{"id":1891,"date":"2024-05-21T12:13:09","date_gmt":"2024-05-21T12:13:09","guid":{"rendered":"https:\/\/www.softlabsgroup.com\/ai-solutions\/?p=1891"},"modified":"2025-02-03T06:27:06","modified_gmt":"2025-02-03T06:27:06","slug":"ai-nlp-to-sql-conversion","status":"publish","type":"post","link":"https:\/\/www.softlabsgroup.com\/ai-solutions\/ai-nlp-to-sql-conversion\/","title":{"rendered":"AI for Natural Language to SQL Conversion"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"1891\" class=\"elementor elementor-1891\">\n\t\t\t\t<div class=\"elementor-element elementor-element-20cb4f4e e-flex e-con-boxed e-con e-parent\" data-id=\"20cb4f4e\" data-element_type=\"container\" data-core-v316-plus=\"true\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-65c5a6ac elementor-widget elementor-widget-text-editor\" data-id=\"65c5a6ac\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.20.0 - 13-03-2024 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<!-- wp:heading {\"style\":{\"elements\":{\"link\":{\"color\":{\"text\":\"#0a2261\"}}},\"color\":{\"text\":\"#0a2261\"}},\"fontSize\":\"large\"} -->\n<h2 class=\"wp-block-heading has-text-color has-link-color has-large-font-size\" style=\"color: #0a2261;\"><strong>Synopsis<\/strong>\u00a0<\/h2>\n<!-- \/wp:heading --><!-- wp:spacer {\"height\":\"33px\"} -->\n<div class=\"wp-block-spacer\" style=\"height: 33px;\" aria-hidden=\"true\">\u00a0<\/div>\n<!-- \/wp:spacer --><!-- wp:paragraph -->\n<p>This case study explores the implementation of a Natural Language to SQL conversion system to streamline data analytics. The NL2 SQL system leverages NLP and machine learning to interpret and convert natural language queries into SQL, empowering non-technical users to extract insights seamlessly. Real-world examples demonstrate improved efficiency, accuracy, and user empowerment, reducing dependency on technical staff and enabling faster decision-making. This study showcases how NL2 SQL bridges the gap between non-technical users and complex data, facilitating data-driven decision-making.\u00a0<\/p>\n<!-- \/wp:paragraph --><!-- wp:image {\"id\":1892,\"width\":\"320px\",\"height\":\"auto\",\"sizeSlug\":\"full\",\"linkDestination\":\"none\"} -->\n<figure class=\"wp-block-image size-full is-resized\"><img decoding=\"async\" width=\"253\" height=\"160\" class=\"wp-image-1892\" style=\"width: 320px; height: auto;\" src=\"https:\/\/www.softlabsgroup.com\/ai-solutions\/wp-content\/uploads\/2024\/05\/nlp-to-sql-doing-the-process.png\" alt=\"\" \/><\/figure>\n<!-- \/wp:image --><!-- wp:spacer {\"height\":\"25px\"} -->\n<div class=\"wp-block-spacer\" style=\"height: 25px;\" aria-hidden=\"true\">\u00a0<\/div>\n<!-- \/wp:spacer --><!-- wp:heading {\"style\":{\"elements\":{\"link\":{\"color\":{\"text\":\"#0a2261\"}}},\"color\":{\"text\":\"#0a2261\"}},\"fontSize\":\"large\"} -->\n<h2 class=\"wp-block-heading has-text-color has-link-color has-large-font-size\" style=\"color: #0a2261;\"><strong>Introduction<\/strong>\u00a0<\/h2>\n<!-- \/wp:heading --><!-- wp:spacer {\"height\":\"33px\"} -->\n<div class=\"wp-block-spacer\" style=\"height: 33px;\" aria-hidden=\"true\">\u00a0<\/div>\n<!-- \/wp:spacer --><!-- wp:paragraph -->\n<p>In recent years, the emergence of Data Transformer A.I. models have resulted in a significant boost in the development of A.I. technologies. Consequently, numerous Natural Language Processing (NLP) models have been developed and applied in various areas such as language translation, speech recognition, text summarization, sentiment analysis, and more. In our case, we aim to utilize these NLP models for the translation of natural language (NL) queries into SQL queries through the implementation of supervised learning techniques. To achieve this, we intend to design and create a custom dataset that will be used to train the models. The goal is for the model to effortlessly translate NL queries into SQL queries.\u00a0<\/p>\n<!-- \/wp:paragraph --><!-- wp:paragraph -->\n<p>By employing a custom-trained A.I. for translation and seamlessly integrating it with the existing database, we eliminate the challenges faced by users when manually writing SQL queries. This not only saves time but also streamlines the process, making it more efficient and user-friendly.\u00a0<\/p>\n<!-- \/wp:paragraph --><!-- wp:image {\"id\":1893,\"width\":\"628px\",\"height\":\"auto\",\"sizeSlug\":\"full\",\"linkDestination\":\"none\"} -->\n<figure class=\"wp-block-image size-full is-resized\"><img fetchpriority=\"high\" decoding=\"async\" width=\"546\" height=\"174\" class=\"wp-image-1893\" style=\"width: 100%; height: auto;\" src=\"https:\/\/www.softlabsgroup.com\/ai-solutions\/wp-content\/uploads\/2024\/05\/nlp-to-sql-process.png\" alt=\"nlp to sql process\" \/><\/figure>\n<!-- \/wp:image --><!-- wp:spacer {\"height\":\"25px\"} -->\n<div class=\"wp-block-spacer\" style=\"height: 25px;\" aria-hidden=\"true\">\u00a0<\/div>\n<!-- \/wp:spacer --><!-- wp:heading {\"style\":{\"color\":{\"text\":\"#0a2261\"},\"elements\":{\"link\":{\"color\":{\"text\":\"#0a2261\"}}}},\"fontSize\":\"large\"} -->\n<h2 class=\"wp-block-heading has-text-color has-link-color has-large-font-size\" style=\"color: #0a2261;\"><strong>NL2SQL Model:<\/strong>\u00a0<\/h2>\n<!-- \/wp:heading --><!-- wp:spacer {\"height\":\"33px\"} -->\n<div class=\"wp-block-spacer\" style=\"height: 33px;\" aria-hidden=\"true\">\u00a0<\/div>\n<!-- \/wp:spacer --><!-- wp:paragraph -->\n<p>There are many NLP models which have been custom fitted for translation of NL2SQL. And each differing in the results and efficiency based on the type of database it has been used for. For our case study, we picked Google&#8217;s Bert t5 model which is trained on a custom SQL query dataset.\u00a0<\/p>\n<!-- \/wp:paragraph --><!-- wp:paragraph -->\n<p>The model functions well with the Dataset of 56,000 unique queries and translates NL2SQL within the parameters provided in the Dataset.<\/p>\n<!-- \/wp:paragraph --><!-- wp:image {\"id\":1894,\"width\":\"509px\",\"height\":\"auto\",\"sizeSlug\":\"full\",\"linkDestination\":\"none\"} -->\n<figure class=\"wp-block-image size-full is-resized\"><img decoding=\"async\" width=\"448\" height=\"101\" class=\"wp-image-1894\" style=\"width: 100%; height: auto;\" src=\"https:\/\/www.softlabsgroup.com\/ai-solutions\/wp-content\/uploads\/2024\/05\/results-from-testing-model.png\" alt=\"results from testing model\" \/>\n<figcaption class=\"wp-element-caption\"><strong>Results from testing the model<\/strong>\u00a0<\/figcaption>\n<\/figure>\n<!-- \/wp:image --><!-- wp:spacer {\"height\":\"25px\"} -->\n<div class=\"wp-block-spacer\" style=\"height: 25px;\" aria-hidden=\"true\">\u00a0<\/div>\n<!-- \/wp:spacer --><!-- wp:paragraph {\"fontSize\":\"medium\"} -->\n<p class=\"has-medium-font-size\"><strong>The following is an example of the queries within the dataset:\u00a0<\/strong><\/p>\n<!-- \/wp:paragraph --><!-- wp:spacer {\"height\":\"33px\"} -->\n<div class=\"wp-block-spacer\" style=\"height: 33px;\" aria-hidden=\"true\">\u00a0<\/div>\n<!-- \/wp:spacer --><!-- wp:image {\"id\":1895,\"width\":\"689px\",\"height\":\"auto\",\"sizeSlug\":\"full\",\"linkDestination\":\"none\"} -->\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"505\" height=\"372\" class=\"wp-image-1895\" style=\"width: 100%; height: auto;\" src=\"https:\/\/www.softlabsgroup.com\/ai-solutions\/wp-content\/uploads\/2024\/05\/results-from-model.png\" alt=\"example of the queries within the dataset\" \/><\/figure>\n<!-- \/wp:image --><!-- wp:image {\"id\":1896,\"width\":\"693px\",\"height\":\"auto\",\"sizeSlug\":\"full\",\"linkDestination\":\"none\"} -->\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"461\" height=\"196\" class=\"wp-image-1896\" style=\"width: 100%; height: auto;\" src=\"https:\/\/www.softlabsgroup.com\/ai-solutions\/wp-content\/uploads\/2024\/05\/model-process-infra.png\" alt=\"model process infra\" \/><\/figure>\n<!-- \/wp:image --><!-- wp:spacer {\"height\":\"20px\"} -->\n<div class=\"wp-block-spacer\" style=\"height: 20px;\" aria-hidden=\"true\">\u00a0<\/div>\n<!-- \/wp:spacer --><!-- wp:paragraph -->\n<p>We would also apply Data Augmentation using PPDB (paraphrasing database). This would help us with different way a certain NL query can be paraphrased and thus improve and increase our model\u2019s accuracy.\u00a0\u00a0<\/p>\n<!-- \/wp:paragraph --><!-- wp:spacer {\"height\":\"25px\"} -->\n<div class=\"wp-block-spacer\" style=\"height: 25px;\" aria-hidden=\"true\">\u00a0<\/div>\n<!-- \/wp:spacer --><!-- wp:paragraph {\"fontSize\":\"medium\"} -->\n<p class=\"has-medium-font-size\"><strong>Following is the complete process that needs to be undertaken to create a custom trained model:\u00a0<\/strong><\/p>\n<!-- \/wp:paragraph --><!-- wp:spacer {\"height\":\"33px\"} -->\n<div class=\"wp-block-spacer\" style=\"height: 33px;\" aria-hidden=\"true\">\u00a0<\/div>\n<!-- \/wp:spacer --><!-- wp:paragraph {\"fontSize\":\"medium\"} -->\n<p class=\"has-medium-font-size\"><strong>Analysis of data features:\u00a0<\/strong><\/p>\n<!-- \/wp:paragraph --><!-- wp:list -->\n<ul><!-- wp:list-item -->\n<li>Analysis of the SQL database and identifying the key features and structure of the model and creating a skeleton of the interlinked connection of the database.\u00a0<\/li>\n<!-- \/wp:list-item --><\/ul>\n<!-- \/wp:list --><!-- wp:paragraph {\"fontSize\":\"medium\"} -->\n<p class=\"has-medium-font-size\"><strong>Preprocessing the Data\u00a0<\/strong><\/p>\n<!-- \/wp:paragraph --><!-- wp:list -->\n<ul><!-- wp:list-item -->\n<li>Creation of all possible SQL queries from the key features and linked database.\u00a0<\/li>\n<!-- \/wp:list-item --><\/ul>\n<!-- \/wp:list --><!-- wp:paragraph {\"fontSize\":\"medium\"} -->\n<p class=\"has-medium-font-size\"><strong>NL Data Augmentation:\u00a0<\/strong><\/p>\n<!-- \/wp:paragraph --><!-- wp:list -->\n<ul><!-- wp:list-item -->\n<li>Creating NL queries for the SQL queries. then using paraphrasing augmented data.\u00a0<\/li>\n<!-- \/wp:list-item --><\/ul>\n<!-- \/wp:list --><!-- wp:paragraph {\"fontSize\":\"medium\"} -->\n<p class=\"has-medium-font-size\"><strong>Data Labelling:\u00a0<\/strong><\/p>\n<!-- \/wp:paragraph --><!-- wp:list -->\n<ul><!-- wp:list-item -->\n<li>Labelling the NL queries with the SQL queries.\u00a0<\/li>\n<!-- \/wp:list-item --><\/ul>\n<!-- \/wp:list --><!-- wp:paragraph {\"fontSize\":\"medium\"} -->\n<p class=\"has-medium-font-size\"><strong>Data cleaning:\u00a0<\/strong><\/p>\n<!-- \/wp:paragraph --><!-- wp:list -->\n<ul><!-- wp:list-item -->\n<li>Analysis of the dataset created and rectifying it.\u00a0<\/li>\n<!-- \/wp:list-item --><\/ul>\n<!-- \/wp:list --><!-- wp:paragraph {\"fontSize\":\"medium\"} -->\n<p class=\"has-medium-font-size\"><strong>Model Selection and Analysis:\u00a0<\/strong><\/p>\n<!-- \/wp:paragraph --><!-- wp:list -->\n<ul><!-- wp:list-item -->\n<li>Selection of a model which is best suited for our custom dataset.\u00a0<\/li>\n<!-- \/wp:list-item --><\/ul>\n<!-- \/wp:list --><!-- wp:paragraph {\"fontSize\":\"medium\"} -->\n<p class=\"has-medium-font-size\"><strong>Model Training and testing:\u00a0<\/strong><\/p>\n<!-- \/wp:paragraph --><!-- wp:list -->\n<ul><!-- wp:list-item -->\n<li>Training the model and testing its response to our queries.\u00a0<\/li>\n<!-- \/wp:list-item --><!-- wp:list-item -->\n<li>Model Evaluation and Retraining.\u00a0<\/li>\n<!-- \/wp:list-item --><!-- wp:list-item -->\n<li>Changes to dataset if needed.\u00a0<\/li>\n<!-- \/wp:list-item --><\/ul>\n<!-- \/wp:list --><!-- wp:spacer {\"height\":\"25px\"} --><!-- \/wp:spacer --><!-- wp:heading {\"style\":{\"color\":{\"text\":\"#0a2261\"},\"elements\":{\"link\":{\"color\":{\"text\":\"#0a2261\"}}}},\"fontSize\":\"large\"} --><!-- \/wp:paragraph -->\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3eec223 e-flex e-con-boxed e-con e-parent\" data-id=\"3eec223\" data-element_type=\"container\" data-core-v316-plus=\"true\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ac7f8ab elementor-widget elementor-widget-heading\" data-id=\"ac7f8ab\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.20.0 - 13-03-2024 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">NLP to SQL Dashboard<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c1c7a50 e-transform e-transform e-transform elementor-widget elementor-widget-image\" data-id=\"c1c7a50\" data-element_type=\"widget\" data-settings=\"{&quot;_transform_skewX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-1,&quot;sizes&quot;:[]},&quot;_transform_skewY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:-1,&quot;sizes&quot;:[]},&quot;_transform_rotateZ_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:1,&quot;sizes&quot;:[]},&quot;_transform_rotateZ_effect_tablet&quot;:{&quot;unit&quot;:&quot;deg&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_rotateZ_effect_mobile&quot;:{&quot;unit&quot;:&quot;deg&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateX_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_translateY_effect_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_skewX_effect_tablet&quot;:{&quot;unit&quot;:&quot;deg&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_skewX_effect_mobile&quot;:{&quot;unit&quot;:&quot;deg&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_skewY_effect_tablet&quot;:{&quot;unit&quot;:&quot;deg&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;_transform_skewY_effect_mobile&quot;:{&quot;unit&quot;:&quot;deg&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.20.0 - 13-03-2024 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"900\" height=\"465\" src=\"https:\/\/www.softlabsgroup.com\/ai-solutions\/wp-content\/uploads\/2024\/05\/NLP-to-SQL-dashboard.jpg\" class=\"attachment-full size-full wp-image-2212\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bd957c4 elementor-widget elementor-widget-text-editor\" data-id=\"bd957c4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<!-- wp:heading {\"style\":{\"elements\":{\"link\":{\"color\":{\"text\":\"#0a2261\"}}},\"color\":{\"text\":\"#0a2261\"}},\"fontSize\":\"large\"} -->\n<div class=\"wp-block-spacer\" style=\"height: 25px;\" aria-hidden=\"true\">\u00a0<\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:heading {\"style\":{\"color\":{\"text\":\"#0a2261\"},\"elements\":{\"link\":{\"color\":{\"text\":\"#0a2261\"}}}},\"fontSize\":\"large\"} -->\n<h2 class=\"wp-block-heading has-text-color has-link-color has-large-font-size\" style=\"color: #0a2261;\"><strong>Conclusion<\/strong>\u00a0<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"33px\"} -->\n<div class=\"wp-block-spacer\" style=\"height: 33px;\" aria-hidden=\"true\">\u00a0<\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph -->\n<p>One of the primary challenges in developing an NL2SQL model lies in creating a precise and robust dataset. The dataset plays a crucial role in training the model to accurately understand and translate natural language queries into SQL queries. The dataset needs to be meticulously designed, ensuring that it covers a wide range of query variations and captures the intricacies of the domain. It requires careful consideration of different query structures, syntactic patterns, and semantic meanings to ensure the model&#8217;s effectiveness.\u00a0<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>While the NL2SQL model should ideally be able to handle some deviation in the queries, establishing a certain ontology becomes crucial. Developing an ontology involves defining a structured representation of the domain-specific knowledge, such as the database schema, query types, and their corresponding SQL representations. This ontology provides a framework for the model to understand the domain and perform more efficiently. By incorporating a well-defined ontology, the model gains a better understanding of the expected query structures and can generate accurate SQL translations.\u00a0<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Once the dataset and ontology are established, the NL2SQL model can be continuously updated to adapt to newer entries or changes in the database. As the database evolves with new data or modifications, the model needs to be retrained and deployed to ensure its performance remains up to date. Regular updates and retraining allow the model to incorporate the latest information and maintain its accuracy over time.\u00a0<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Moreover, it is essential to have a mechanism in place to gather user feedback and continuously improve the NL2SQL model. User feedback can help identify areas where the model may struggle or produce incorrect translations. This feedback loop enables the refinement of the dataset, ontology, and model architecture, leading to continuous enhancements in performance and accuracy.\u00a0<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>&nbsp;<\/p>\n<!-- \/wp:paragraph -->\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-8edee39 e-grid e-con-boxed e-con e-parent\" data-id=\"8edee39\" data-element_type=\"container\" data-core-v316-plus=\"true\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-82bc030 e-grid e-con-boxed e-con e-child\" data-id=\"82bc030\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f23efdf elementor-widget elementor-widget-contentviews_widget_grid1\" data-id=\"f23efdf\" data-element_type=\"widget\" data-widget_type=\"contentviews_widget_grid1.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class='pt-cv-wrapper'> <div class=\"pt-cv-view pt-cv-blockgrid iscvblock iscvelementor grid1 layout2 pt-cv-left\" id=\"pt-cv-view-f23efdf\"><div data-id=\"pt-cv-page-1\" class=\"pt-cv-page\" data-cvc=\"2\"><div class=\" pt-cv-content-item pt-cv-1-col\"  data-pid=\"3470\"><div class=\"pt-cv-thumb-wrapper  \"><a href=\"https:\/\/www.softlabsgroup.com\/ai-solutions\/ai-powered-custom-dealer-portal\/\" class=\"_self pt-cv-href-thumbnail pt-cv-thumb-default\" target=\"_self\" ><img loading=\"lazy\" decoding=\"async\" width=\"550\" height=\"300\" src=\"https:\/\/www.softlabsgroup.com\/ai-solutions\/wp-content\/uploads\/2026\/04\/AI-Powered-Custom-Dealer-Portal-Manufacturers.png\" class=\"pt-cv-thumbnail\" alt=\"ai powered custom dealer portal for manufacturers and deals\" \/><\/a><\/div>\n<div class=\"pt-cv-remain-wrapper\"><h4 class=\"pt-cv-title\"><a href=\"https:\/\/www.softlabsgroup.com\/ai-solutions\/ai-powered-custom-dealer-portal\/\" class=\"_self\" target=\"_self\" >AI-Powered Custom Dealer Portal for Distributors &amp; Dealers<\/a><\/h4><\/div><\/div>\n<div class=\" pt-cv-content-item pt-cv-1-col\"  data-pid=\"3441\"><div class=\"pt-cv-thumb-wrapper  \"><a href=\"https:\/\/www.softlabsgroup.com\/ai-solutions\/ai-powered-hrms-software\/\" class=\"_self pt-cv-href-thumbnail pt-cv-thumb-default\" target=\"_self\" ><img loading=\"lazy\" decoding=\"async\" width=\"550\" height=\"300\" src=\"https:\/\/www.softlabsgroup.com\/ai-solutions\/wp-content\/uploads\/2026\/04\/AI-Powered-HRMS-Software.png\" class=\"pt-cv-thumbnail\" alt=\"AI-Powered-HRMS-Software\" \/><\/a><\/div>\n<div class=\"pt-cv-remain-wrapper\"><h4 class=\"pt-cv-title\"><a href=\"https:\/\/www.softlabsgroup.com\/ai-solutions\/ai-powered-hrms-software\/\" class=\"_self\" target=\"_self\" >AI-Powered HRMS Software: Automate Attendance &amp; Payroll Automatically<\/a><\/h4><\/div><\/div><\/div><\/div> <\/div> \t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Synopsis\u00a0 This case study explores the implementation of a Natural Language to SQL conversion system to streamline data analytics. The 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