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REVOLUTIONIZE YOUR INDUSTRY WITH TARGETED AI APPLICATIONS

Pandemic Insights Analyzer:
Covid Articles Project

MediaMixer Digest:
Automated Parsing
and Analysis of Content

Customer Success AI: AI-Driven Support ExcellenceEva Stark

AI & Machine Learning:

AI & Machine Learning:

AI & Machine Learning:

- Efficient Data Processing
- Advanced NLP Techniques
- Data Cleaning and Sampling
- Natural Language Processing
- Geographical Data Processing
- Data Filtering Strategies
- Machine Learning Models

- Automated Performance Metrics
- Volume of Support Tickets
- Performance Analysis
- Identifying Underlying Issues
- GPT-4 Integration
- Weekly Reports

- Trend Analysis
- Time Efficiency
- File Handling

The Covid Articles Project is a sophisticated platform for data processing and analysis, specifically tailored to handle extensive
text data related to Covid-19.
Its main objective is to equip researchers, analysts, and public health officials with an efficient tool
for processing and categorizing information
from numerous sources.

This initiative focused on leveraging GPT-4 to automate the quality assessment of customer support staff. In response to our client's concerns about increasing subscription cancellations, our goal was to gain a more comprehensive understanding of the service quality provided by their support team, who were managing thousands of tickets daily.

This project aims to streamline the process
of staying updated with the latest news and reviews in the film and TV industry. It is designed
for professionals, enthusiasts, and researchers
who need to keep abreast of multiple sources
but lack the time to browse through each one.

Python

GPT-4

Python

Pandas

GPT-4 API

Pandas

SpaCy

SpaCy

HTML

CSS

FastAPI

MySQL

JavaScript

- Time Efficiency
- Content Aggregation
- Customization
- Accessibility

- Data Parsing and Analysis
- API Development
- Database Management
- News Digest Generation
- Frontend Development

Latest AI Work & Cases

See All

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Maximizing Business Outcomes:
The Strategic Role of Data Science
in Pricing and Sales Efficiency

Revolutionizing Cell Type Annotation
in Single-Cell RNA Sequencing with GPT-4:
A Comparative Study

Mastering the Digital Transformation:
AI and Machine Learning
in Manufacturing

In the rapidly evolving business landscape, data science emerges as a pivotal asset, transforming vast amounts of data into actionable insights that drive strategic decisions.

In the realm of single-cell RNA sequencing (scRNA-seq), the identification of cell types within diverse tissues is a pivotal yet challenging step.

Manufacturers face a plethora of challenges in the digital transition, including
substantial capital investments for integrating IoT and machine learning technologies.

Belgrade, Serbia, GMT+1 Primary location

ai insights

Pandemic Insights Analyzer: Covid Articles Project

The Covid Articles Project is a sophisticated platform for data processing and analysis, specifically tailored to handle extensive text data related to Covid-19. Its main objective
is to equip researchers, analysts, and public health officials with an efficient tool for processing and categorizing information from numerous sources. This capability is crucial
for gaining insights into the pandemic's spread, impact, and the public's response.




- Efficient Data Processing: The project utilizes a multi-stage filtering process to ensure
the extraction of high-quality, relevant information.
- Advanced NLP Techniques: Employing Natural Language Processing for entity recognition, the platform can pinpoint and categorize essential details like locations, dates, and numerical data.
- Geographical Insights: The extraction of location data offers valuable perspectives
on the geographical spread and focus of Covid-19-related discussions and reports.
- Trend Analysis: The project's ability to filter and categorize data based on specific criteria, such as mentions of US cities or gathering numbers, facilitates detailed trend analysis.
- Time Efficiency: Automation of data processing and filtering steps significantly saves
time and resources, leading to quicker analysis and decision-making.
Technological Details and Stack
- Data Cleaning and Sampling: Initial stages involve cleaning raw data, addressing missing values, and sampling rows for manageable processing.
- Python and Pandas: The project's backbone is Python, with Pandas for data manipulation and handling CSV files.
- Natural Language Processing: NLP libraries like spaCy and Flair are utilized for text processing, entity recognition, and extracting location and date information.
- Geographical Data Processing: The integration of geographical data processing uses
a dataset of US cities (uscities.csv) to map city mentions to corresponding counties
and states.
- Data Filtering Strategies: Various strategies are employed to refine the dataset, focusing
on US-related content and extracting specific information like gathering numbers and tense analysis.
- Machine Learning Models: The project uses pre-trained GloVe models for cosine similarity scoring, aiding in the identification of gathering amounts.
- File Handling: Capable of reading and writing data in both CSV and Excel formats,
the project ensures compatibility with a range of data analysis tools.




The Covid Articles Project is a standout comprehensive solution for processing
and analyzing Covid-19-related articles. Its blend of efficient data processing, cutting-edge NLP capabilities, and thorough filtering strategies renders it an invaluable asset for researchers and analysts seeking to comprehend the pandemic's impact and trends.
The technological stack guarantees robustness and scalability, meeting the demands
of large-scale data analysis in the dynamic field of public health information.

ADVANTAGES

MediaMixer Digest: Automated Parsing and Analysis of Content

This project aims to streamline the process of staying updated with the latest news
and reviews in the film and TV industry. It is designed for professionals, enthusiasts,
and researchers who need to keep abreast of multiple sources but lack the time
to browse through each one.



The primary functionality of this project is the automated weekly parsing of eight specific pages from four leading film and TV websites. The system scans these pages for articles
and reviews, focusing on identifying overlapping content across different sources.
For example, if two different websites publish articles about the same TV show
(e.g., Yellowstone), the system detects and processes this information.



This project is particularly beneficial for users who want to consume industry news efficiently. Instead of reading through eight different web pages, users receive a concise, aggregated summary of the most relevant news, saving time and effort.



- Data Parsing and Analysis:
Python Libraries: The project employs Python libraries such as NLTK for natural language processing, Scikit-learn for machine learning tasks, and Pandas for data manipulation.
These tools are crucial for parsing, analyzing, and categorizing content from the targeted web pages.
- API Development:
FastAPI: The backend API application is developed using FastAPI, a modern, fast web framework for building APIs with Python. FastAPI provides high performance and easy scalability, making it an ideal choice for handling web scraping and data processing tasks.
- Database Management:
MySQL: The project utilizes MySQL for database management, storing parsed data
and processed results. MySQL's reliability and efficiency make it suitable for handling
the data storage needs of the project.
- News Digest Generation:
OpenAI Library: To generate concise summaries or digests of the news, the project leverages the OpenAI library. This integration enables the creation of intelligent, coherent, and relevant summaries of the parsed content.
- Frontend Development:
A straightforward frontend interface is developed using web technologies such as HTML, CSS, and JavaScript. This interface allows users to interact with the system, view summaries, and access detailed analyses as needed.
Advantages
- Time Efficiency: Significantly reduces the time required to stay updated with industry news.
- Content Aggregation: Provides a consolidated view of news from multiple sources.
- Relevance: Employs advanced data science techniques to identify and summarize
the most pertinent information.
- Customization: Can be tailored to focus on specific topics or sources according to user preference.
- Accessibility: The simple frontend interface makes it easy for non-technical users
to access and understand the content.

Functionality

USE CASE

TECHNOLOGICAL DETAILS

Customer Success AI: AI-Driven Support Excellence

This initiative focused on leveraging GPT-4 to automate the quality assessment of customer support staff. In response to our client's concerns about increasing subscription cancellations, our goal was to gain a more comprehensive understanding of the service quality provided by their support team, who were managing thousands of tickets daily.



- Volume of Support Tickets: The client's support team was overwhelmed by the sheer number of daily tickets, making manual quality assessment impractical.
- Performance Analysis: Determining the effectiveness of each support staff member was challenging amidst varied and complex customer interactions.
- Identifying Underlying Issues: Our aim was to pinpoint the specific factors leading to customer dissatisfaction and subscription cancellations.
Solution Using GPT-4
- GPT-4 Integration: We utilized the advanced language processing capabilities of GPT-4 to analyze customer support interactions.
- Automated Performance Metrics: GPT-4 generated metrics to evaluate staff performance, focusing on response quality, timeliness, and customer feedback.
- Weekly Reports: We established a system for providing the client with weekly reports. These reports detailed each staff member's performance and highlighted notable cases or areas needing attention, all presented in a concise, easy-to-understand summary table format.



- Streamlined Performance Monitoring: The integration of GPT-4 enabled more efficient and accurate analysis of staff performance, offering insights that were previously challenging to obtain.
- Actionable Insights for Improvement: The client received detailed, actionable insights weekly, which were instrumental in identifying training needs and areas for service improvement.
- Potential Increase in Customer Satisfaction: By addressing the identified issues, the client was positioned to enhance the overall quality of their support services, potentially reducing the rate of subscription cancellations.


This project exemplifies the practical application of AI in optimizing customer service operations. By leveraging GPT-4's sophisticated analysis capabilities, we provided our client with a powerful tool to enhance their customer support quality, ultimately aiming to improve customer satisfaction and retention.

challenges and objectives

OUTCOMES

TrendScope Parser: Social Media Data Parser

In today's digital age, data is king. Our project, the advanced Social Media Data Parser, is designed to empower businesses by extracting valuable insights from two of the world's most influential social media platforms. This tool is more than just a script; it's a gateway to understanding market trends, consumer behavior, and competitive landscapes.
The parser expertly navigates social media profiles, adhering to criteria specified by our clients. It meticulously collects data, ranging from user engagement metrics and hashtag usage to content trends. This data is then efficiently organized and stored in a database, ready for analysis and strategic application.



- Competitive Intelligence: Analyze your competitors' social media activities to develop strategies that keep your business ahead.
- Market Insight: Understand the content that resonates with your audience, aiding in the customization of marketing campaigns.
- Trend Analysis: Stay on top of emerging trends by examining popular hashtags and topics.
- Customer Engagement: Monitor user interactions with your brand and competitors, fostering more effective customer engagement strategies.



- Language & Frameworks: Developed using Python, renowned for its capabilities in data manipulation and web scraping. Frameworks like Beautiful Soup and Selenium are employed for navigating and parsing social media content.
- API Integration: The tool uses official Instagram and Twitter APIs for reliable data extraction, where possible.
- Data Storage: Data is stored in a robust database system (such as MySQL or MongoDB), ensuring integrity and ease of access.
- Scalability & Performance: Designed for scalability, the parser efficiently processes large volumes of data without compromising speed or performance.
- Customization: Configured to meet specific client needs, whether tracking particular hashtags, user demographics, or engagement metrics.



- Front-End: A minimalistic approach, focusing on dashboard displays for data visualization, potentially using JavaScript libraries like D3.js.
- Back-End: Python, enhanced with libraries for web scraping and data processing.
- Database: SQL or NoSQL databases for storing and managing extracted data.
- APIs: Instagram and Twitter APIs for authorized data access.
- Hosting/Cloud Services: Utilization of cloud services like AWS or Azure to ensure the application's reliability and scalability.

STACK

Advantages

TECHNOLOGICAL DETAILS

Data Extraction/ETL

-Python
-Beautiful Soup
-Selenium
-MongoDB
-D3.js -NoSQL

Maximizing Business Outcomes:
The Strategic Role of Data Science
in Pricing and Sales Efficiency

Maximizing the Potential of Data Science

To fully exploit the potential of data science, businesses must first understand the intrinsic value of data
and how it can be harnessed to address specific business challenges. Data scientists specialize in collecting, analyzing, and interpreting complex datasets, creating predictive models that unveil patterns and trends invisible to the naked eye. However, without a clear directive on the business problems to solve, even the most sophisticated analyses may fall short of their transformative potential.

Collaboration between business leaders and data scientists is essential for translating data-driven insights
into actionable strategies. By closely aligning the objectives of data science initiatives with business goals, companies can identify and capitalize on opportunities to enhance their competitive edge.

The Strategic Advantage of Data-Driven Pricing

One of the most impactful applications of data science lies in refining pricing strategies. The nuanced understanding
of customer behavior, purchase patterns, and market dynamics that data science provides can inform dynamic pricing models that adapt in real-time to changing conditions. This approach not only optimizes revenue
but also enhances customer satisfaction by offering value that aligns with their expectations and preferences.

Companies like Amazon have set industry benchmarks by employing data science to adjust prices dynamically based
on factors such as demand, competition, and inventory levels. This capability enables them to maximize profitability
while maintaining a competitive position in the market.

Driving Revenue Growth Through Data Science

The immediate impact of leveraging data science is most evident in its capacity to drive revenue growth. Through detailed analysis of customer data, businesses can identify which products are in demand, the most effective product bundles, and optimal pricing strategies for different market segments. This insight allows companies to make informed decisions that bolster sales and improve margins.

Empowering Sales with Data-Driven Insights

A well-defined data science strategy extends its benefits to the sales process, equipping sales teams with the insights they need to close deals more effectively. By understanding the market, competitor pricing, and customer behavior,
sales representatives can offer tailored solutions that meet clients' needs, thereby enhancing the likelihood
of conversion.

Conclusion

In conclusion, data science is not merely a technical discipline but a strategic business tool that, when effectively leveraged, can significantly enhance decision-making, operational efficiency, and profitability. Companies that invest
in data science and foster a culture of collaboration between data scientists and business units stand to gain
a competitive advantage in the modern business ecosystem. As the landscape continues to evolve, the role of data science in shaping business strategies will undoubtedly grow, making it an indispensable asset for companies aiming
to thrive in the digital age.

In the rapidly evolving business landscape, data science emerges as a pivotal asset, transforming vast amounts of data into actionable insights that drive strategic decisions. This domain's impact is particularly profound in enhancing a company's pricing strategy, customer engagement, and overall operational efficiency. Leveraging investments in data science can yield immediate and significant business results, provided that there
is a symbiotic relationship between business units and data science teams.

The advent of Generative Pre-trained Transformers (GPT), such as GPT-3.5 and GPT-4, has brought new possibilities
to the field. These large language models, designed for comprehending and generating language, have shown promise in biomedical contexts. This paper posits that GPT-4, in particular, could significantly streamline the cell type annotation process, potentially moving it from a manual to a semi- or fully automated procedure. GPT-4's integration into existing single-cell analysis workflows, such as Seurat, could offer a cost-effective and efficient solution without the need
for additional data pipelines or the collection of high-quality reference datasets. Its vast training data allows for broad applications across various tissues and cell types, while its interactive nature enables users to refine annotations further.

This study comprehensively evaluates GPT-4's performance in cell type annotation across ten datasets encompassing five species and a wide array of tissue and cell types, including both normal and cancer samples. The analysis shows
that GPT-4's annotations closely match manual annotations in over 75% of cell types across most studies and tissues, demonstrating its ability to produce expert-comparable cell type annotations. This high level of agreement is especially notable for marker genes identified through literature searches.

Moreover, GPT-4 outperforms previous models and other automated annotation methods in terms of average
agreement scores. Its speed and cost-efficiency further underscore its potential as a valuable tool in single-cell analysis.
The findings suggest that GPT-4 can robustly identify mixed/single cell types and known/unknown cell types,
even under varying conditions of subsampling and noise levels.

Despite these promising results, certain limitations, such as the undisclosed nature of GPT-4's training data, necessitate human evaluation to ensure the quality and reliability of the annotations. The study also highlights the need for caution in cases where high noise levels in scRNA-seq data or unreliable differential genes might affect annotation accuracy.

In summary, GPT-4 represents a significant step forward in cell type annotation for scRNA-seq analysis, offering a blend of accuracy, efficiency, and user interaction that surpasses current methods. This advancement not only facilitates faster and more reliable analysis but also opens new avenues for exploring cellular diversity and function within and across tissues.

In the realm of single-cell RNA sequencing (scRNA-seq), the identification of cell types within diverse tissues is a pivotal yet challenging step. This process, traditionally manual and time-intensive, involves comparing genes that are highly expressed in each cell cluster against a set of canonical cell type marker genes. Although automated methods for cell type annotation exist, the manual approach remains prevalent due to its reliability and accuracy.

Revolutionizing Cell Type Annotation
in Single-Cell RNA Sequencing with GPT-4:
A Comparative Study

Manufacturers face a plethora of challenges in the digital transition, including substantial capital investments
for integrating IoT and machine learning technologies, managing change, bridging technical skill gaps, and handling
the growth and security of data. To navigate these complexities, Shukla emphasizes a structured approach, beginning with "datafication" — transforming business processes into quantifiable data for analysis. This process is supported
by an enterprise-wide Data & AI council, guiding the definition, lineage, and sources of critical data.

AI and ML are pivotal in extracting value from vast data collected daily, enabling Jabil to deliver business insights, automate tasks, and refine system capabilities. Shukla highlights their strategy of employing AI algorithms for enhancing factory inspection processes and leveraging advanced data analytics for business intelligence.

The migration to SAP S4 Hana in January 2022 marked a significant leap for Jabil, shedding decades of technical debt
for improved data processing speeds and streamlined data structures. This transition is not just about technical upgrades but about unlocking new value for users, enhancing productivity, and embracing new functionalities for process transformation.

Moreover, robotic process automation (RPA) has played a crucial role in automating repetitive tasks, with Jabil personifying these digital tools as part of their digital workforce. This approach not only achieves maximum ROI
but also fosters a digital-first culture within the organization.

To overcome the challenges in AI/ML implementation, Jabil adopted a customer-centric strategy, focusing on solving business problems and enhancing safety, quality, delivery, and cost metrics. This entails a deep understanding
and communication with stakeholders to align efforts with business priorities.

Regarding scalability and adaptability in manufacturing operations, Shukla underscores the importance of centering
on people as part of a digital-first approach. He advocates for upskilling and reskilling workers to harmonize human capabilities with intelligent machines, fostering an environment that values human insight alongside technological advancement.

Addressing cybersecurity, Shukla describes Jabil's comprehensive Defense-in-Depth approach, encompassing threat identification and blocking, rapid incident response, and minimizing downtime post-incident. Guided by the National Institute of Standards and Technology (NIST) Cybersecurity Framework, Jabil ensures robust data protection, continuous threat monitoring, and enterprise resilience against cyber threats.

Through this interview, Shukla provides a comprehensive overview of the strategic, technical, and security
considerations involved in leveraging AI and ML technologies in manufacturing. His insights highlight the potential
of digital transformation to enhance operational efficiency, foster innovation, and secure the manufacturing sector
against emerging threats.

In a revealing dialogue with Asia Business Outlook, BN Shukla, the Operations Director at Jabil, India, with over 28 years of expertise in operational excellence and business management, sheds light on the transformative influence of artificial intelligence (AI)
and machine learning (ML) in manufacturing. He delves into the inherent challenges
of deciphering the decision-making mechanisms of AI and ML, strategies for their efficient deployment, safeguarding critical production data, ensuring compliance with regulatory standards, and more.

Mastering the Digital Transformation:
AI and Machine Learning
in Manufacturing

Stay tuned!


More updates are coming very soon!