Reinventing Logistics: Harnessing Generative AI and GPT for Intelligent Document Processing

September 11, 2023


The logistics business has seen a dramatic transition in recent years, contending with complicated supply networks, vast amounts of data, and extensive paperwork. The incorporation of cutting-edge technologies such as Generative AI and GPT (Generative Pre-trained Transformer) into intelligent document processing systems is one of the most exciting developments on the horizon. This strong combination has the ability to transform how logistics organizations operate, reducing operations, improving accuracy, and ultimately providing better services to their consumers. We'll look at how Generative AI and GPT can revolutionize intelligent document processing in logistics in this blog article. This new approach's major components, problems, and prospective applications will be discussed. 

As we all know, transportation and logistics (T&L) operations create a plethora of papers, ranging from invoices to delivery notes, which are frequently slowed down by time-consuming manual procedures. This is especially true in the field of third-party logistics (3PL). Inefficient document handling causes not only problems in logistics operations, but also financial losses and disagreements between shippers and carriers. The lack of proper document management affects dispute resolution for transport and logistics stakeholders even more. Furthermore, with each typical load requiring 4-6 documents on an average, document processing is crucial in the load fulfillment process. The time-consuming job of certifying and archiving these records for future use adds to the administrative strain. Given the sheer amount of papers moving through businesses and sectors, the desire for a long-term solution to document management issues becomes clear. Generative AI and Language Models (LLMs) have taken center-stage in this, driving document processing into the domain of Intelligent Document Processing (IDP). This game-changing strategy promises to elevate the administration of massive document collections while drastically lowering the requirement for human participation.

I. The Imperative for AI in Document Processing

The need for AI in document processing within the Transportation and Logistics (T&L) sector is undeniable. T&L operations generate a multitude of critical documents, from invoices to delivery notes, and the manual handling of these documents is not only time-consuming but also error-prone, leading to potential financial losses and disputes. The complexity is exacerbated in third-party logistics (3PL) scenarios. With typical loads involving several documents, the burden of validating and storing them for future reference becomes a labor-intensive challenge. 

Intelligent Document Processing (IDP), driven by Generative AI and Language Model capabilities, emerges as a sustainable solution to address these woes. It promises to streamline document management by significantly reducing human intervention while ensuring accuracy and consistency in data handling. In an era where vast volumes of documents flow across industries, IDP stands as a beacon of efficiency, revolutionizing how T&L stakeholders handle their critical paperwork and enhancing overall operational performance.

The Transformative Power of Generative AI in Transportation and Logistics

Generative AI is a special kind of artificial intelligence that can create entirely new data, even though it's based on what it has learned. This technology, along with the rise of Large Language Models (LLMs) like GPT (Generative Pre-trained Transformer), is poised to bring big changes to various industries, including transportation and logistics. For companies in logistics (especially third-party logistics or 3PLs), Generative AI and GPT offer exciting possibilities. They can help automate tasks that are repetitive, make smart decisions based on data, and provide better service to customers. GPT, in particular, is super smart because it can learn from a huge amount of information, which means it can answer all sorts of questions accurately. GPT-3, one of these smart models, has already made a big impact in the world of AI, and it's likely to keep driving new ideas and progress in the future. So, there's a lot of potential for AI, including models like ChatGPT, to bring some really great benefits to the transportation and logistics industry. 

II. Generative AI in Logistics

An area of artificial intelligence known as generative AI focuses on developing models capable of producing material that resembles human-like creativity. In the area of logistics, generative AI algorithms can be critical in managing unstructured data such as handwritten papers, invoices, purchase orders, and bills of lading.

1.  Optical Character Recognition (OCR) and Generative AI

OCR technology, which has been in use for decades, scans printed or handwritten text and converts it into machine-readable text. However, OCR has limitations when dealing with complex documents, especially those containing handwritten notes or non-standard fonts. This is where generative AI can step in. Generative AI algorithms, often powered by DL techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can be trained to enhance OCR accuracy by deciphering messy or ambiguous text. These models can analyze contextual clues, patterns, and handwriting styles to improve the recognition accuracy of OCR systems.

2. Data Augmentation and Data Synthesis

Generative AI can also generate synthetic data for training and testing purposes. In logistics, this capability is invaluable in creating diverse datasets that reflect the variability of real-world documents. By training AI models on this synthesized data, they become more robust and adaptable to different document styles, languages, and formats.

III. GPT: The Power of Natural Language Understanding

GPT is a cutting-edge language model created by OpenAI; it is renowned for its natural language processing capabilities. GPT models have been pre-trained on massive volumes of text data, allowing them to interpret and create human-like prose. In the logistics sector, GPT can enhance intelligent document processing in several ways.

1.  Contextual Understanding

Logistics documents often contain complex terminology and industry-specific jargon. GPT's contextual understanding allows it to interpret such documents with a high degree of accuracy. It can extract key information from documents like shipping manifests, customs declarations, and bills of lading, providing a deeper understanding of the logistics process.

2.  Multilingual Support

The logistics industry operates on a global scale, necessitating support for multiple languages. GPT models, being trained on diverse text sources, excel at language translation and multilingual document processing. This capability ensures that logistics companies can handle documents from various regions seamlessly.

IV. Augmenting Document Classification and Data Extraction

Intelligent document processing in logistics relies heavily on document classification and data extraction. Traditional rule-based systems struggle with the variability and complexity of logistics documents. Generative AI and GPT can overcome these challenges.

1.  Document Classification

Generative AI can be used to develop advanced document classification models. These models can automatically categorize incoming documents into different types, such as invoices, purchase orders, or shipping instructions. By utilizing GPT's natural language understanding, these models can adapt to the specific needs of a logistics company and improve classification accuracy over time.

2.  Data Extraction

Data extraction involves retrieving structured information from unstructured documents. GPT can aid in this process by recognizing patterns, key data points, and relationships within documents. With the assistance of generative AI, data extraction models can handle variations in document formatting, enabling logistics companies to extract critical information like tracking numbers, product descriptions, and shipping addresses more efficiently.

V. Document Summarization and Contextual Analysis

Another area where Generative AI and GPT shine is in document summarisation and contextual analysis. Logistics professionals often deal with lengthy reports, contracts, or technical documents. GPT can be employed to summarize these documents, extracting the most pertinent information and presenting it in a concise format. Moreover, it can perform contextual analysis to identify trends, anomalies, and potential risks within the logistics data, aiding decision-making processes.

VI. The Document Processing Market: A Lucrative Opportunity

The document processing market is witnessing substantial growth, driven by the increasing adoption of automation technologies across various industries, including logistics. Below are key statistics that highlight the market's size and potential:

1.  Market Size and Growth

As of 2021, the global document processing market was valued at approximately $42.2 billion. From 2021 to 2026, the market is expected to increase at an 11% CAGR, projected to reach a value of $76.7 billion.

2.  Automation Adoption

The adoption of document processing solutions, including AI-powered intelligent document processing, is on the rise. It is estimated that around 45% of enterprises have already implemented some form of document automation in their operations.

3.  Industry-Specific Adoption

Within the logistics sector, the adoption of intelligent document processing is gaining momentum. Market reports indicate that logistics companies are increasingly investing in automation technologies to optimize their document-intensive processes.

4.  Geographical Trends

North America and Europe are currently leading in terms of market share for document processing solutions. The Asia-Pacific area, on the other hand, is predicted to develop at the fastest rate due to the rapid digital transformation and increasing demand for efficient document management in emerging economies.

5.  Key Players

Major players in the document processing market include IBM, Microsoft, Adobe Systems, ABBYY, and OpenText, among others. These companies are actively investing in AI and machine learning to enhance their document processing capabilities. 

VII. Challenges and Considerations

While the integration of Generative AI and GPT holds tremendous promise, it is not without challenges and considerations:

1.  Data Privacy and Security: Logistics documents often contain sensitive information, and to safeguard this data from illegal access or breaches, effective data privacy and security procedures must be in place.

2.  Model Training: Training generative AI and GPT models requires substantial computational resources and high-quality labeled data. Logistics companies must invest in infrastructure and data labeling to leverage these technologies effectively.

3.  Interpretability: Understanding how these AI models arrive at their decisions is critical, especially in applications involving regulatory compliance or legal matters. Ensuring model interpretability is an ongoing challenge.

4.  Real-Time Processing: Logistics operations often require real-time document processing. Optimizing AI models for speed and scalability is essential to meet these demands.

VIII. Applications and Future Prospects

The fusion of Generative AI and GPT with intelligent document processing has far-reaching applications in logistics:

1.  Supply Chain Optimization: AI-driven document processing can enhance supply chain visibility by providing real-time insights into inventory, demand, and logistics performance.

2.  Customs and Compliance: Streamlining customs documentation, tariff code classification, and compliance checks can significantly reduce delays and errors in international shipments.

3.  Customer Service: Intelligent document processing can improve customer service by expediting query resolution and providing customers with accurate and up-to-date information.

4.  Cost Reduction: By automating document-related tasks, logistics companies can reduce operational costs, minimize errors, and improve overall efficiency.

5.  Predictive Analytics: The rich data generated from intelligent document processing can be leveraged for predictive analytics, enabling logistics companies to anticipate demand, optimize routes, and mitigate risks.

In the future, we can expect continued advancements in Generative AI and GPT, making them even more integral to logistics operations. Additionally, the integration of these technologies with IoT devices may further enhance transparency and security across the supply chain.


The integration of Generative AI and GPT into intelligent document processing is poised to revolutionize the logistics industry. These technologies offer enhanced accuracy, multilingual support, and the ability to handle complex, unstructured documents with ease. While challenges like data privacy and model interpretability persist, the potential benefits are too substantial to ignore. Logistics companies that embrace these innovations will not only streamline their operations but also gain a competitive edge in an increasingly complex and interconnected global marketplace. The future of logistics is here, and it's driven by the power of Generative AI and GPT.

Latest Blogs
This is a decorative image for: A Complete Guide To Customer Acquisition For Startups
October 18, 2022

A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

  • Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
  • Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

Reference Links

This is a decorative image for: Constructing 3D objects through Deep Learning
October 18, 2022

Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

  • Highly calibrated cameras that take a photograph of the image from various angles.
  • Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

  • Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

Reference Links

This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts
October 18, 2022

A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links

This is a decorative image for: GAUDI: A Neural Architect for Immersive 3D Scene Generation
October 13, 2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

  • The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
  • Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
  • GAUDI also removes GANs pathologies like mode collapse and improved GAN.
  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

Reference Links

Build on the most powerful infrastructure cloud

A vector illustration of a tech city using latest cloud technologies & infrastructure