About Yottasys

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).

BOOST YOUR PRODUCTION OPERATIONS WITH NIRMAAN
Our Product

NIRMAAN

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.

AUGMENTED DECISIONS

Make more insightful decisions and By using real time data processing technique to ingest continuously ticking large data

UNSUPERVISED DECISIONS

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.

CONTINUOUS LEARNING

intelligence decision without human intervention for reduce exceptions and reworks.Appropriately interpret a defect process, Necessarily memorise the newly occurred failures.

POWERING TO MANUFACTURING

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.

USE CASES

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.

AVERT CRITICAL MACHINE FAILURES

Prevent catastrophic machine failures that lead to scrapped production hardware and unscheduled machine downtime.

INCREASE PROCESS YIELD

Determine process drift prior to generation of non-conformances, reduce cost of poor quality, and preserve capacity.

REDUCE MAINTENANCE COST

Reduce Maintenance Cost Enable planned maintenance windows with lower inventory levels of machine spare parts.

PREDICTIVE MAINTENANCE

Nirmaan Models predict signs of equipment failure well before they happen.Nirmaan quickly develops highly-accurate models that transform maintenance operations.

PROCESSING CONDITIONS

Once the domain of highly-specialized practitioners, the tuning of processing conditions at production facilities can now be automated with Nirmaan.

SMART PRODUCTS

Smart Products constantly learn through continual historical data analysis, allowing systems to evolve and improve faster than ever before

WITH NIRMAAN

PRODUCTION OPERATIONS USE CASES

QUALITY MANAGEMENT

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.

PREDICTIVE MAINTENANCE

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.

DEMAND FORECAST

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.

PROCESSING CONDITIONS

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

RESEARCH& DEVELOPMENT

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.

SMART PRODUCTS

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 defect classification and automation of fmea testing

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 Based Combustion Chamber Gas Quality Improvement

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 based defect classification in hot strip rolling steel mills

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 metal discovery

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.

Preventive maintenance of hydraulic cylinders

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 Defect classification for Automobiles

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.

Ai driven credit rating and loan amount predictions for NBFC’s

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.

ML driven EV Range predictions for electric vehicles

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.

BOOST YOUR PRODUCTION OPERATIONS WITH NIRMAAN

Meet Our Team

Arun Pandey

CEO-Co Founder

BALARAJU M

PRODUCT LEAD-SAAS-TECH

DINESH KRISHNAN

CO-FOUNDER

HARIOM SINGH

AI ARICHTECT

KRANTHI KUMAR

OPERATIONS LEAD

SHIVALINGAHYA

DATA SCIENTIST

SURESH KUMAR

PRODUCT LEAD-DATA SCIENCE

AARADHYA

DATA SCIENTIST

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Yottaasys

  • #172, SIRI SKYLIGHT SECTOR 7,HSR LAYOUT
    BANGALORE,INDIA
  • 080 43707012
  • arun.pandey@yottaasys.com / dinesh.krishnan@yottaasys.com