SULU - Save Millions On Your Large Research & Development Projects with Graph Based KM


                                   


Search tries to take a human question and apply it against documents in their original form.

Our EnterpriseNLP product reduces that document into a grammatical morphological analysis which empowers more accurate searches, but it's still limited to information within one document.

What if you could take the knowledge of your business and organize it into a self organizing net of information so that information was easier to find and relate?

That is the power of SULU.  It brings your enterprise information from a disparate array of sources and documents into one powerful self learning system.

SULU is a graph based knowledge management platform. It is a hybrid design supporting CNNs and Louvain clustering.

SULU is targeted towards large research and development projects where cognitive search with NLP is not enough.  Are you developing the next tactical fighter, building a vehicle that takes off like a plane and goes into orbit, the worlds fastest parallel processor for neural networks, or the latest vaccine - SULU is for you.

Companies spend billions of dollars bringing advanced research technologies to life, yet spend hardly anything on the kinds of information tools that can save them millions and produce better products. SULU bridges that gap and delivers a next level information interaction experience. Sometimes its not only saving money, but producing a mission critical product that doesn't have a critical failure. Invest in the best tools in the market. They have now evolved from simple document management infrastructure tools and from simple enterprise search. SULU is the next level. Would you dare trust your multi-million dollar project to anything less?

SULU is built from the ground up to support strong levels of security tagging as well as state of the art encryption to ensure that even the most rigorously secured products can work within it's framework.

Unique Features Include:
    Information GAP Analysis: Using Predictive AI it tells you where information is needed
    Conceptual Relationship: Shows you what features are helpful to solving a problem
    Project Focus Analysis: Helps managers understand how the project is shifting focus and what problem areas are at the forefront and what information is needed for the next step
    Direct Knowledge Capture: Rather than simply query documents, SULU let's your team experts directly encode and relate critical project information
    Enterprise Fact-Base Ontologically and Grammatically Searchable

Why trust the success of your research and development project to 1990s tech? Get modern, Get SULU!

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CES 2020 – Jan 8 2020 Noonean Announces SULU the Self Learning Platform for Language Understanding.

Today Noonean Cybernetics is announcing the launch of SULU, a new technology for natural language understanding. SULU, which stands for Supervised/ Unsupervised Language Understanding – is a hybrid of Ontology architecture and a self trainable neural network. Search Engines like Noonean's EnterpriseNLP product does its best to use advanced grammar morphological comparisons to match user questions to documents and sentences to achieve a best in class precision and rank. But the problem has always been that all of that information is in document form and it can only answer questions which are directly there in the document. In contrast to that, as SULU learns the language of your business and their relationships, it relates information together into a unified superbase.


By combining the latest in dynamic learning neural networks and Ontologies SULU provides a best in class technology for general knowledge capture and language understanding. In short it can understand and answer harder questions than traditional NLP systems, even when they need to be inferred across several linguistic relationships. Noonean president Gianna Giavelli states - “SULU is going to be our foundational technology platform to bring more human language systems to information retrieval, knowledge management, and large scale research initiatives.”