows a machine to understand human speech, but speech synthesis is what is needed to help the machine respond in kind. Machine learning has advanced significantly in the synthesis of natural-sounding voices in recent years.
With just a few hours of training data, we create speech synthesis models that generate natural-sounding speech using custom voices. These models help our clients automate customer calls and provide prompt and personalized messages to users, for example, in the event of a service outage.
However, speech synthesis has a wider range of applications and is actively used to create audio versions of written articles and even audio books. Although the quality isn’t quite as good as when it’s read by someone with a deeper understanding of the material and a wider cultural context, it’s still a useful tool.
Knowing who is speaking is often as crucial as understanding their message. This can be very useful for meeting memos, automatic subtitling, customer identification, and even for law enforcement needs.
Speaker recognition is one area where machines very often outperform humans, not only in recognition accuracy but also in the number of people these systems can accurately identify. As with humans, this is, of course, strongly affected by the quality of the sound. For example, speaker recognition in a phone call is inherently less accurate than in a recording of a meeting.
Emotions can be determined from audio in a similar fashion to sentiment analysis in text. This is even more important here since speech recognition itself is imperfect, and people focus less on the tone of words when voice can be used.
This is particularly important when speaking with customers over the phone since it helps to understand how strongly they feel about the reason for the call, or when contacting emergency services because the emotion in the caller’s voice can indicate the urgency of the call.
Working closely with your company, we will identify the key areas where AI can bring the most value. This involves meetings with all stakeholders and developing a roadmap for action together.
We will evaluate if you have the business data needed to train relevant machine learning models. If required, we identify additional frameworks for data collection in your company.
Based on our meetings and data analysis, we’ll share with you the possible AI use cases for your company. We will work hand-in-hand to agree on the desired outcome.
We will build and apply various machine learning models to your business data, to find the best solution. As a result, we will develop algorithms that accomplish the desired goal.
We integrate the machine learning model with an API or front-end product, making it user-friendly and accessible to the end-user.
Any system might require time to time maintenance, and we are happy to support our customers with that.
It depends on what “understanding” implies, so it’s a tricky question, but the overall response would be no, not really. NLP techniques solve specific problems, such as classifying text into predetermined categories, or converting text into sound or vice versa, but this does not mean that computers actually understand the language itself.
Typically, data scientists, machine learning engineers, or both, are involved. NLP is a rapidly developing field where cutting-edge tools are continually being created. This means that using these new tools and techniques frequently necessitates a solid understanding of both data science and the application of these models to specific problems.
This depends on the problem being solved and the data that NLP is applied to. If the problem is fairly simple and the data is text, language-agnostic methods can be used, and there is no language dependency. For more complex problems, there is some dependency even when working with text, because pre-trained models are necessary, but there are resources available for the majority of languages. Audio is particularly complicated because there aren’t many resources to fall back on, and the language dependency is the strongest.
During the project evaluation, we provide a feasibility assessment and, for problems in which we have experience, we can provide a more precise prediction at the level of the computer vision metrics and discuss the forecasted minimum level of performance.
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