Our Success Stories

Our Proven Business Use cases by Industries!

PREDICTIVE MAINTENANCE

The Client was a large Water Utility that needed to predict failure for their water pumps. AI Modes were generated for the equipment using historical data parameters for the pumps along with the past equipment downtime instances. Then, real-time equipment data was gathered from the remote equipment using IoT sensors and uploaded to our secure cloud.

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MINSKYCUBETM

The Client was a large Power Utility company that required real time data acquisition from remote assets (Such as Transformers, Power equipment, etc) for conditional monitoring and predictive maintenance. In

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PREDICTIVE MAINTENANCE FOR SUBSTATION USING MINSKYTM AND BRAINCHIP’S AKIDATM (EDGE AI)

In general, Utility companies face significant challenges in effectively maintaining their critical electrical substations. They lack a proactive maintenance strategy, resulting in costly downtime and unplanned outages. The initial problem included limited historical data for analysis

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PREDICTIVE MAINTENANCE FOR HYDRAULIC OIL RIG USING MINSKYTM AND BRAINCHIP’S AKIDATM (EDGE AI)

Typically, Oil rigs are critical assets for the energy industry, and their downtime due to unexpected failures can lead to significant financial losses and environmental risks.

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DOCUMENT ANALYSIS USING MINSKY™-CHATGPT

Typically, all organisations are constantly faced with a need to analyse a large number of documents such as Legal Agreements, Contracts, etc. This requires a large amount of manual processing and extracting the salient features of a document which effects the efficiency and productivity of any organization. The objective of the project is to use Artificial Intelligence/ Generative Ai to automatically extract and summarize the salient feature of any documents (PDF, Word etc) based on user defined questions.

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MEDICAL DATA EXTRACTION USING MINSKYTM AND GENERATIVE AI

Typically, Medical billing is a complex and time-consuming process that involves analyzing detailed medical records and generating relevant data for filing insurance claims to process payments

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AI ENABLED EV INTRUSION DETECTION SYSTEMS

Currently, Electric vehicles (EVs) have gained significant demand due to their eco-friendly and cost-effective nature. However, with the increasing connectivity and complexity of EV systems, the risk of cyberattacks targeting these vehicles has also vastly increased.

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GLOBAL GHG EMISSIONS

Climate change has been a critical problem that requires rapid action in today’s Digital world to save our planet. Its impacts are already being felt globally and the negative effects are expected to grow exponentially, with disproportionate consequences for the world’s most marginalized communities. Tackling climate change requires action across society, spanning many communities, approaches, and tools. After a detailed analysis and research, Ai labs has done has developed and implemented a Proof of Concept (POC) using its proprietary engine Minsky to highlight the effects of Climate change.

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GHG EMISSIONS FROM POWERPLANT

Climate change has been a critical problem that requires rapid action in today’s Digital world to save our planet. Its impacts are already being felt globally and the negative effects are expected to grow exponentially, with disproportionate consequences for the world’s most marginalized communities.

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CO2 EMISSIONS FROM AUTOMOBILES

Climate change and environmental concerns have brought significant attention to the reduction of carbon dioxide (CO2) emissions This is true particularly in the automotive sector where CO2 emissions is a major contributor to the global Climate Change crisis.

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CO2 EMISSIONS FROM AGRICULTURE

The agriculture sector is a significant contributor to carbon dioxide (CO2) emissions, primarily due to practices like deforestation, fertilizer use, and transportation. Reducing agricultural emissions is crucial for mitigating climate change, yet accurately quantifying these emissions is a complex challenge.

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WATER TREATMENT FORECAST

Global demand for water has been increasing as urban economic activity expands. A significant amount of effort is required to obtain clean water and the reduction of this effort requirement is a major concern for water utilities. The aim of this project was to predict if water is safe for human consumption based on the chemical composition (such as PH, Hardness, Total solids dissolved etc) for water produced or coming from different sources. This helped the client to make better decisions on whether or not to process the water for consumption.

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HYDROCEPHALUS

Hydrocephalus (HYC) is the segregation of fluid in the cavities (ventricles) deep within the brain. The excess fluid increases the size of the ventricles and puts pressure on the brain. Cerebrospinal fluid normally flows through the ventricles and bathes the brain and spinal column. Hydrocephalus is a frequent complication which is affecting almost all age groups following subarachnoid haemorrhage. Few studies investigated the association between laboratory parameters and shunt-dependent hydrocephalus. Non-contrast materialenhanced head computed tomographic (CT) examination is an important method for the diagnosis of HYC because it can observe the enlargement of the ventricles, and sometimes determine the cause of HYC. However, due to the lack of uniform standards, different range of patients’ ages and the various levels doctors’ expertise, it is rather difficult to reach a diagnosis. Therefore, after a detailed analysis and research, Ai labs developed and implemented a Proof of Concept (POC) using its proprietary engine Minsky™ to highlight the need for a shunt for an individual and mortality caused by Hydrocephalus.

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BRAIN TUMOR

Typically Brain tumor occurs due to uncontrolled and rapid growth of cells and could be fatal if not treated early. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size .And the first step in treatment for any brain tumor patient is often surgery to remove as much of the mass as possible and should not be delayed. The objective of this case study is to classify brain tumor through image segmentation from the MRI. Our approach was used to deliver comprehensive results using machine learning and deep learning algorithms on brain tumor detection through image segmentation magnetic resonance imaging combined with patient medical data. After a detailed analysis and research, Ai labs developed and implemented a Proof of Concept (POC) using its proprietary engine Minsky™ to identify the presence of a tumor.

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HEART DISEASE PREDICTION

Globally, Heart Disease has become a major cause of morbidity. The deaths are rising significantly annually due to this disease. Of all heart diseases, coronary heart disease (heart attack) is the most common and fatal. The silver lining is that these heart attacks can be highly preventable by maintaining healthy lifestyle (such as reducing alcohol and tobacco use, eating healthily and exercising) coupled with early treatment that greatly improves its prognosis. It is, however, difficult to identify high risk patients because of the multi-factorial nature of several contributory risk factors such as diabetes, high blood pressure, high cholesterol, etc. This is where machine learning and data analytics can be used to analyse the co-relation between factors/parameters and predict the risk of heart disease. The objective of the project is to predict the risk of heart attacks using MinskyTM Machine Learning Models that can help clinically in analysing the risk factors of the disease and interpretation of the important factors affecting the particular patience.

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WORKERS COMPENSATION FRAUD

Globally, workers compensation Insurance fraud has become a major concern for all the insurance companies which is continuously increasing year by year. The aim of this project was to build an AI model in order to rate the claims so that the SIU teams can process the cases according to the risk levels. This could lead to significant savings by eliminating fraudulent claims. This led to improved revenues and business continuity in the long term.

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HEALTHCARE INSURANCE

This case study is based on data from a large global insurance company which is engaged in providing health insurance. With offices in multiple countries, the claims department handles various activities such as processing and approving claims etc. Each of these processes comprise of several activities that are time consuming but critical for employee satisfaction. This solution leverages our Minsky™ AI engine to categorize each claim as High Risk or Low Risk based on the customers historical data. Minsky™ then creates accurate Modes that can be used to process live data for high risk/low risk to be handled by the appropriate claims adjuster based on the rating for further processing. This real time monitoring/actions helped us automate the claims processing across various geo locations offices while also reducing fraudulent claims. Solution was integrated with an existing system.

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PHARMACY CLAIMS

The US federal government has several Pharmacy reimbursement programs that require drug manufacturers to provide outpatient drugs to eligible health care organizations and covered entities at significantly reduced prices. These programs cover entities to stretch scarce federal resources as far as possible, reaching more eligible patients and providing more comprehensive services. These Govt. Pharmacy programs were established in order to provide discounted drugs for covered entities, such as “high-Medicaid public and private non-profit hospitals, community health centres’, and other safety net provider to help those facilities to deliver pharmacy services to those underinsured or uninsured outpatient populations Each of these processes comprise of several activities/forms and lot of manual work is involved that are time consuming but critical for to reduce costs to Insurance companies. The objective of the project was to decrease insurance reimbursement and high levels of uninsured patients and healthcare providers are required to be more cost effective in delivering their services. Therefore, after a detailed analysis and research, Ai labs developed and implemented a Proof of Concept (POC) using its proprietary engine Minsky to predict the claim eligibility for these programs for individual patient claims.

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DATABRICKS™ INTEGRATION WITH MINSKY™

A data warehouse is a type of data management system that often contains large amounts of historical data from single or multiple data sources such as APIs, Databases, Cloud Storage, etc., using the ETL (Extract Load Transform) process. It is designed to enable and support business intelligence (BI) activities, especially analytics to understand the relationships and trends across the data. Data warehouses are used to perform complex queries and analytics. This collection of business data is used for reporting and also to help an organisation to make better decisions.

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INTEGRATION OF MINSKYTM WITH OPENAI

ChatGPT is a large natural language model chat bot developed by Open-AI company based on GPT-3.5. It has an ability to interact in conversational language form and provide responses that seems to be similar like human.It is trained with massive amounts of data to accurately predict next word comes in a sentence.

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INTEGRATION OF MINSKYTM WITH MS-AZURE

A cloud platform refers to the operating system and hardware of a server in an Internet-based data center. It allows software and hardware products to co-exist remotely and at scale.

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CUSTOMER CHURN

Customer churn has created huge concerns in the highly competitive service sectors and especially in the telecom sector. The aim of this project was to build a customer churn model using AI (Artificial Intelligence) to predict whether certain customers would leave in the future. This helped the client to retain customers for the long run which improved revenues and business continuity.

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AI IN REAL ESTATE

Typically, when it comes to data management in the real estate sector, there are several major challenges. Often the required data are either unavailable, not granular enough or outdated. Even when available, they might not be fully organized across geographic areas which requires lot of manual effort. This also requires other manual data corrections such as missing values or incorrect master data. In other words, there is a risk of ending up with expensive but worthless or even misleading analysis results such as personal bias by the expert “correcting” the data issues. The objective of the project is to model geo spatial data of any region in the world to accurately predict real estate demand and prices. Data and Insights were predicted from social demographics, rents, purchase prices and geographic points-of-interest (POIs). Typically, useful data in the Real Estate Industry always comes at a potentially steep price which can overcome by accurate modelling/analytics using Minsky™.

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SEMICONDUCTOR WAFER DEFECT ANALYSIS USING AI/ML

Semiconductor manufacturing is a highly complex and competitive branch of industry, comprising hundreds of process steps, which do not allow any deviations from the specification. Depending on the application area of the products, the production chain is subject to strict quality requirements.

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FLEET MANAGEMENT

Typically, fleet management operations worldwide face major challenges to run successfully. They have high operating expenses and significant maintenance issues which can be detrimental to the business if not addressed properly in time. Sometimes, timely detection of an upstream problem such as engine failures can prevent more expensive downstream issues. Un-scheduled maintenance issues might disrupt Supply Chain deliveries. In order to address these challenges, companies are turning to Ai to avoid catastrophic failures in the fleet vehicles.

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SEMICONDUCTOR WAFER-DEFECT ANALYSIS

Typically, fabrication of good quality semiconductor wafers without defects is challenging for any semiconductor manufacturer. It consists of sequential process steps performing physical and chemical operations on wafers. Usually, wafers are aggregated into so-called lots of size 25 or 50, which always pass through some operations in the production chain. Therefore, the entire manufacturing process can involve lengthy repetitive steps, and takes place in a sterile clean fabrication room designed to prevent even the tiniest speck of dust from falling on the pristine wafers. The fragile wafers may get scratched or get particulates from the clean room that could cause the micro circuits to malfunction when tested after the manufacturing process. Often, these flaws are microscopic and completely invisible to the naked eye which leads to poor production quality. If these flaws are not addressed at contamination phase, then there might be decrease in test yield that results in the wafer manufacturing costs. After a detailed analysis and research, Ai Labs developed and implemented a Proof of Concept (POC) using its proprietary engine Minsky to highlight the defects in wafer fabrication process. The objective of this case study is to automate inspections process and identify the defects by categorizing them as (good, Bad) in the process using Deep learning classification model depending on the response value set (Threshold value) by the semiconductor company.

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DISC BRAKE QUALITY INSPECTION

Typically for any manufacturing company, production quality and yield are two important performance metrics. Manufacturing processes typically include one or more steps where the product is visually inspected for defects. Typically, visual inspection is a highly manual process that can be time consuming and prone to errors. Poor production quality control results in significant operational and financial costs in the form of reworked parts, scrap generated, reduced yield, increased work in process inventory, post-sale recalls, warranty claims, and repairs. After a detailed analysis and research, Ai labs developed and implemented a Proof of Concept (POC) using its proprietary engine Minsky™ to highlight the defects of casting product operations. The objective of this case study is to automate inspection process and identify the defects by categorizing them as (Defective, Pass) in the casting process using deep learning classification model.

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ULTRASOUND BREAST CANCER

Breast cancer is one of the most common causes of the death among women worldwide. Early detection helps in reducing the number of deaths. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. Historically, it was a difficult and time consuming process to review the medical images of breast cancer using ultrasound scan and identify the tumor in the breast . The objective of this case study is to use evaluate and classify the type of tumor using ultra sound breast images. After a detailed analysis and research, Ai labs developed and implemented a Proof of Concept (POC) using its proprietary engine Minsky™ to automatically evaluate and classify the type of Breast tumor to aid in early treatment.

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ULTRASOUND FETUS BRAIN

The evaluation of fetal brain can be critical because deficits in the perfusion (passage of blood flow) of this territory may lead to inadequate development of the central nervous system and even jeopardize fetal vitality. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. The objective of this case study is to evaluate the maturity of fetal ultrasound brain in a real maternal-fetal clinical environment to automatically classify fetal anatomical planes of Brain. After a detailed analysis and research, Ai labs developed and implemented a Proof of Concept (POC) using its proprietary engine Minsky™ to automatically evaluate the maturity of fetal brain via ultrasound images and classify the data image views accordingly.

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