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Automating PDF Purchase Order Processing for a Shopify Enterprise Using AI
 Automating PDF Purchase Order Processing for a Shopify Enterprise Using AI 150 150 grtlabs
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Overview

Many enterprises rely on Shopify for B2B operations. However, for a large client, most purchase orders were submitted as PDF files, not through the online cart.

Sales reps were spending 2–3 hours per PO manually extracting line items, prices, and metadata from PDFs to create Shopify Draft Orders. This was:

  • Slow and error-prone
  • Difficult to scale
  • Risky due to sensitive data stored in S3 buckets

We solved this challenge by building a secure, AI-driven automation pipeline that reduced processing time to ~10 minutes per PO, without exposing any data outside the corporate network.

The Challenge

  • Customers uploaded PDFs via Shopify but could not create orders directly.
  • Sales reps manually retrieved PDFs from a secure S3 bucket.
  • They copied and pasted order details into Shopify Draft Orders, applied pricing rules, and submitted the order.
  • Strict privacy regulations prevented the use of external AI APIs or sharing sensitive data.
  • The process was repetitive, slow, and costly.

Our Solution

We designed a privacy-first, queue-driven pipeline combining:

  • Docling – Open-source PDF extractor
  • Mistral 7B – Self-hosted AI model for structured extraction
  • Node.js & Python – Middleware for processing and Shopify integration
  • AWS S3, SQS, RabbitMQ – Secure storage and job queueing
  • GPU-powered inference server (AWS G5.xlarge)

Workflow

  1. Customer uploads PDF → S3 Upload Bucket
  2. S3 triggers SQS message for processing
  3. Node.js worker forwards the file to Python Docling engine
  4. Docling extracts structured data (tables, line items, headers)
  5. Cleaned data enters AI queue (RabbitMQ)
  6. Mistral 7B processes and outputs structured order information
  7. Node.js creates Shopify Draft Order using Admin API
  8. Draft order visible to the customer immediately

Architecture Diagram

Customer → Shopify Upload → S3 → SQS → Node.js Worker → Docling → RabbitMQ → Mistral → Shopify Draft Order
  • Docling: Handles multi-column PDFs and table extraction
  • Mistral 7B: Interprets context and maps data to Shopify schema
  • SQS & RabbitMQ: Ensure reliable queueing and scaling
  • Node.js: Integrates with Shopify Admin API and applies pricing rules

Model Optimization

Due to data privacy constraints:

  • No external training allowed
  • Mistral 7B self-hosted on a GPU instance
  • Prompt engineering and iterative tuning using ~200 anonymized PDFs
  • Schema validation against product catalogs
  • Final result: ~94% extraction accuracy, near-zero human corrections

Results

MetricBeforeAfter
Processing time per PO2–3 hours~10 minutes
Manual data entryHighMinimal
Error rateFrequentNear-zero
Customer visibilityDelayedInstant

Key Benefits:

  • Dramatically increased throughput
  • Reduced operational costs
  • Improved data accuracy
  • Maintained full compliance with privacy regulations

Conclusion

By combining open-source PDF extraction, a self-hosted LLM, and a secure AWS-based queue architecture, we transformed a manual, error-prone workflow into a highly automated, scalable, and compliant process.

This approach demonstrates how enterprises can leverage AI to accelerate operations while preserving data privacy and security.

Demystifying AI MCP: The Future of Scalable AI Model Configuration
 Demystifying AI MCP: The Future of Scalable AI Model Configuration 944 600 grtlabs
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In today’s rapidly evolving digital landscape, deploying artificial intelligence (AI) models at scale demands more than just powerful algorithms and vast data sets. It requires robust frameworks for managing how these models are configured, optimized, and deployed across diverse environments. Enter AI MCP — Artificial Intelligence Model Configuration Protocol — a concept gaining momentum among engineers and AI architects as a foundational approach to streamline AI lifecycle management.

What is AI MCP?

AI MCP (Model Configuration Protocol) refers to a structured framework or set of standards designed to manage the configuration, parameter tuning, and deployment settings of AI models. Think of it as the “blueprint layer” between data science and production environments, ensuring that models are not only well-trained but also well-managed throughout their lifecycle.

At its core, AI MCP aims to solve three primary challenges:

Governance and Compliance: Supports audit trails, version control, and reproducibility, which are vital in regulated industries like healthcare, finance, and defense.

Configuration Drift: Ensures consistency between development, staging, and production environments.

Scalability: Facilitates the deployment of models across multi-cloud, edge, and on-prem infrastructure.

Key Components of AI MCP

A modern AI MCP typically includes:

  • Model Configuration Schema: A JSON/YAML-based standard describing hyperparameters, input/output types, dependencies, and pre/post-processing steps.
  • Environment Definitions: Metadata for container specs, compute resources, and runtime environment (e.g., Python versions, libraries).
  • Versioning and Rollbacks: Built-in mechanisms to version control model configurations and revert to previous stable builds.
  • Monitoring and Logging Hooks: Integrated logging endpoints and telemetry for performance and drift detection.
  • Security Policies: Configurable rules for access control, encryption, and sandboxing.

Why Does AI MCP Matter?

As organizations increasingly shift toward AI-driven decision-making, the overhead of managing hundreds (or even thousands) of models becomes significant. Without a formal configuration protocol:

  • Small misconfigurations can lead to big failures.
  • Cross-team collaboration becomes chaotic.
  • Debugging model behavior in production becomes a nightmare.

AI MCP introduces a single source of truth for how models are expected to behave, making it easier to detect anomalies, troubleshoot issues, and maintain trust in AI systems.

Real-World Use Cases

  1. MLOps Platforms: Tools like MLflow, Kubeflow, and SageMaker use MCP-like structures to define how models are trained and served.
  2. Edge AI Deployment: When deploying models to edge devices (IoT, mobile), AI MCP helps define lightweight variants and runtime constraints.
  3. AI Compliance: In regulated industries, AI MCP plays a key role in documenting how a model was trained and what parameters were used — crucial for audits.

Looking Ahead: The Standardization of AI MCP

The future of AI MCP lies in standardization. Just like OpenAPI changed how we describe REST APIs, we may soon see an open standard for AI MCP that’s universally adopted. This would unlock better interoperability between platforms, simplify onboarding for new AI engineers, and improve the reliability of model deployments.

Final Thoughts

AI MCP is not just a technical add-on; it’s a necessity for any organization serious about operationalizing AI at scale. By treating model configurations with the same rigor as application code or infrastructure, teams can build AI systems that are not only powerful, but also dependable, explainable, and secure.

Increasing Sales and Profit for a Window Replacement Business
 Increasing Sales and Profit for a Window Replacement Business 512 512 grtlabs
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Introduction:

The Houston Window Replacement Company is a small business that specializes in providing high-quality window replacements for residential and commercial properties. Despite being in the business for several years, the company was facing a decline in sales and profit. The management team realized that they needed to take action to improve their sales and profitability.

Problem:

The company was struggling to increase sales and profitability due to a lack of effective marketing strategies and a limited online presence. The management team realized that they needed to reach a wider audience and improve their online visibility to increase sales and profit.

Solution:

The management team decided to work with a digital marketing agency to develop and implement a comprehensive online marketing strategy. The strategy included the following elements:

  1. Search Engine Optimization (SEO): The agency optimized the company’s website for search engines to improve its visibility and ranking. This helped to increase website traffic and generate more leads.
  2. Social Media Marketing: The agency created and managed social media accounts for the company on platforms such as Facebook and Instagram. This helped to increase brand awareness and reach a wider audience.
  3. Pay-Per-Click (PPC) Advertising: The agency ran PPC campaigns on platforms such as Google Ads and Bing Ads. This helped to increase website traffic and generate more leads.
  4. Content Marketing: The agency created and distributed valuable content such as blog posts and infographics to educate and engage potential customers.
  5. Email Marketing: The agency created and executed email campaigns to nurture leads and convert them into customers.

Results:

The digital marketing strategy implemented by the agency helped to increase the company’s sales and profitability significantly. The company was able to achieve the following results:

  1. Website traffic increased by 200%.
  2. Online leads increased by 150%.
  3. The conversion rate increased by 50%.
  4. Sales increased by 60%.
  5. Profit increased by 50%.

Conclusion:

The Houston Window Replacement Company was able to turn around its sales and profitability by implementing a comprehensive online marketing strategy. The strategy helped to increase website traffic, generate more leads, and convert leads into customers. The company was able to achieve a significant increase in sales and profitability, which helped to secure its future and growth. The management team realized that investing in online marketing was a smart decision and will continue to invest in it for future growth.

Elevate Your Data Insights: Upgrade to Google Analytics 4
 Elevate Your Data Insights: Upgrade to Google Analytics 4 512 512 grtlabs
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Google Analytics is a powerful tool for tracking and analyzing website traffic, user behavior, and other key metrics. However, with the recent launch of Google Analytics 4 (GA4), the platform has undergone a significant upgrade that offers even more advanced features and capabilities. In this blog post, we’ll explore the benefits of upgrading to GA4 and provide a step-by-step guide to help you make the switch.

One of the most significant improvements in GA4 is the enhanced data modeling capabilities. GA4 uses machine learning to automatically identify and group similar user behaviors, making it easier to understand and analyze your data. This allows you to gain deeper insights into your users and their interactions with your website, and make more informed decisions about your marketing and product strategy.

Another key advantage of GA4 is the improved tracking and measurement of user engagement. GA4 uses a new event-based model, which allows you to track specific user actions, such as clicks, scrolls, and form submissions, in real-time. This gives you a more complete picture of how your users are interacting with your website and allows you to optimize your website and marketing campaigns accordingly.

In addition, GA4 also offers improved data privacy and security features. With GA4, you have more control over your data and can easily comply with data privacy regulations such as GDPR and CCPA.

Upgrading to GA4 is easy, and you can do it in just a few simple steps. First, you’ll need to create a new GA4 property in your Google Analytics account. Then, you’ll need to install the GA4 tracking code on your website, and finally, you’ll need to set up and configure your events and parameters.

In conclusion, upgrading to GA4 is an excellent way to take your data insights to the next level. With enhanced data modeling, improved engagement tracking, and increased data privacy and security features, GA4 offers a more powerful and comprehensive solution for tracking and analyzing your website traffic and user behavior. So, upgrade your google analytics today and elevate your data insights.

AI Taking Over the World: How Artificial Intelligence is Making Human Life Easier
 AI Taking Over the World: How Artificial Intelligence is Making Human Life Easier 512 512 grtlabs
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As technology continues to advance at an unprecedented rate, the concept of artificial intelligence (AI) taking over the world is becoming a reality. The idea of AI making human life easier to live is a tantalizing one, as it offers the potential to solve some of humanity’s most pressing problems.

One of the most exciting ways in which AI is making human life easier is by automating many tedious and time-consuming tasks. For example, AI-powered chatbots are now able to handle customer service inquiries, freeing up human representatives to focus on more complex issues. Similarly, AI-powered virtual assistants can help manage our calendars, schedule appointments, and even make phone calls on our behalf.

Another area where AI is making a big impact is in the field of healthcare. AI-powered diagnostic tools and robots are now able to assist doctors in performing surgeries, and AI-powered algorithms are being used to analyze medical images, helping doctors make more accurate diagnoses. In addition, AI is helping to develop new drugs and treatments, which will save lives and improve the quality of life for many people.

AI is also making a big impact in the field of transportation. Self-driving cars are now a reality, and they have the potential to make our roads safer and reduce traffic congestion. Furthermore, AI-powered logistics and delivery systems are being used to optimize supply chains, which will help to reduce costs and improve efficiency.

Despite the many benefits that AI offers, it is important to be aware of the potential risks. As AI becomes more advanced, there are concerns about job displacement and the potential for AI to be used for malicious purposes. Therefore, it is important for society to address these issues and ensure that the development and deployment of AI is done in a responsible and ethical manner.

In conclusion, AI is making human life easier to live by automating many tedious tasks, improving healthcare and transportation, and helping to develop new drugs and treatments. While there are some concerns, society has the opportunity to use AI to solve some of our most pressing problems, and it is important that we do so responsibly and ethically.

#ArtificialIntelligence #AI #Automation #Healthcare #Transportation #Jobdisplacement #AI-powered #Chatbots #Virtualassistants #Self-drivingcars

Agile practices – they’re not just for software development
 Agile practices – they’re not just for software development 1024 614 grtlabs
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At Penn Medicine, agile methods have been adopted by a number of information technology teams, including teams that support its enterprise EHR, data analytics, IT operations and systems administration.

Agile software development methodologies such as Scrum and Kanban have been widely adopted by industry software development teams. Although agile practices originated in the realm of software development, other types of information technology teams can benefit from adopting similar approaches.

Fundamentally, agile methods are a set of practices that help teams to collaborate, organize their work, and see it through to completion.

Agile methods for software development

Agile methodology has emerged as a widespread practice for software development in response to the frustrations common in traditional software development projects, with their distinct, lengthy phases of scoping, requirements definition, design, build and testing. Each of these sequential phases can take months, leading to long software development cycles.

Especially in large organizations, traditional software projects can be difficult to manage and unsatisfying for those involved. If you’ve ever had to grapple with business requirement documents that are hundreds of pages long, or if you’ve sat through requirements and design walkthroughs for days on end, only to be disappointed by the software that was eventually delivered nine to12 months down the road, you’ll understand those frustrations.

At the risk of oversimplifying, I would describe agile software development as a shift to short software development cycles (weeks vs. months) and restructuring how people work together to develop software. I have found that these changes significantly improve the experience of software development for all parties involved.

Benefits of incremental delivery

Years ago, before agile methods became widely adopted, I asked one of my software development directors about the release schedule for his products. Since I came from a traditional software development organization, I expected to hear that he had two or three or four major releases per year.

Somewhat apologetically, he replied that his team didn’t have a formal release schedule. They just released features and enhancements whenever they were ready. Although unusual for the time, that incremental, “agile” approach has proven to be an effective way to work for many teams, resulting in improved products and a better experience for team members.

  • Product/usability benefits. Quickly getting new features into the hands of users provides important benefits. Software can only provide value when it is in the hands of end users, so the sooner we release, the sooner we can realize that value. Feedback, whether positive or negative, is best gathered from actual use, so the quicker we get that feedback the better.
  • Benefits to the development team. Short development cycles, for example two weeks, can benefit delivery teams in many ways. Small development tasks are easier to estimate than large ones, which makes meeting delivery deadlines easier. Short cycles establish clearly understood objectives and limit the amount of work in progress. This provides teams with the focus that helps them to drive tasks to completion. Software testing is simplified because a small amount of change is introduced in each release.

Finding better ways to work together

In addition to short development and release cycles, agile methods define a set of practices for how teams work together. A key concept is that the team commits together to getting the work done, which helps reduce overcommitting and finger-pointing.

A common practice is to have small, self-managing, self-organizing teams who interact with each other frequently, for example in short daily “standup” meetings. Another frequent practice is to break work down into units that take a short time to finish (for example, one to three days). In addition, many agile teams work together as a group to estimate work, increasing the accuracy of their estimates.

Beyond software development

Besides software development teams, other teams can benefit from adopting agile methods. Just as the field of project management grew from its early beginnings in engineering and construction into a more generalized discipline, agile methods can be broadly useful for various types of work.

In my organization agile methods have been adopted by a number of information technology teams, including teams that support our enterprise EMR, data analytics, IT operations, and systems administration. Non-IT teams have also followed suit, for example in marketing and our innovation center.

Regardless of the nature of your work, if you are interested in finding a better way to work, consider agile.

Credit: Glenn Fala is the Associate CIO of Application Development at Penn Medicine.

Dubai Unveils World’s Largest On-Site 3D Printed Building
 Dubai Unveils World’s Largest On-Site 3D Printed Building 1024 682 grtlabs
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Dubai has just unveiled a major milestone in construction printing technologies. The region’s new building in Warsan now holds the Guinness record for largest on-site 3D printed construction. The two-storey building also meets all the stringent building standards and has undergone a year of testing.

The project was in development with the help of Apis Cor and the University of Nantes, who also researched the technology and special materials. The building was 3D printed from mineral-infused fluids that solidify into concrete, forming the structure on-site without any extra assembly works. Standing at 9.5 metres tall with a total area of 640 square metres, the construction process used only local components.

This project is a major turning point in the construction sector. 3D printing technologies in construction will increase the speed of execution and [lead to the] completion of buildings in record time. This will reduce construction costs and contribute to the development of solutions to demographic challenges by reducing the number of construction workers.Dawoud Al Hajri, Dubai Municipality director general

Construction Printing in Dubai

Dubai has already shown a keen interest in construction printing technologies for quite a while now. From various additive manufacturing facilities in the region to companies like Xtree and Concreative setting up new ventures, they have been exploring the possibilities of emerging technologies in construction and civil engineering at a rapid pace.

Largest on-site 3D printed

“The building has been designed with different shapes through which it was tested to show it can be possible for 3D printing to construct a variety of designs,” Mr Al Hajri said. “Dubai’s 3D printing strategy aims to make it a global capital for this technology.”Related StoryXtreeE & Concreative Set Up 3D Construction Venture in UAE

However, one of the issues the region has always had to deal with is harsh weather conditions. Construction 3D printing can have major hurdles depending on the environment. The building in Warsan is also a major milestone for materials development, taking great care to have ones that could withstand the heat and humidity. Aside from that, there are also massive savings benefits for the construction industry.

Officials said construction costs for a building this size would be cut from about Dh2.5 million to less than Dh1m. The process used about half the usual number of construction workers, needing only 15, and generated about 60% less waste. There were also some major time savings in using the 3D printer. The walls took just two days to build, while the rest of the house took a further four months.

Featured image courtesy of Apis Cor.