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Transforming Data into Decisions: The Deep Data Insight Way

Artificial Intelligence

Transforming Data into Decisions: The Deep Data Insight Way

21 August 2025

What Makes Artificial Intelligence Essential for Modern Businesses? Artificial Intelligence is no longer futuristic—it’s the driving force behind AI-powered business solutions today. At Deep Data Insight (DDI), AI isn’t just about algorithms; it’s about building AI-powered ecosystems that simplify complexity, empower professionals, and accelerate growth. Every solution ensures that data turns into data-driven decision-making with real-world outcomes. How Does Deep Data Insight Follow a Human-First Innovation Model? Unlike many technology providers, DDI leads with human-first innovation. Their mission is simple yet powerful: building solutions that solve real challenges. Whether it’s artificial intelligence in healthcare to decode medical records, AI recruitment solutions to help recruiters evaluate candidates, or predictive insights for finance leaders—DDI focuses on impact, not just capability. Each product is designed with meaning, ensuring businesses can rely on AI to solve actual problems while driving measurable results. How Does DDI Turn Concepts into Real AI Solutions? Deep Data Insight follows a structured project lifecycle that balances creativity with precision: This approach ensures every project moves seamlessly from strategy to execution, producing sustainable outcomes. What Innovative Products Define Deep Data Insight? DDI brings innovation to life through cutting-edge platforms and business intelligence tools: Why Do Businesses Choose Deep Data Insight? Organizations partner with DDI for clear, measurable advantages: These strengths make DDI a trusted partner for enterprises seeking advanced AI-powered business solutions. What Do Clients Say About Deep Data Insight? Long-term partners frequently describe DDI as an extension of their own team. From automating complex workflows to overcoming data bottlenecks, clients highlight the company’s ability to deliver high-impact solutions that align with strategic business goals. What Is the Future of AI and Data-Driven Decision Making? As data volumes surge, the real challenge isn’t collection—it’s transformation into intelligence. DDI is committed to shaping that future. By combining business intelligence platforms, advanced analytics, and scalable AI, Deep Data Insight empowers organizations to thrive in a data-driven decision making environment. FAQ’s

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AI-Powered Insights: How Businesses Are Leveraging Machine Learning

14 August 2025

In an era where data drives decisions, AI-powered insights are transforming how businesses operate. Machine learning (ML) has evolved from a niche technology to a strategic cornerstone across industries—from eCommerce retailers predicting consumer behavior to financial firms detecting fraud in real time. Understanding how businesses are leveraging machine learning isn’t just insightful—it’s essential for staying competitive.  What Are AI-Powered Insights and Why Do They Matter for Businesses? AI-powered insights refer to predictions, patterns, and recommendations generated by algorithms that learn from historical and real-time data. These insights matter because they enable businesses to act proactively—identifying risks, opportunities, and customer needs before they manifest. Consider how Netflix uses recommendation systems to suggest shows, increasing viewer engagement and retention. Using collaborative filtering and deep learning, Netflix reportedly achieves a 75% lift in content consumption thanks to personalized recommendations. That’s a tangible outcome: more time on platform, higher satisfaction, better retention. Equally, Amazon leverages machine learning to optimize inventory and recommend products dynamically, boosting purchases and streamlining operations. In short, AI insights empower businesses with foresight, efficiency, and personalization—driving measurable ROI. Which Sectors Stand to Gain the Most from Machine Learning? Retail and eCommerce Retailers and eCommerce companies harness ML for demand forecasting, dynamic pricing, and customer segmentation. For example, fashion retailer Zara uses real-time sales data and demand prediction models to replenish trending items, reducing overstock and markdowns. A company like Stitch Fix employs machine learning algorithms that consider customer preferences, fit, and style to curate personalized clothing selections. This lowers return rates while simultaneously increasing customer satisfaction—a win-win situation. Finance and Banking In finance, ML models detect fraudulent transactions by analyzing behavioral patterns and anomalies. A typical credit card fraud detection system flags suspicious activity within milliseconds, preventing losses. Additionally, robo-advisors use ML to construct personalized investment portfolios based on risk tolerance and market trends, handling thousands of customer profiles simultaneously, with accuracy that rivals human advisors. Healthcare and Life Sciences Healthcare benefits from predictive diagnostics and patient risk scoring. ML algorithms analyze electronic health records (EHRs), wearable data, and genomic sequences to identify early signs of conditions like sepsis or diabetes. One hospital system reduced ICU admissions by 20% by early detection of patient deterioration using ML-powered alert systems. How Do Businesses Implement Machine Learning? A Step-by-Step Guide Step 1 – Identify Strategic Use Cases Implementation starts with selecting use cases that align with business goals: reduce churn, increase upsell, automate processes, or personalize services. You can think of each use case as a lever—pinpoint which lever yields the best outcomes with least complexity. Step 2 – Gather and Prepare Quality Data Data is the fuel for ML. Businesses must gather, clean, and label data from CRM systems, log files, customer feedback, and external APIs. An analogy: building ML models without proper data is like trying to bake a cake without measuring ingredients—results will be inconsistent or fail. Step 3 – Choose the Right Model and Tools Depending on your use case, you might use supervised models (like regression, classification), unsupervised models (like clustering for customer segmentation), or reinforcement learning (for real-time bidding systems). Toolsets like TensorFlow, PyTorch, or AutoML platforms such as Google’s Vertex AI or AWS SageMaker make model training accessible even to non-experts. Step 4 – Train, Validate, and Iterate Training uses historical data to teach the model; validation tests the model on unseen data; and iteration fine-tunes hyperparameters. A practical example: in churn prediction, the model might flag high-risk customers; after validation, teams may adjust features such as purchase frequency or engagement metrics to improve accuracy. Step 5 – Deploy and Monitor Continuously Deployment embeds the model in production environments—via APIs, dashboards, or embedded systems. Monitoring for data drift—where incoming data patterns change—and performance decay is equally essential. Setting up automated retraining pipelines ensures models stay accurate over time. What Real-World Examples Illustrate ML in Action? Predictive Maintenance in Manufacturing Think of a factory where machines are monitored by sensors capturing temperature, vibration, and operational metrics. ML models predict when a machine is likely to fail, allowing proactive maintenance. In one case, a manufacturing firm reduced unplanned downtime by 30%, saving millions in production losses. Chatbots and Customer Service Automation Customer service teams in industries ranging from telecom to travel extensively use AI-powered chatbots. These chatbots, powered by natural language understanding (NLU), resolve tier-one queries such as balance checks or booking changes, cutting handling time by 40%. Escalation to human agents only occurs for complex issues—driving both efficiency and satisfaction. Personalized Marketing Campaigns By analyzing behavioral data like email interactions, website clicks, and past purchases, marketing teams run ML-driven segmentation that defines high-conversion audiences. Case in point: a travel agency used ML to recommend packages based on browsing history and social data—tripled click-through rates and maximized campaign ROI. How Can Small and Medium Businesses (SMBs) Leverage ML Without Big Budgets? SMBs often assume ML is out of reach, but “AI insights for SMBs” and “business machine learning use cases” show otherwise. Cloud platforms offer affordable, managed AutoML services that require no in-house data science teams. For instance, a local eCommerce store used Google AutoML Tables to predict top-selling products, increasing revenue by 15% in 3 months. These services also provide templates—like churn prediction or lead scoring—so SMBs can launch proof-of-concept projects quickly and economically. What Are Key Metrics to Measure ML Success? Understanding the impact of ML requires tracking meaningful KPIs. For classification tasks (e.g., fraud detection), precision, recall, and area under the ROC curve (AUC) matter. In regression tasks like demand forecasting, mean absolute error (MAE) or root-mean-square error (RMSE) helps quantify accuracy. Beyond model metrics, business outcomes such as uplift in conversion rates, reduction in churn, or cost savings from automated workflows evaluate ROI. For example, an insurer using ML for claims triage reduced claim resolution times by 25%, resulting in happier customers and lower labor costs. What Challenges Do Businesses Face When Adopting ML? While the benefits are compelling, businesses face common hurdles like data quality issues, model interpretability, and scaling challenges.

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AI Based Wound Assessment Tool

WoundCareAI – Transforming Wound Assessment with AI-Powered 3D Analysis

3 April 2023

Wound detection is an essential aspect of wound assessment that enables healthcare professionals to determine the severity of tissue damage and provide the appropriate course of treatment. However, traditional wound depth detection methods can be prone to subjective measurements and errors. Fortunately, advancements in artificial intelligence (AI) and synthetic data generation have revolutionized the field of wound depth detection. Traditional wound depth detection methods One of the main challenges of traditional wound depth detection methods is the subjectivity of the measurements. In some cases, healthcare professionals use visual inspection to estimate the depth of a wound, which can lead to measurement inconsistencies. Other methods, such as probes or ultrasound, can be invasive, time-consuming, and expensive. Synthetic data generation Researchers have turned to AI and synthetic data generation to overcome these challenges to develop more accurate and efficient wound depth detection methods. Synthetic data can mimic real-world scenarios, which can help overcome the challenges associated with real-world data. Different tools, such as SculptGL for creating a wound, Paint3D for improving generated synthetic data, and Online 3D Viewer for measuring the angled distance from skin to the deepest point of the wound and for measuring the horizontal length between the two points, are used. Using synthetic data can significantly reduce the time, cost, and privacy concerns associated with data collection while allowing machine learning models to detect wound depth accurately. AI algorithms learn to detect wound depth by analyzing images of wounds and correlating wound features with the known depths of similar wounds. Synthetic data can simulate wounds of varying depths, shapes, and sizes, enabling machine-learning models to recognize patterns indicative of different levels of wound depth. DDI Team, has developed an AI system using 3D synthetic data similar to real wound images. The system uses a convolutional neural network (CNN) to detect wound depth from images. The team trained the CNN using a dataset of 3D synthetic wound images generated with SculptGL and Paint3D. The synthetic images were designed to simulate wounds of varying depths, shapes, and sizes. Using AI and synthetic data generation in wound depth detection has several benefits. First, it can reduce the subjectivity and errors associated with traditional wound depth detection methods. Second, it can save time and reduce costs related to data collection. Third, it can enable healthcare professionals to make more accurate and timely decisions regarding wound care, leading to better patient outcomes. The use of AI and synthetic data generation has revolutionized the field of wound depth detection. The method allows for more accurate and efficient wound depth detection, which can lead to better patient outcomes. The development of the AI system by the DDI Team using synthetic data and machine learning is an excellent example of the potential of this technology in the healthcare field. As AI advances, we can expect to see more innovative solutions to healthcare challenges.

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Large Language Models: Huge progress in Artificial Intelligence

20 December 2022

Large Language Models – An overview Large Language Models are offering numerous advantages for organisations who want to use AI to bring efficiencies to their workflows. But this has not always been the case. For the last few years, language-based Artificial Intelligence models have come to the forefront of Natural Language Processing (NLP). In simple terms, a language model can be derived as a probability distribution over a sequence of words. It assigns a probability to a piece of unseen text, based on some training data. The capabilities of a language model vary from simple analytical tasks such as sentiment analysis, spell checking, translations between languages etc. to more advanced features; question and answering, speech recognition, text summarization, semantic search and many more. Voice assistants like Siri, Alexa, Google Translator, and search engines like Google Search, and Bing are the biggest and most familiar examples that showcase the power of language models. As a result of continuous research, academic institutions, and big tech companies such as OpenAI, Microsoft and Nvidia have come up with more improved versions of these simple language models. They are building more intelligent systems with a richer understanding of language extending the capabilities of existing models. So, these latest high performing language models are now called large language models (LLM). As the name reflects these models are larger in sheer size. Firstly, the enormous amounts of data (covering different styles, such as user-generated content, news data and literature which amounts to billions) on which they have trained. Also, the wide range of tasks which can be applied to create smarter platforms with a greater processing speed. The most fascinating thing about the LLM is that with large models there is no need to start from scratch, train and finetune with costly clusters of servers. Large models are capable of recognizing things that haven’t explicitly been seen during training (zero-shot scenarios) or use with fine-tuning, based on a specific domain with a minimal amount of data (few-shot scenarios). Recently, companies like Nvidia, Microsoft, and Open AI have taken steps to release API access to these LLMs making them accessible and affordable for everyone. Not only that, the usages of LLM have grown dramatically and the models are able to question and answer with tabular data, content generation, image generation, code completion and more. Among LLMs, BERT and GPT can be pointed out as the most capable and easily accessible model families in the market. Large Language Models: BERT Bidirectional Encoder Representations from Transformers (BERT) is a transformer language model developed by Google. BERT was one of the first solutions to dominate the market with transformer architecture in 2018. Amongst many applications, BERT is able to: Interestingly, unlike many other LLMs, BERT is open source, allowing developers to run models quickly without spending fortunes on development. The variants of BERT (SpanBERT, DistilBERT, TinyBERT, ALBERT, RoBERTa and ELECTRA) are special versions which are intelligently optimized to outperform the drawbacks of BERT. Large Language Models: GPT The Generative Pre-Trained Transformer (GPT) model was first introduced in 2018 by OpenAI. The powerful performance of the model with few or zero unlabeled data based on Autoregressive Transformer language intrigued the NLP community at that time. After the initial release, further iterations appeared. GPT-3 appeared in 2020 and was the highest performance LLM at the time, being 1,000 times the size of GPT-1. GPT3 can perform: This is a version which can be used even without fine-tuning the model. GPT-Neo, GPT-J, and GPT-NeoX are the other variants of this family that were trained and released by EleutherAI. Open-source versions of GPT-3 which are released by Open-AI now available for affordable prices. Google researchers in February 2022 published a model far smaller than GPT-3, called Fine-tuned Language Net (FLAN) which beats GPT3 on a number of challenging benchmarks. It outperformed GPT-3 on 19 out of the 25 tasks as well as performance on 10 tasks. Large Language Models: In Conclusion The community of Large Language Models is unstoppable. Their capability is proven and has proven benefits. Use of LLMs is widespread and mainstream. They are extremely accessible since they exist increasingly as open-source solutions. LLMs continue to evolve with new research and technology. Large Language Models and Deep Data Insight Deep Data Insight have been at the forefront of using LLMs to transform their customers’ experiences. Key benefits are to the efficiencies of workflows, where high-cost manual time is replaced with an LLM model. DDI’s emerging ‘Document AI’ platform will ensure that all data extracting functions will be available through a single platform. With Document AI, LLMs are used to extract data from a given document using the LLMs Question and Answering feature. This means that the whole document does not have to be reviewed – instead just ask a question to find and extract the relevant documented information. This can even be used to find and extract data from tables in CSV or Excel files. The platform will also allow a user to search across a variety of documents to find the one that is most relevant for any given keyword. DDI are active in a number of sectors, including healthcare, real estate and insurance. These are all sectors that Platform AI will benefit with its pioneering application of LLMs. For information about how we are successfully helping our clients achieve amazing ROI in their workflows, take a look at our case studies here: https://www.deepdatainsight.com/case-studies/

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Using Artificial Intelligence for table detection in documents

20 December 2022

Introduction AI is capable of table detection in documents. As the world becomes increasingly digitized, we are all feeling the benefit of having our documentation available online. Whether we are individual consumers, or big businesses, these advantages are palpable. Benefits include: As we have seen in previous posts, Deep Data Insight have worked on numerous projects where the introduction of Artificial Intelligence into workflows has brought enormous efficiencies. However, whilst this process is relatively straightforward for text, it is much harder when it comes to tabulated information. Recently though DDI have pioneered the art of table detection in documents. Table detection in documents: The Challenge For centuries, humans have used tables as a way of comparing data and analysing for trends. This evolved in the 20th century when programmers introduced the world’s first spreadsheets. Having data represented in table format means that it can be understood relatively intuitively, and can be the basis for in-depth interrogation with, for example, graphical and figurative interpretations. The 21st Century has seen enormous advances in the use of Artificial Intelligence and machine learning to understand and predict text. The two main technologies are ICR (Intelligent Character Recognition) and OCR (Optical Character Recognition). ICR and OCR enable a computer to digitise information accurately and quickly. PDFs are the pre-eminent solution to presenting documents such as invoices and receipts. Electronic Source PDFs will be digitised from the outset; these are also known as ‘Native’ PDFs. ‘Scanned’ PDFs will have started life as a physical document, captured by a mobile device. The issue arises however when tabulated information is included in documents such as PDFs. This is because the information is no longer being represented in a way that AI can be programmed to understand, and table structure will vary tremendously from one table to another. This is becoming more of a pronounced challenge as we become increasingly reliant on our own mobile devices. We use their cameras to capture information, and increasingly to convert these images into usable data. However, because tables by their nature are problematic to AI, table detection in documents has long been a real challenge, and many non-tech industries will still rely on manual processes for extracting and recreating tables in their documents. This is labor intensive and therefore costly and prone to error. This is largely a sector-agnostic challenge. It follows though that where an increased number of tables are included into the mix of information to be used, then the issue will be more prevalent. Such sectors include engineering, science and academia, FMCG and others. Table detection in documents: The Solution Deep Data Insight have created a set of smart solutions using Artificial Intelligence for table detection and data extraction from any type of document. This means that the process of digitising and therefore streamlining workflows need not be held up if the data involved includes tabulated information. Key to these solutions are two open source technologies to which DDI connects through APIs: Tensorflow and Keras Tensorflow is an open-source software library that has been created as a learning and development resource for programmers, specifically those involved in machine learning and artificial intelligence. Keras is another open-source resource which provides a python interface for artificially created neural networks. By using Tensorflow and Keras, Deep Data Insight can accelerate model building and the creation of scalable machine learning solutions. As well as these two back-end technologies, DDI employ their significant expertise with a deep learning model known as CNN – Convolutional Neural Network – to analyse the visual imagery. A novel CNN model developed with pre-trained VGG-19 features Optical Character Recognition (OCR) is used in order to extract table data accurately. DDI have years of experience with OCR, as this technology underpins its successful EDDIE product. Table detection in documents: The Results The first thing to understand is that the huge generic advantages of digitising data are already being gained by Deep Data Insight customers. They are experiencing enormous cost savings across their multiple workflows. However, since DDI now also has a set of solutions for table detection in documents, and their extraction from varying and multiple documents, these savings be further increased and provided by a single supplier – DDI. These benefits are sector agnostic, and can be as follows: DDI is now supporting its clients across many sectors that are heavily reliant on tabulated information. Insurance, where individual documents needs processing; Construction, where agreement documents often contain tables and in healthcare, where tables are often found in medical prescriptions. Next Steps As with any type of technology, table detection in documents is already evolving quickly, and Deep Data Insight are at the forefront of this evolution. In the not-too distant future, we will see this technology being applied to any object detection problem such as video surveillance and anomaly detection in healthcare. Notes Document AI is a Deep Data Insight product developed over years by our data scientists and using the latest deep learning and OCR technologies. For more information about our client successes, read our case studies here https://www.deepdatainsight.com/case-studies/

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ARTIFICIAL INTELLIGENCE AND WORKFLOWS: Bringing efficiencies to workflows using AI and Machine Learning

2 December 2022

Artificial Intelligence and Workflows: Introduction Deep Data Insight works strategically with a client based in California who specialise in finding commercial properties for their clients in the healthcare sector. Their client works on a large-scale; they have around 50,000 properties within their portfolio at any time and work on both a purchase and lease basis. The client is a successful and growing business; they are adding hundreds of new properties to their portfolio every month. Deep Data Insight has created an Artificial Intelligence factory that produces bespoke and licensable AI solutions for organizations from almost any sector. Artificial Intelligence and workflows has long been a key focus for DDI. One of their modular platforms, EDDIE, uses Optical Character Recognition and Intelligent Character Recognition technologies to provide huge ROI for companies that have medium to large amounts of data to process. Artificial Intelligence and Workflows: Challenge In short, DDI’s client faced a workflow challenge. Their existing processes were manual, with information scattered over a number of files and locations. They were experiencing a lack of efficiency and occasional mistakes. And since their client base covers numerous and disparate locations, the task of bringing this all into one place was vast. In workflow terms, the challenge was a combination of digitizing documents, checking for errors and duplication, consolidating information from a variety of sources and finally extracting important information from less important information documents. The data was in handwritten, typed and picture format.  Artificial Intelligence and Workflows: Solution Deep Data Insight deployed their EDDIE platform to provide solutions to four specific areas: One way in which Artificial Intelligence and Workflows come together is with address matching. At any one time, the client holds a huge repository of building addresses which are uploaded onto a series of spreadsheets by a team of researchers. Different researchers can potentially enter the same address multiple times, so these require cross-referencing and master indexing to ensure duplicates are removed and there is one unique, accurate entry per building.  To complicate matters further, the client is also in charge of a list of around 3,000 physicians that will move between institutions. In fact, the value of the properties can depend on the number and seniority of physicians within a building, so, the master index is a live and moving thing!  EDDIE solves this problem by pulling data from the client’s servers, processing and cleansing the data before sending it back in a master index to the client. A master index can be retained by Deep Data Insight if required. Obviously, security is of the highest priority, so the Deep Data systems are completely secure, as is the process of transferring data. The technologies involved in this solution are exact matching, parsing, deduplication and master indexing. Since EDDIE is phenomenally quick, the ROI is impressive for the client. EDDIE can run an entire cycle for around 300,000 buildings within 5-10 minutes.           2. Offering Memorandums A critical part of what Deep Data Insight’s client does is to produce marketing brochures for their properties. These are around 15-20 pages in length and cover all aspects of the building, including environment, utilities, condition etc. They are all typed by a person in the first instance. Since this client works on such a large scale, hundreds of brochures are being produced each month.  The challenge comes from the fact that all of the information needs to be accurate and de-duplicated. The process improvement comes from extracting the important data which will be in amongst less important, marketing information. The client’s database needs to include key information every time for every property – square footage, location, rental terms etc. Without EDDIE, this is a time consuming process that is prone to errors. EDDIE uses AI to go through the whole brochure to pull out the 20 or so pieces of information that are going to be valuable. This was previously done by staff members by reading each document, which had been time consuming.  It will also go through an address matcher since the same brochure might come in from multiple sources. Once OCR and ICR have been used at the start of the process, the technology being used is string matching and a deep learning language model; if this cannot be applied, then EDDIE will trend the model by Question and Answer. Since the EDDIE can work away in the background, the client will start processing the brochures overnight, so that at the start of the working day, everything is ready, saving at least one person’s salary.           3. Transferring ‘Underwriting files’ onto a central database Another way that Artificial Intelligence and Workflows conflate is when transferring data. The client produces thousands of underwriting files every month. These are pre-filled excel sheets, manually completed. They will vary in their make-up; sometimes cells are merged…some contain pictures. DDI’s client needs all of these files moving from their archive onto a central database, so that they are more accessible in the future and can be searched globally. Naturally, not all the information contained is required…the salient information needs identifying, extracting and digitizing. This is what EDDIE does, using Logic models. EDDIE will process each sheet including all tabs completely within sixty seconds. This provides enormous ROI to a process that would have taken hours for a human to complete.           4. Lease files Every Lease File that the client accesses is a complicated legal document of up to 200 pages in length. They contain a massive amount of superfluous information for the client’s purposes and are therefore impossible to search quickly. In fact, within each full document there are around 22 fields that actually need extracting. Imagine having to review the whole document for that one piece of critical information, for example what are the building’s boundaries. Even though they are legal documents, there are still many different styles. EDDIE processes the entire document, and extracts only the salient information using string matching technology and a deep

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Artificial Intelligence in Sports

Artificial Intelligence in Sports

29 June 2022

Why technology needs to be the first name on your team sheet

As humans, we innately love sports. It is thrilling, dramatic and speaks to our inborn combative nature. It divides cities, yet unites countries. It provides an entire vocabulary that transcends borders. Games and matches can be won or lost by the merest of fractions. Every sporting event is a script whose ending is not yet written.

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Deep Fake image, Artificial intelligence, face recognition and image processing

What is Deep Fake and should we be worried?

30 March 2020

The joining of ‘deep learning’ and ‘fake news’ makes it possible to create audio and video of real people, saying words that they never spoke or things they never did and it is a form of Artificial Intelligence.Getting machines to do this is not an easy task, and it involves skills from different field of knowledge such as Computer Science, Statistics, etc.

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