Machine Learning for the Masses

Our mission is to make it incredibly easy for non-technical users to access AI & ML, and make it much more cost efficient for enterprises to do the same… We learned some important things along the way, which is, for non-technical analysts and sometimes even developers, you have to abstract an entire complex AI data engineering workflow and make it point and click… you have to do all the things required inside that workflow, and do them really well, like ingest, ETL, data preparation, services orchestration… and then most importantly make the results consumable. Grounded in best practices and practical implementation strategies, yet steeped in academic driven innovations, NNData continues to be a leader in providing greater clarity to ever increasing volume and stores of data.

NNData engineers are well-versed in enterprise data solutions and have assisted the Federal government and commercial organizations implement new data technologies and methods for over 20 years.


Unstructured Data Management

There’s plenty of tools and services on the market for data preparation and management of structured data, but not really anything in the way of true unstructured data. Turns out it’s really, really hard. NNData has been solving difficult unstructured data management problem sets since day 1 - focusing on the application of machine learning and orchestration of internal and external cloud ML tools like AWS Comprehend and Textract.


AI/ML Orchestration

ML is complex, difficult to execute, and the data has to be prepped in a certain way (especially unstructured data). Then the resulting output has to be interpreted by developers and SME’s. This is definitely true for open source AI, as well as new ML tools from say AWS (like Comprehend, Comprehend Medical, Sagemaker, Textract, etc). We built our software, NNCompass, to be the glue between customers data sources and cloud ML tools, so we negate the need to set up complex data pipelines, Lambda functions, S3 accounts, S3 buckets, have a separate ingest, ETL, and preparation tool, or have to work with command line interfaces. All of this is a non-starter for a lot of organizations.


Making the Output of AI/ML Consumable & Actionable

When organizations attempt to utilize AI & ML against a difficult business process or question that needs to be answered, they spend a lot of time making the output of ML, often JSON with millions of tokens with offest and x/y coordinates, consumable, actionable, and deployable into a production environment. Our goal is to again abstract all of that from the developer or business analyst so they can understand the results in an easy way.


To help our customers cultivate and strengthen their data environments by being at the forefront of implementing the latest data advancements while maintaining operational solution deployment rigor.

Our targeted services and solutions turn Big Data complexity into Smart Data clarification and results.