Maana Knowledge Platform™
Maana’s platform brings knowledge into every step from discovery to enrichment and utilization to operationalization. Maana’s Knowledge Assistants enable technical experts to quickly create new iterative models and Maana’s Knowledge Applications enable business users to collaborate with technical experts for faster optimization of assets and processes.
Maana has over 14 Knowledge Assistants modules such as Semantic Similarity, DocAssist and Time Series Analysis. These Knowledge Assistants enable data scientists and technical experts to collaborate and rapidly create new iterative knowledge models for optimizing assets and processes.
- Similarity Assist
- Document Assist
- Clustering + Labelling
- Join Assist
- NLP + Topic Modeling
- Data Profiling
- Time-series Prediction
- Field Classifiers
- Parse Assist
- Many more…
Empowers users to identify instances, cases, events or records similar to the one they would like to investigate. For example, when looking for unprofitable insurance contracts, companies won’t be limited to searching for fixed attributes like high claims. Instead, they can simply use Semantic Similarity to pinpoint those contracts that are most similar to a sample unprofitable contract. Its guiding algorithms search across a number of dimensions (performance, economic, competitive, etc.) to identify similarities in the context of what the user is trying to solve.
Enables extraction of targeted knowledge in the relevant context from unstructured documents, like PDF or word files, emails, or images. For example, a large amount of data is usually stored in tables or hidden in unstructured documents like PDFs or Word files. Maana’s Knowledge Platform uses machine learning to extract structure from the documents as well as domain specific terminology that can be labeled for further training of the machine learning model.
Time Series Analysis
Analyze terabytes of multivariate, high-frequency (time) series data using interactive visualization and search.
Search results can be stored as a pattern with automatically labeled data, enabling a semantic interpretation of continuous signals. For example, sensors with specific patterns over a specific interval, could indicate a broken shaft. Predictive Algorithms and approaches, including non-sequential and deep learning techniques, help answer the key question: what are the leading indicators of failure and how early can they be detected so that preventive measures can be taken?
Join Assist guides users in finding non-obvious relations, even when the user does not fully understand their data relationships.
Often users have a good understanding of the available data, however the effort to manually connect it all on large data models makes the job difficult and time consuming. Maana provides a user-guided, machine-assisted mechanism for automatically building the relevant joins through a visual interface and little manual intervention.
Search and Explore
Maana’s patented Semantic Search enables users to find the most relevant data and knowledge inputs from across silos in the context of optimizing an asset or process. Once these knowledge inputs are indexed in the knowledge graph, this patented technology suggests and completes user queries using domain-specific knowledge from the knowledge graph.
By entering a keyword or phrase, the user is presented with a disambiguation dialog that provides a ranked and filtered view of all knowledge in the graph. Once the user guides the knowledge graph on the relevancy of the suggestions, data is presented using rich, interactive visualizations for subsequent refinement and action.
Extendable into Existing Tools
Maana provides extensibility through plug-ins, libraries, and REST APIs. Maana supports access to Python and R modules in an interactive environment as well as via a command line interface. Jobs can be run in a distributed environment for quick results. By using knowledge assistants in the platform and by leveraging external libraries, problems can be solved using the best tool available for the job.
Maana enables business users and domain experts to utilize Maana’s Knowledge Applications or easily develop their own to solve real-world business challenges faster than ever. These applications are used for optimizing business processes like supply chain, call center, accounts receivables or optimize assets through predictive maintenance and more.
- A/R Optimization
- Risk Management
- Customer Intelligence
- Competitive Intelligence
- Fraud Investigation
- Capital Allocations
- Supply Chain Optimization
- Field Services
- Call Center Optimization
- Pricing Optimization
- Predictive Maintenance
Operationalizes and Measures Impact on KPIs
Maana enables subject-matter experts and business analysts to translate the extracted knowledge into recommendations and embed them into the line-of-business applications for thousands of employees to make data-driven decisions. The result of this operationalization forms a feedback loop providing executives an understanding of the impact that the Maana Knowledge Platform has on the business and its KPIs.
Learns and Adapts
Once Maana’s Knowledge Graph recommendations are operationalized, the models continue to learn and adapt from the actions and feedback of subject-matter experts. This is another key advantage of having flexible models as they can be easily modified when needed. The user doesn’t have to start from scratch and can take advantage of prior work.
Maana’s Differentiation White Paper
How Maana’s Knowledge Platform Helps
Fortune 500 Companies Accelerate Digital Transformation
Maana Knowledge Platform
Increasing Enterprise Profitability