The word "Yotta" in Yottaasys stands for the largest unit of data which can be measured, Yottaasys is all about data and insights based on data. We have productized data sciences in our product Decision Sciences Factor(DSF).
DSF is a Data Sciences product which helps users of all experience levels to solve business problems statistically and that too with great value & affordability. DSF has been segmented by verticals and domain focus areas include BFSI, Hi-Tech Manufacturing(FMEA bots) and Supply Chain Management(Last Mile delivery bots).
AI-POWERED SAAS BASED PLATFORM FOR HIGH TECH MANUFACTURING
Explore the impact of change in manufacturing performance index for AI decision making.
Increase production visibility and improve planning efficiency through real-time OEE monitoring
Optimize energy utilization through connected plant utility management (Water, Electricity, Thermal).
Explore the impact of change in manufacturing performance index for AI decision making.
Make more insightful decisions and By using real time data processing technique to ingest continuously ticking large data
Nirmaan eats world dirtiest data for breakfast It leverages its expertise in building algorithms that use Machine Labelled datasets, in order to provide unsupervised intelligence.
intelligence decision without human intervention for reduce exceptions and reworks.Appropriately interpret a defect process, Necessarily memorise the newly occurred failures.
NIRMAAN leverages its proprietary Automated Model Building solution to generate the most globally optimal model for each an data set. Automated Model Building is capable of performing multiple analytical techniques, including Anomaly Detection, Clustering, Classification and Regression. NIRMAAN’s, YottoPredictTM is able to consume unlabeled data (or data without known failures and states, also known as unsupervised lea rning) and perform an ensembled, automated clustering technique. After specific clusters are identified, users are able to classify them via the user interf ace. YOTTOPredictTM is also capable of handling labeled data (Data with known failures, also knownas supervised learning). It does this by leveraging automated classification and regression algorithms to optimize for the fitness of any new data in a streaming format. In the same way as the clustering algorithms, these classifications can be relabeled and modified within the user interface to retrain the system.
YOTTOPredict as part of its data ingestion process runs a set of routines for cleaning and filtering data to address “dirty data” such as bad sensor data .Truly missing data can be interpol a ted using a variety of methods, from more advanced clustering techniques to simple linear fitting. YOTTOPredict is able to automatically identify the best method for interpolating the data in these instances and implement the appropriate technique.Learn More
YOTTOPredict’s unique automated model building algorithm tests state of the art tree basedalgorithms, different types of regression, deep recurrent neural networks, and more to build initial models. Then, the top performing initial models are combined using genetic algorithms and ensembling techniques to evolve a final model that achieved a global optimal solution.Learn More
YOTTOPredict leverages aproprietary algorithm, Nirma anArtemisTM, to derive thousands of additional features for every data set.YOTTOPredict utilizes a patented algorithm, NIRMAANPythia, to perform feature selection.it is requisite to identify the features and patterns most strongly correlated with the problem at hand. NirmaanPythi does this by using a combination of supervised and unsupervised methods to identify exactly which fea tures can be removed from the model building process.Learn More
YOTTOPredict will not only develop the optimal algorithm for any solution, but it can also distil the complex ensemble back into something that is understandable to a human YOTTOPredict does this by breaking down the derived features used in the model to identify top contributing original features.This creates the capability to not only identify when a specific event or anomaly might occur but also identify precursor variables that are leading to the event or anomaly.Learn More
Nirmaan is applying its AI/Data Science technologies to identify process drift before the generation for non confirming materials, preventive maintenance and competently predict future failures.
Prevent catastrophic machine failures that lead to scrapped production hardware and unscheduled machine downtime.
Determine process drift prior to generation of non-conformances, reduce cost of poor quality, and preserve capacity.
Reduce Maintenance Cost Enable planned maintenance windows with lower inventory levels of machine spare parts.
Nirmaan Models predict signs of equipment failure well before they happen.Nirmaan quickly develops highly-accurate models that transform maintenance operations.
Once the domain of highly-specialized practitioners, the tuning of processing conditions at production facilities can now be automated with Nirmaan.
Smart Products constantly learn through continual historical data analysis, allowing systems to evolve and improve faster than ever before
A good model reduces the lead time and cost of inspections by inspecting only areas that are higher risk.With Nirmaan, the quality of products can be modeled based on data about materials, production status, and other environmental variables. In addition, by visualizing models with Nirmaan, warning factors are detected much earlier, improving the overall yield.
Nirmaan Models predict signs of equipment failure well before they happen. By using historical data such as electrical current, vibration, and sound generated by manufacturing equipment, Nirmaan quickly develops highly-accurate models that transform maintenance operations.
Accurate demand forecasting makes production plans more efficient and helps eliminate waste. By applying machine learning to market data, product specifications, and sales trends, Nirmaan predicts future sales more accurately than traditional forecast methods.
Once the domain of highly-specialized practitioners, the tuning of processing conditions at production facilities can now be automated with Nirmaan. By quickly generating models that offer highly-accurate predictions in a fraction of the time of manual methods, manufacturing firms speed the set up of new production lines
Manufacturing firms have a number of important considerations for R&D, including setting direction and priorities for the company and making build vs. buy decisions. Nirmaan drives improvements for R&D through the visualization and comparison of Nirmaan models.
With IoT, almost every device now connects to the Internet, constantly updating and providing value-added services. By leveraging models built by Nirmaan in connected (or offline) line items, these Smart Products constantly learn through continual historical data analysis, allowing systems to evolve and improve faster than ever before.
AI driven process automation of defect classification and FMEA testing for high quality steel plates. After successful implementation the FMEA testing time was reduced from 3 months to 3 minutes and costs were brought down by 99%. This implementation was formally accoladed by the Board of one of the most respected Japanese multinationals.
AI Driven optimization to uncover the best proportion of gasses to be mixed in the gas chamber and Provide a proportion measure for each gas in the chamber as the optimum heat produced depends on the proportion of gasses mixed. Finally derivation of Combustion Quality Estimates (CQE) can be derived using various optimization techniques.
Ai driven real-time product classification model for hot strip rolling mills which can automatically classify the products being manufactured as good or anomalous, Nirmaan’s sensitivity and specificity curve is tuned automatically to reduce the false negative rate. The overall solution also Reduced the ambiguity during manual classification with warning delivery using a validation sample, and false positive. Interpretable dashboard with summary on the nature of feature.
AI driven composite material discovery for semiconductor sesquioxides, this use case is still in implementation and has the potential to reduce the new alloy discovery time from an average of 16 years to a few weeks.
Ai driven predictive maintenance for hydraulic cylinders for cold rolling mills, this is the first ever implementation of AI for predictive maintenance of Cold Rolling Mills.
Ai driven defects classification using computer vision on finished surfaces of automobiles, this is still in the pilot phase and has the potential of automating the current methods of manual/semi automatic testing of finished surfaces across the entire automobile sector.
In this implementation we are predicting the credit ratings and loan amounts for the undocumented customer, The overall solution includes Ai driven end to end assessment, underwriting, credit rating, loan amount, profile ranking and an overall holistic score. The solution is targeted to disrupt the untapped undocumented customer specifically in the emerging economies.
In this implementation we are predicting the proposed real-time EV charge scheduling which depends upon the battery dynamics and availability of charging slots. Based on the scheduling management facility, the system will deliver the information to the user regarding the nearest charging station, best cost function and booking slots with respect to estimated vehicle battery SOC.
PRODUCT LEAD-DATA SCIENCE
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