Innverse helps private and public natural language processing leaders make better, more affordable, and more accessible for millions of people around the world.

Unlock the value of your
data with Natural language processing software

There are lots of unknowns and risks when it comes to machine learning. And you might not always have enough talent in-house to successfully execute your AI project.

We delivered more than 100 custom AI solutions in 20 countries worldwide and developed the AI strategy for the Estonian government, so we have enough competence to support you.

Stop Processing Speech or Text Manually services

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.

At Innverse, we create natural language processing solutions to help unlock the value of your data, automate routine tasks, and make data-driven decisions.

We provide natural language processing solution

Data labeling

Data Labelling involves adding tags or annotations for machine learning.

Data architecture

Data architecture designs the structure and flow of information systems.

AI strategy

AI strategy outlines plans for implementing artificial intelligence initiatives effectively.

Piloting

Piloting involves testing small-scale implementations to assess feasibility and effectiveness.

Scaling

Scaling refers to expanding and optimising systems or operations for growth.

NLOps

Natural Language Processing, involves analysing and understanding language data.

Data collection

Data collection entails gathering information for analysis, often from diverse sources.

App development

App development involves creating software applications for various platforms and devices.

It means that we are with
you from the start to the
finish of the final solution

From validating ideas on the business side to creating a strategy that is based on them. Making sure everything is ready from the data side from quality, quantity, engineering and scalability.

We set up all the necessary MLOps infrastructure for initial pilots and scale successful pilots. Of course, we develop the actual AI models producing the desired output and the supporting applications to exploit the output of those models.

AI for business: Reinvent what's
possible

AI is accelerating into a mega-trend, transforming industries, companies and the way we live and work. Organizations that build a strong data & AI foundation will be better positioned to reinvent, compete and achieve new levels of performance.

Accenture helps companies move from AI interest to action to value, in a responsible way with clear business cases. We help companies get their data, people and processes ready for AI, with a secure, cloud-based digital core that allows for continuous reinvention and greater growth, efficiency and resilience.

Artificial Intelligence

A new era of generative AI for everyone

The technology underpinning ChatGPT will transform work and reinvent business.

Natural language understanding

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.

Sentiment analysis

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.

Speech recognition

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.

Speech synthesis

Speaker identification

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.

However, Speaker identification 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.

Emotion detection

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.

Our projects

Language learning platform using NLP

A conversational interface with its own voice, Annika takes calls from clients, listens to what they have to say, and directs them to the best course of action. This is done using multilingual speech recognition to translate speech to words, transformer based NLP models to understand the content of a customer’s sentence and non-autoregressive Transformer based text to speech models that provide Annika with her signature voice.

Chatbot application leveraging NLP

Building a job search and career development platform requires quite a bit of data collection - user profiles, job descriptions, cover letters, etc. We designed a system that takes user provided documents - CV-s, cover letters, job and education descriptions, etc. - as input and, using transformer based language models, extracts the relevant information that fits the data model of the platform.

Sentiment analysis tool employing NLP

The machine learning system learns from expert auditors and helps to detect potential tax fraud. This solution is deployed at the Estonian Tax and Customs Board of Estonia.

Summarisation tool using NLP

Our system helps power line utilities to inspect their power grids with the help of drones to capture data and a specialized inspection platform called uBird to analyze it.

We speed up your learning curve

The Innverse team works with machine learning projects all the time, and has highly qualified specialists on board; thus, we can start implementing the project right away, not spending time learning how to be more efficient.

We already know who is good at what and who to include to efficiently deliver your project. This might mean including a specific person with a particular skill set for 1 day or 6 months.

Unlocking the potential of AI

Since our inception, we’ve successfully built a reputation of trust, reliability and of delivering exceptional services. We are progressively diversifying into new markets with our battle-hardened methodologies. Every day, we work to empower our customers to get the maximum out of technology.

We challenge, we innovate, and we continue to deepen our knowledge and expertise to realize the best value for our customers. We do this through a culture that cultivates a relationship-based approach to helping people and businesses be successful.

Waseem Ahmad

Frequently ask question

Discovering the right process to be enhanced with AI may be an unusual task for business people.

People should come to us when they have a business problem where they intuitively feel that the solution could be hidden in data and that it can not be solved by writing a couple of simple rules. If this is the case, AI might be the solution.

To actually define an AI use case, business leaders will need the help of an AI team, who judge if and how to proceed with the problem enhancement; the problem owner who knows the most about the issue; and technical specialists who understand how the problem described by the problem owner can be interacted with in the technical world.

Data collection is most often on the client side as it is connected to the peculiar business problem to be solved we help as much as we can, especially if it is a data source we have worked with before.

Labeling can be on the client side if very specific knowledge is required for labeling or it can be outsourced to a labeling company or us. Hybrid solutions are also available, and in all cases, we provide proper labeling training and guidelines tailored to the computer vision task.

If you imagine the process made up of iterations of the cycle: data collection, labeling, model development + training, testing & evaluation and deployment into the final solution, usually the first 2 steps take 20-50% of the time (the smaller the project / standard problem, the higher the percentage) while the split between the latter 3 is really dependent of the novelty of the problem being solved, the required performance level and the complexity of deployment. The AI project range is from 3 months to multi-year collaborations.

During the project evaluation, we provide a feasibility assessment, and for problems in which we have experience, we can provide a more precise prediction and discuss the forecasted minimum level of performance.

How can we help you?

Are you ready to push boundaries and explore new frontiers of innovation?

Let's Talk

Why contact us?

Validate your situation with our business advisors and IT executives
Understand business results, not just technical implications
Discuss possible solutions
Achieve a better knowledge of the best choices
Get a cost estimate, no obligation

Natural language processing

Natural language processing analyses and understands human language for various applications.

Unlock the value of your data with Natural language processing software