Automate Product Quality Inspection with Computer Vision

AI-powered Product Quality Checking Systems Adopted by Leading Manufacturers

Maintaining product quality is crucial for any manufacturing brand. We have seen that more than 65% of manufacturers depend on manual inspection process. This is causing them huge loss of production time along with unavoidable issues like human-prone error, missing subtle defects, delay in production, and wastage of materials.

Therefore, manufacturers are shifting towards automated product quality inspection systems, which save them time and money.  These automated systems are packed with intelligent automation and real-time analysis to incorporate data-driven approach to eliminate issues like human-errors, delay in production time, raw material wastage. 

Data Science Services We Offer

Our data science services are not only about just “consultation.”  We dive deep to evaluate your data culture and business goals. Depending on the evaluation result, we select and blend the data science technologies to develop solutions, aligned with your business goals. We bring 100+ years of combined experience to the table along with deep business acumen. 

Summary of Our Data Science Expertise:

With over 15 years of hands-on expertise across the data science development process, we assist enterprises with a complete package of data science services.

  • Predictive, Descriptive, and Perspective Analytics
  • Machine Learning Algorithms
  • Large Language Models (LLMs) Development
  • Cluster Analysis
  • Regression Analysis
  • Classification
  • Time Series Analysis
  • Anomaly Detection
  • Sentiment Analysis
  • Recommendation Systems
  • Text Mining
  • Image and Video Analysis
  • Market Basket Analysis
     
     

Phases of Our Data Science Services

Data Collection and Integration:

Businesses often have valuable data scattered in multiple places. We gather, organize, and integrate those raw data from different sources into a centralized data platform for seamless analysis.

Data Cleaning and Preprocessing:

The quality of the data ingested into the AI model is one of the deciding factors of the result accuracy. We clean up and process the collected data to eliminate inconsistency and missing values to derive correct results from the analysis.

Data Visualization and Reporting:

The stakeholders need clear, interactive, and informative dashboards that show the results in real time and help them get actionable reports to make data-driven decisions. We enable businesses to transform the raw data into actionable insights with enterprise-scale business intelligence (BI) platforms.

Leveraging Machine Learning and AI:  

Enterprises usually choose some common use cases or operational challenges to solve with Artificial Intelligence and Machine Learning. We develop and implement custom AI models, and ML algorithms to automate time-consuming tasks, make data-driven predictions, and identify patterns in large datasets. 

Predictive Modeling:  

To accommodate changing markets, surpass competitors, or optimize business strategy, businesses need to have tools for forecasting future trends and outcomes. The goals vary based on the business goals – predict customer churn, sales figures, security concerns, product quality, equipment performance, or market trends. We build custom predictive models for different business requirements.

Model Deployment and Integration:  

It is important to integrate the developed models into the existing operational systems to make the data science solutions cost-effective and result-driven. We take care of the entire integration process as an integral part of our Data Science As A Service (DaaS) offerings. 

Model Monitoring and Maintenance:  

To get accurate and effective results, model monitoring and maintenance is an utmost priority task. The models have to be fed with the latest datasets and fine-tuned to keep them functioning optimally. We provide post-deployment support so that businesses can uninterruptedly enjoy the investment they made. 

Primary Aspects of Data Science Solutions

Artificial Intelligence

The Artificial Intelligence revolution is gaining critical mass within enterprises worldwide. AI is transforming all functional areas. It is elevating decision-making precision by creating efficiencies, saving costs and delivering new solutions to critical problems. ThirdEye helps enterprises to participate in this AI revolution and leapfrog the competition by simply being able to take better decisions.

Deep Learning

Deep Learning (DL) is an aspect of Artificial Intelligence (AI) that emulates the learning approach that human beings use to gain certain types of knowledge. With the advent of Big Data technologies and the powers of Cloud Computing, the powers of DL is within the reach of every enterprise. ThirdEye helps enterprises effectively harness DL technologies and turn them into profitable business ventures.

Machine Learning

Machine learning focuses on the development of computer programs and algorithms, which provides computers the ability to learn and to find hidden insights from the ingested data without being explicitly programmed. ThirdEye understands how best to leverage Machine Learning techniques to best solve business problems. And it uses the latest Big Data technologies underneath it all.

Our Hands-On Experience in the Latest Data Science Tools & Technologies

ThirdEye’s data scientists are highly skilled with the latest data science technologies. We help enterprises to select and leverage the right tools and technologies based on their business goals. We aim to bring higher ROI by eliminating any additional cost acquired by unnecessary tools and guide you in selecting cost-effective yet impactful ones.

Function of this area: We gather and clean raw or unstructured data for analysis.

Tools and technologies we use in this area:  

  • For Data Discovery: Alteryx Designer, Trifacta Wrangler
  • For Data Extraction: Apache Sqoop, Apache Flume, Apache Kafka
  • Programming Languages: Python, Pandas, NumPy, R, including dplyr, tidyr.
  • For Data Cleaning: OpenRefine, Trifacta Wrangler
  • For Data Integration: Informatica PowerCenter, Talend Open Studio

Function of this area: This area helps us to gain initial insights into data characteristics and relationship.

Tools and technologies we use in this area:  

  • Data Visualization Libraries: Matplotlib, Seaborn, ggplot2, Tableau Public
  • Statistical Analysis Libraries: Pandas, SciPy, R base package
  • Interactive Notebooks: Jupyter Notebook, RStudio

Function of this area: We develop and train models to make predictions or classifications.

Tools and technologies we use in this area:  

  • Machine Learning Libraries: Scikit-learn, TensorFlow, PyTorch, Caret
  • Deep Learning Frameworks: TensorFlow, PyTorch, Keras
  • Statistical Modeling: Various Packages of R

Function of this area: We integrate the developed models into real-world applications to get the output.

Tools and technologies we use in this area:   

  • Model Deployment Frameworks: TensorFlow Serving, PyTorch Serving, Flask
  • Cloud Platforms: AWS SageMaker, Azure Machine Learning, Google Cloud AI Platform
  • Container: Docker, Kubernetes

  

Function of this area: In this area, we equip the decision makers with the dashboards to communicate with data insights effectively.  

Tools and technologies we use in this area:  

  • Data Visualization: Tableau, Power BI, QlikView, Looker
  • Data Storytelling Platforms: Tableau Public, Narrativa

Customer Success Stories

Enhancing Sales Operations for a Fabric Manufacturing Company

Establishing a fully integrated AI system capable of making informed predictions to optimize inventory and sales strategies by integrating existing data sources and external data sources.

Predictive Maintenance & Component Failure Analysis of Aircrafts

Developed a suite of predictive maintenance algorithms to analyze data from various sources to predict aircrafts' component health and optimize maintenance schedules.