smartextract.ai’s Post

𝐒𝐭𝐫𝐮𝐠𝐠𝐥𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐈𝐧𝐚𝐜𝐜𝐮𝐫𝐚𝐭𝐞 𝐃𝐚𝐭𝐚 𝐄𝐧𝐭𝐫𝐢𝐞𝐬? Do you still do manual data entry at your company ? Then listen up. Have you ever finished entering data from an email or PDF—a purchase order, for example—into your system and wondered if you got it right? It’s nerve-wracking. Did you type 30 Euros or 30,000.00 Euros? Let’s face it—errors can happen and slip through the cracks, especially when everything is done manually. But it can be costly in some instances. smartextract ensures you don’t have to worry about that. Our AI not only extracts data from documents but also validates it before transferring it to your system. With our effortless data review feature and matching algorithm, you’re on the safe side. Confirm and edit extracted data directly alongside the original documents within our UI if necessary, reducing errors and ensuring everything is accurate before further processing. Be on the safe side. Use smartextract to eliminate manual data entry errors. 𝐓𝐞𝐬𝐭 𝐟𝐨𝐫 𝐟𝐫𝐞𝐞 𝐭𝐨𝐝𝐚𝐲: https://lnkd.in/e-rYpWj5 #management #startups #AI #technology #machinelearning #LLM #innovation #education #freetrial

Did you know? According to #Forbes, nearly 70% of organizations have made a significant business decision with inaccurate financial data. As the backbone of decision-making, data quality can either make or break your enterprise. But here's the twist: Artificial Intelligence is transforming the way we manage and improve #dataquality. No longer are we shackled by manual processes; #EntepriseAI is stepping in to ensure our data is accurate, consistent, and reliable. In this post, we'll dive into how #Enterprise AI is revolutionizing data quality management, turning what was once a massive challenge into a streamlined, efficient process. Let’s explore how AI is setting a new standard for data excellence! Enterprise AI is revolutionizing data quality management by contributing in the following ways - Data Cleansing and Enrichment: AI algorithms can identify and correct errors, fill in missing data, and enrich datasets with additional information. For instance, machine learning models can analyze patterns in customer data to predict and fill in missing values, ensuring datasets are complete and useful. - Anomaly Detection: Machine learning models can detect anomalies and inconsistencies in data, flagging potential issues before they escalate. This is particularly valuable in industries like finance, where detecting fraudulent transactions early can save millions of dollars. - Predictive Analytics: AI can predict future data quality issues by analyzing trends and patterns, allowing businesses to proactively address potential problems. For example, in supply chain management, AI can predict delays or disruptions based on historical data and current conditions, enabling companies to adjust their plans accordingly. - Automated Data Matching and Deduplication: AI can automatically match and deduplicate records, ensuring that each entry is unique and accurate. This is crucial for maintaining clean customer databases in marketing and sales operations. Curious to read more about the importance of data quality to enterprises? Click on the link below: https://lnkd.in/gEF-JpWF #DatalensAI #EnterpriseAI #DoMoreWithData #DataQuality #DigitalInnovation

🌟 Building Safe & Trustworthy AI: nasscom's Developer Playbook 🚀 💡 Core Philosophy: • AI development isn't just about capability - it's about responsibility 🎯 • Safety by design, not afterthought 🛡️ • Transparency builds trust 🤝 • Proactive risk management > reactive fixes ⚡ 🔍 The How-To Guide: 1. Risk Assessment Framework: 📊 For each AI type (Discriminative/Generative/Applications): - Pre-deployment safety checks ✅ - Bias detection protocols 🎯 - Security vulnerability testing 🔒 - Impact assessment templates 📝 2. Implementation Roadmap: 🗺️ 🧠 Conception: • Start with "Why" - Define clear purpose & impact • Map stakeholders & risks • Set measurable safety metrics 📊 Data Management: • Privacy-first architecture • Quality validation frameworks • Security-by-design principles 🛠️ Development: • Regular bias checks • Continuous security testing • Documentation as you build 🚀 Deployment: • Phased rollouts with feedback loops • Real-time monitoring • Clear incident response plans 💫 Practical Steps for Teams: 1. Start Small: 🌱 - Begin with pilot projects - Build safety frameworks - Test & iterate 2. Document Everything: 📝 - Use provided templates - Track decisions & changes - Maintain transparency 3. Regular Reviews: 🔄 - Safety audits - Stakeholder feedback - Performance monitoring ✨ Impact: • Reduced risks 🛡️ • Faster market entry 🚀 • Regulatory compliance ✅ • Stakeholder trust 🤝 This isn't just another guidebook - it's your practical roadmap to building AI that's both powerful and responsible! 💫 #ResponsibleAI #AIStrategy #DigitalIndia #TechForGood 🌟

𝗬𝗼𝘂𝗿 𝗔𝗜 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝗵𝗶𝗻𝗴𝗲𝘀 𝗼𝗻 𝘁𝗵𝗶𝘀 𝗼𝗻𝗲 𝘁𝗵𝗶𝗻𝗴... To harness the full potential of AI, 𝗵𝗮𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗱𝗮𝘁𝗮 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗶𝘀 𝗻𝗼𝗻-𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝗯𝗹𝗲. Whether you're scaling your AI efforts or just getting started, the success of your initiatives heavily depends on the foundation you build. But what does an AI-ready data platform really look like? Here are the key requirements that will make or break your AI strategy: 1️⃣ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: 📈 Your platform must handle increasingly complex data workloads without a hitch. If your platform can't scale, your AI won't either. 2️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: 🤖 Implementing AI often means working with massive amounts of data. Automating data workflows ensures you can process and analyze this data quickly and efficiently. 3️⃣ 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆: ✅ AI is only as good as the data it’s trained on. Ensuring high-quality, accurate, and consistent data is crucial for the success of any AI initiative. 4️⃣ 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: 🔗 Your data platform needs to seamlessly integrate with existing systems and tools. This ensures smooth data flow across your organization, enabling better insights and decision-making. Building an AI-ready data platform isn't just about the technology—it's about setting the right foundation to drive real results. Curious to learn more? Dive deeper into the key requirements for an AI-ready data platform in our blog post. https://lnkd.in/gmHwgp34

There will be a point in your AI project where somebody, an engineer, a developer, a conversation designer will ask you: How is this supposed to work? What are the most important contacts you want the bot to handle? What do you want the bot the answer? What are the follow up actions or notes you want the bot to take? In the era of Artificial Intelligence, data is the lifeblood that empowers companies to develop innovative solutions and gain competitive advantages. Without high-quality and sufficient data, it is nearly impossible to fully leverage the potential of AI. Particularly in projects relying on machine learning and AI algorithms, data is the driving force behind model building and optimization. But what data exactly is needed to effectively deploy AI? 1. **Customer Data**: A deep understanding of customers is essential for personalizing offerings, predicting needs, and optimizing interactions. And we are not "simply" talking about things like name and address. Ideally you have first party data like transaction data, transaction and communication history, behavioral data, feedback, and preferences but also meta data like demographic information. These are just some examples of relevant customer data. 2. **Process Data**: Efficient business processes are a cornerstone of every enterprise. By analyzing process data, bottlenecks can be identified, workflows optimized, and costs reduced. Data workflows, supply chains, and production processes are invaluable here. Last but not least the quality of the data in important as well. **Quality Data**: Inconsistent, incomplete, or erroneous data can significantly impair the performance of AI models. Robust data management and quality control processes are therefore essential. A company that recognizes the importance of data for AI projects and invests accordingly lays the groundwork for sustainable success. It's not just about having data, but also ensuring that it is accessible, up-to-date, relevant, and of high quality. In a data-driven world, companies that can leverage their data optimally are the true pioneers. Therefore, organizations considering an AI initiative should first ensure they have the necessary data to achieve their goals. Do you have any experiences or thoughts on this topic? Feel free to share them in the comments! #ArtificialIntelligence #DataAnalysis #AIImplementation #DataDriven #bigdata

AI is a trojan horse that can set up data teams for failure and create substantial risk for companies... While the emergence of LLMs has brought AI to the mainstream once again, leaders need to be cautious about jumping hastily into this fast-moving tech trend. There is tremendous value in AI, and companies that approach it correctly will have outsized gains. But actually enabling an AI transformation within a company is challenging and costly for those who approach it wrong. We believe the largest risk to AI implementations is poor data quality. Specifically, AI quickly transforms your company's data from a resource used for insights to a full-fledged product with end users. While scaling the insights and automations related to your business data is powerful, poor data quality results in being wrong at scale—hence the trojan horse. This doesn't mean companies need pristine data to successfully use AI. Instead, companies need to be intentional about 1) identifying what data assets are driving value (ideally revenue) and will be fed into AI or traditional ML applications, 2) understand the requirements and constraints of effectively using this data, and 3) protect these data assets from failing. Gable's platform can help with automatic data asset detection by directly reading from databases or even the code itself that changes data assets. This feature has been key for our design partners in quickly getting early wins in understanding the data footprint they need to protect. Once data assets are detected, teams can place data contracts to constrain the data to business requirements and then automatically detect any changes to databases or code that will violate the contract. Interested? Sign up for our product waitlist on our website. 🔗 Product Waitlist: https://lnkd.in/gR33Huet #ai #data

The journey from AI/ML model development to deployment is full of challenges. Here are some key hurdles and how to overcome them: 1. 🔍 Data Quality and Quantity: Challenge: Many businesses struggle with incomplete, inconsistent, or biased data. Solution: Implement robust data governance practices, invest in data cleaning and preprocessing tools, and establish a feedback loop to improve data quality continuously. Leveraging synthetic data can also help fill gaps. 2. 🧩 Model Complexity: Challenge: Understanding and managing complex models. Solution: Use simpler models when possible, prioritize explainability, and employ tools that provide interpretability features. Regularly review and validate models with domain experts to ensure alignment with business objectives. 3. ⚖️ Scalability: Challenge: Deploying models at scale. Solution: Invest in cloud-based infrastructure and scalable machine learning platforms. Implement efficient data pipelines. 4. 🚀 Deployment and Integration: Challenge: Integrating models with existing systems and workflows. Solution: Use APIs for seamless integration. Adopt continuous integration and continuous deployment (CI/CD) practices to streamline the deployment process. 5. 🤖 Monitoring and Maintenance: Challenge: Continuous monitoring and updating of deployed models. Solution: Set up automated monitoring systems to track model performance and detect drift. Schedule regular retraining sessions and set up a maintenance protocol. Use MLOps practices to ensure systematic and reliable model management. By proactively addressing these challenges, businesses can unlock the full potential of AI and ML, driving meaningful impact and staying ahead in the competitive market. What solutions have worked for you in your AI journey? #AI #MachineLearning #DataScience #BusinessInnovation #TechChallenges #AIDeployment #Solutions #MLOps #AIModels

🎯 Common Data Science Malpractices That Inflate Model Performance - 𝗢𝘃𝗲𝗿𝗳𝗶𝘁𝘁𝗶𝗻𝗴: Training your model for too many epochs or with too many features without proper validation - 𝗗𝗮𝘁𝗮 𝗟𝗲𝗮𝗸𝗮𝗴𝗲: Including future or test data in your training set or using a feature that inadvertently contains information the model is supposed to predict - 𝗜𝗺𝗽𝗿𝗼𝗽𝗲𝗿 𝗖𝗿𝗼𝘀𝘀-𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: Relying on a simple train-test split without thorough segmentation can introduce bias and skew results - 𝗖𝗵𝗲𝗿𝗿𝘆-𝗣𝗶𝗰𝗸𝗶𝗻𝗴 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: Highlighting only the metrics that showcase your model’s strengths while overlooking those that reveal its weaknesses - 𝗛𝘆𝗽𝗲𝗿𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 𝗧𝘂𝗻𝗶𝗻𝗴 𝗼𝗻 𝗧𝗲𝘀𝘁 𝗗𝗮𝘁𝗮: Adjusting model parameters using the test set instead of a separate validation set 📢 Need expert help with AI adoption? DM us to accelerate your success. #innovation #management #artificialintelligence #technology #business #strategy #finance #logistics #advertising #healthcare #sales #money https://lnkd.in/gDacJANb

𝐂𝐚𝐧 𝐀𝐈 𝐒𝐨𝐥𝐯𝐞 𝐘𝐨𝐮𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬? Businesses are ALL OVER AI. From chatbots to predictive analytics, it promises to revolutionize everything we do. But hold your horses! Integrating AI can be tricky. Here's what could be tripping you up: 1. Fitting the Puzzle Pieces: Imagine trying to shove a square peg into a round hole. That's what integrating new AI systems into existing infrastructure can feel like. Ugh! 2. Data Dilemma: AI thrives on good data. Bad data? Not so much. Dirty, incomplete, or biased data can lead to unreliable results. Think "garbage in, garbage out." ️ 3. Ethical Tightrope Walk: AI can be a powerful tool, but with great power comes great responsibility. Privacy concerns and potential biases need careful consideration.  𝐁𝐮𝐭 𝐝𝐨𝐧'𝐭 𝐝𝐞𝐬𝐩𝐚𝐢𝐫! 𝐀𝐈 𝐬𝐮𝐜𝐜𝐞𝐬𝐬 𝐬𝐭𝐨𝐫𝐢𝐞𝐬 𝐚𝐫𝐞 𝐨𝐮𝐭 𝐭𝐡𝐞𝐫𝐞! 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐚𝐫𝐞 𝐮𝐬𝐢𝐧𝐠 𝐀𝐈 𝐭𝐨:   Boost customer service with chatbots that answer questions 24/7.   Predict sales trends and optimize marketing campaigns.   Automate repetitive tasks freeing up employees for more strategic work. 𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐬𝐞𝐜𝐫𝐞𝐭 𝐬𝐚𝐮𝐜𝐞 𝐟𝐨𝐫 𝐨𝐯𝐞𝐫𝐜𝐨𝐦𝐢𝐧𝐠 𝐀𝐈 𝐡𝐮𝐫𝐝𝐥𝐞𝐬: Start small, scale smart: Don't try to automate everything at once. Focus on specific tasks and build momentum. Data is king: Invest in high-quality data collection and cleaning. Think of it as building a solid foundation for your AI castle. Ethics matter: Be transparent and accountable in your AI development and implementation. Remember, AI should work for people, not the other way around. 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐣𝐨𝐢𝐧 𝐭𝐡𝐞 𝐀𝐈 𝐫𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐭𝐡𝐞 𝐡𝐞𝐚𝐝𝐚𝐜𝐡𝐞𝐬? 𝐖𝐞 𝐜𝐚𝐧 𝐡𝐞𝐥𝐩! 𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐙𝐢𝐧𝐗𝐒𝐨𝐟𝐭 𝐭𝐨𝐝𝐚𝐲 𝐟𝐨𝐫 𝐚 𝐅𝐑𝐄𝐄 𝐜𝐨𝐧𝐬𝐮𝐥𝐭𝐚𝐭𝐢𝐨𝐧. 𝐎𝐮𝐫 𝐞𝐱𝐩𝐞𝐫𝐭 𝐭𝐞𝐚𝐦 𝐰𝐢𝐥𝐥 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐚𝐬𝐬𝐞𝐬𝐬 𝐲𝐨𝐮𝐫 𝐧𝐞𝐞𝐝𝐬 𝐚𝐧𝐝 𝐟𝐢𝐧𝐝 𝐭𝐡𝐞 𝐩𝐞𝐫𝐟𝐞𝐜𝐭 𝐀𝐈 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐠𝐫𝐨𝐰𝐭𝐡. 𝐖𝐞 𝐨𝐟𝐟𝐞𝐫 𝐜𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐞𝐝 𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐭𝐡𝐚𝐭 𝐚𝐝𝐚𝐩𝐭 𝐭𝐨 𝐭𝐡𝐞 𝐞𝐯𝐞𝐫-𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐦𝐚𝐫𝐤𝐞𝐭. Visit our website at www.zinxsoft.com to learn more! P.S. Share this post with your business buddies who might be struggling with AI! #AI #BusinessGrowth #ZinXSoft #informationtechnology

Krista's Chief AI & Customer Success Officer sums up the AI virtuous cycle of value creation that funds further value capture beautifully in this short video. #GenAI #AIROI #AIValue #DataValue #LLMs