Clean & Normalize Your Data Using AI

At Xyonix, we train custom AI systems to recognize and correct errors in your data.

Your data might be wrong. Much of organizational data is entered by human hands, and humans often make mistakes like typos and spelling variations. Data errors often propagate downstream which can lead to operational errors costing a business time and money. Machines can be trained to recognize where there might be errors in data entry, and can help to correct the data. Machines can also be used to prevent incorrect data from being entered in the first place, by, for example, suggesting likely values and prompting humans to make a simpler, less free form decision. Off the shelf solutions can be cumbersome to configure, and often don't take advantage of the structures unique to your data. In addition, privacy, security and other constraints may prevent you from allowing your data into commingled cloud environments.

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Sherri Engvall

“Xyonix’s most impressive characteristic is their passion for helping us solve our problems. They truly believe in what we’re trying to achieve — they’re excited about it. Their members see the value of our work and understand our vision.”

Sherri Engvall, IT Manager @ Delta Dental of Washington [more]

Check out some of our recent projects to get a taste of our capabilities
AI Driven Video Summarizer, A Case Study

AI Driven Video Summarizer

For an extremely rapidly growing startup, we built an AI system that transforms lengthy, often instructional videos into concise, optimized segments. The system cleanses transcripts, then leverages LLM models we fine tuned using training data meticulously annotated by a combination of other LLMs and our human team at Xyonix. It's adept at extracting key topics, generating summaries, easing navigation, and producing short, impactful videos in various formats. Already in public use and benefiting millions daily, this system is a game-changer for educational and instructional content, making complex information more accessible and engaging.

Video Content Creator, A Case Study

Video Content Creator

In collaboration with a rapidly growing startup, we've crafted an AI-driven solution for helping users rapidly create high-quality video content by automatically analyzing user generated movie scripts and identifying optimal video assets for inclusion in short videos. Our role extended beyond data science; we engineered and now host a fully scaled system. This platform excels in generating scene text and keywords, leveraging semantic search within a bespoke imagery index, and creating video metadata from video frames. Designed to facilitate the production of engaging videos and scripts for social media and advertising, this service is robust, fully managed, and caters to millions of users daily.

NLP for Public Health: Vaccine Hesitant Persona Mapper, A Case Study

Vaccine Hesitant Persona Mapper

We were asked by Columbia University to help build a map of vaccine hesitancy to assist public health officials in increasing vaccination rates. We built a corpus of millions of social media messages from platforms like Twitter, Reddit and Youtube comments. We are now analyzing this data by manually annotating thousands of training examples using a multi-parent taxonomy and iteratively (active learning driven) training a multi-label machine learning and NLP powered parser. If we are successful, we hope to save lives by convincing the vaccine hesitant to protect themselves and their communities. [Read more]

Interested in learning more? Check out one of our articles on applications of ai in the real world
Filling in the Gaps: AI-Powered Data Imputation Using Autoencoders
Filling in the Gaps: AI-Powered Data Imputation Using Autoencoders

Missing data can disrupt machine learning workflows, but imputation can help fill in the blanks to keep your models on track. Autoencoders, a type of neural network, excel at reconstructing data by learning complex patterns, outperforming traditional methods like random forest imputation. In our experiment on housing data, autoencoders reduced errors by 3-6 times across features, proving their effectiveness in handling intricate feature relationships. This makes them a powerful tool for imputation and beyond, with applications in denoising, feature extraction, and anomaly detection.

Top 5 Must-Listen ‘Your AI Injection’ Episodes of 2024
Top 5 Must-Listen ‘Your AI Injection’ Episodes of 2024

In this year-end roundup of “Your AI Injection,” we spotlight five episodes that touch on AI’s most pressing ethical and societal questions. Each conversation challenges the notion of what should be built—rather than just how—covering topics from data strategy and personalized tutoring to energy regulation and AI-driven manufacturing. These episodes emphasize AI’s immense potential while underscoring the critical need for transparency, equity, and responsible development.

When Is the Right Time for Your Business to Invest in Custom AI Solutions?
When Is the Right Time for Your Business to Invest in Custom AI Solutions?

80% of AI projects fail—not because the technology isn’t ready, but because businesses aren’t. Companies that thrive with AI begin by identifying clear, high-impact problems it can solve, backed by quality data and a strategic vision for success. This article explores the critical elements of AI readiness: defining your business challenges, ensuring your data infrastructure is robust, and leveraging AI to gain a competitive edge. Whether it’s automating repetitive tasks, personalizing customer experiences, or predicting trends, the key to success isn’t adopting AI early—it’s adopting it smartly. Learn how to assess your readiness and prepare for an AI-powered future.