Our Technology
A combination of state-of-the-art models, transfer learning, and finely-tuned logic to deliver superior results.
Built for the Modern Data Stack
A cloud-native data pre-processing tool for unstructured data
The platform is designed to help data professionals leverage AI and Machine Learning in their data processing workflows. The platform enables data teams to go beyond simple transformations, and use intelligent algorithms to structure data.
Zero Training, Relevant Results
A powerful unsupervised learning solution
Data teams can spend weeks creating and customizing text analysis algorithms. This time is pulled away from more relevant tasks, such as building use-case-specific models that deliver higher value to the business.
The NovaceneAI Platform provides a fully unsupervised learning experience. Unlike other solutions that require you to customize algorithms from one project to another, our platform uses a combination of state-of-the-art and proprietary algorithms that provide relevant results automatically without the need to train models, regardless of the types of data you are processing.
We continuously invest in R&D, publish original research, and constantly improve the accuracy of our technology. We deploy new algorithms on an ongoing basis, offering you an ever growing number of solutions. Learn more about our multilingual open-ended survey responses analysis algorithms.
We support events that promote the advancement of disruptive technologies. Read about our latest event.
Under the Hood
Advanced technology, finely tuned.
AI research, and particularly Natural Language Processing (NLP) has seen massive acceleration in the last few years thanks to the availability of increased computing power, generation of massive amounts of data, and academic focus. As a result, there has been a flurry of new and incredibly powerful AI being released. Since Google introduced BERT, a language understanding model in 2018, and since then, the introduction of GPT-3 and others, gave way to the creation of powerful tools that brought AI to the masses. These AI models are being referred to as foundation models because of their adaptability to solve many problems, including problems for which they were not initially trained to solve. Even if these foundation models don’t perfectly solve a specific problems, they can be trained further to acquire the skills to solve very specific problems. This fine-tuning technique is known as transfer learning.
But foundation models and transfer learning isn’t enough. Effective AI requires careful fine tuning to ensure it performs well across different datasets. This is where the NovaceneAI Platform™ comes in. At Novacene, we leverage foundation models, transfer learning and perform careful tuning to ensure our technology works great regardless of the data. This combination of approaches led to the creation of peer-reviewed AI technology that performs better than competing solutions.
Technology
Solid technology backed by R&D
Peer-reviewed research
We work with clients and partners to advance the state-of-the-art in AI.
Read our latest peer-reviewed scientific paper.
Proud supporter of deep technology events
We support events that promote the advancement of disruptive technologies.
Read about our latest event.
R&D partners
Powerful Functionality
Right out of the box
| Enricher | Description |
|---|---|
| Cluster | Clusters text by grouping similar content together into categories. |
| Cluster Label | Outputs the single most representative sample in a cluster, providing an understanding of the topic of the samples in the cluster. |
| Cluster Sampler | Outputs a set of the topmost representative samples in a cluster. |
| Cluster Summary | Outputs a few samples formatted as a paragraph, providing context as to what the samples in the cluster are about. |
| HTML Cleanser | Removes HTML tags from text. |
| Image Quality Scoring | Assigns a score based on image quality factors such as contrast, sharpness and more. |
| Keyword Extractor | Outputs the top keywords or important terms found in a cluster of samples. |
| Language Translator | Detects non-English text and translates into English. (Supports French, Spanish, and Chinese). |
| Named Entity Recognition | Detects and highlight people, places, organizations, and other known entities found in text. |
| Paraphraser | Generates a short description of the contents of each cluster. |
| Peer Clustering | Clusters records that share many similar attributes. |
| Public Support Detector | Assigns one of five classes on a 5-point Likert Scale ranging from strongly approve to strongly disapprove. |
| Sentiment Analysis | Classifies the tone of the text as being positive or negative. |
| Social Media Content Cleanser | Removes special characters commonly used in social media updates such as hashtags, @mentions, and more. |
| Term Frequency | Identifies and counts the number of times a unigram or noun-phrase appears in an input text. |
| Text Summary | Summarizes text. |
| Theme Generator | Outputs key phrases that describe the contents of a cluster. |
| Threats / Abuse Detector | Detects threatening or abusive language. |
Market research data firm automates the curation of records
A market research data company reduced manual data processing workload by 70%
The company was able to:
- Categorize thousands of records in minutes
- Automate the processing of newly acquired records
- Integrate the AI into their existing data pipeline
