Self-service and
Highly Intuitive User Experience


Enables subject-matter-experts and business analysts to:

  • Build, enrich, search, and visually explore the Maana Computational Knowledge Graph™.
  • Understand the relationships and interdependencies between concepts such as equipment, people and activities.
  • Collaborate with data scientists to develop the digital knowledge layer of the enterprise in weeks.
Maana Q screenshot
Maana Q screenshot
Maana Q screenshot

Enhanced Architecture



Maana Q architecture is built using GraphQL which provides an increasingly popular interface to the Maana Computational Knowledge Graph, making it easier to integrate new and existing intelligent services and bots.


Knowledge Bots

The Knowledge Bots automate many of the computational modeling performed by your data scientists, such as field classification, entity recognition and supervised machine learning. Now you can can accelerate developing models that power your Knowledge Applications.

Cloud Native

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Cloud Native on Azure

MAANA Q is also cloud native, allowing enterprises to deploy Maana on Microsoft Azure for greater enterprise security, scalability and control.

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DOCKER Containerization

Maana Q architecture uses Docker for enhanced agility, portability and security making it easy to extend the platform with additional components.

Knowledge Platform Resources

Maana's Differentiation White Paper

How Maana’s Knowledge Platform Helps
Fortune 500 Companies Accelerate Digital Transformation


Maana Q Solution Brief

Enhanced self-service capabilities for subject-matter experts.


Knowledge Graph

All available and relevant knowledge is digitally represented in the Maana platform in the form of models, which capture information and the relationships between it. Multiple models can be connected together to form an intelligent, relationship driven, easily searchable and scalable semantic Knowledge Graph.

This Knowledge Graph sits at the core of the Maana platform and puts relevant knowledge – in the form of recommendations – at the fingertips of decision makers. In this way, the Knowledge Graph vastly accelerates decisions, improves the quality of decisions, and learns from outcomes, which improves recommendations over time.

Knowledge Graph screen

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Knowledge Assistants

As their name suggests, knowledge assistants use artificial intelligence, machine learning, and many other techniques to augment the work of people as they extract, explore, discover, and model new forms of knowledge that describe some aspect of their business domain.

This user-guided, machine-assisted approach to decision making optimizes the interactions between people and machines to maximize organizational productivity and effectiveness.

Knowledge Assistants screen

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Knowledge Applications

Knowledge Applications use machine intelligence to augment user decisions, enhancing both the quality and the speed with which day-to-day decisions are made.

Specifically, these applications are tailored for business users and provide prebuilt analytic functionality for predictive maintenance, supply chain optimization, accounts receivable optimization, and more. Users can also build new, custom Knowledge Applications or extend existing ones to meet unique requirements.

Knowledge Applications screen

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Knowledge Modeling

Knowledge, from Maana’s perspective, is captured in the form of reusable models, which capture information and the relationships between it. Knowledge modeling is the digital encoding of information/data together with human expertise to generate actionable recommendations that can then be fed into specific decision flows.

The modularity of our platform enables the digitization of all high-impact assets and decision flows (which for a large enterprise, typically number more than 100), at scale and speed. By representing each decision flow as one or more reusable models, users can apply, repurpose, track and combine models – much like building blocks – to meet new business needs. In this way, knowledge modelling accelerates digital transformation.

Knowledge Modeling screen

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IT organizations should be able to integrate and extend any new, enterprise wide platform to their current technologies. The knowledge platform can support traditional 3rd party ETL, data wrangling, visual analytics, dashboards etc.

Knowledge exists in many forms which is required to support any backend system, such as Google’s TensorFlow, gridMathematica, or even a proprietary system through a pluggable interface. Users should also have access to the right tool for each job. Whether it is a Python-based optimization package or a complex, deep learning network, fluid integration and composition is necessary and available.

Extensibility logos

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Search and Explore

The Maana platform’s rich user experience ensures that it understands the intent and contextual meaning of the terms users - subject matter experts, analysts and data scientists – are searching for. Any follow-up inquiries are automatically considered in context, making interacting with the platform much like talking with a person and brainstorming about a problem.


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