{"id":20080,"date":"2026-10-27T13:24:48","date_gmt":"2026-10-27T08:24:48","guid":{"rendered":"https:\/\/multiqos.com\/blogs\/?p=20080"},"modified":"2026-10-27T13:28:59","modified_gmt":"2026-10-27T08:28:59","slug":"fde-in-enterprise-ai","status":"publish","type":"post","link":"https:\/\/multiqos.com\/blogs\/fde-in-enterprise-ai\/","title":{"rendered":"From Pilot to Production: How Forward Deployed Engineers Make Enterprise AI Work"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Imagine this: An AI pilot has impressed the leadership team in the demo. The models deliver accurate results, dashboards look clean, and everyone agrees that this should be made live. Now the real work begins. The AI pilot needs to be connected with the legacy CRM and ERP and traditional datasets. It also needs to fit into the already existing workflows. As a result, the project is stalled, and the demo is now a distant memory.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This is a common story of almost every enterprise. Rarely, the bottleneck is the AI model. In most of the cases, it is the deployment strategy. To avoid this situation, AI companies are changing how they work with the engineers. Now, the deployment engineers are directly placed in the production environment.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">These engineers are called Forward Deployed Engineers (FDE). Read on to learn why modern AI enterprises are moving towards them.\u00a0<\/span><\/p>\n<h2><b>What is a Forward Deployed Engineer?\u00a0<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">As technology evolves, building a powerful solution is not enough. Today\u2019s AI and enterprise systems are complex and rarely work well out of the box. Every business has its own systems, data, workflows, and constraints, so they need someone who works in their environment and has a complete overview of their operations.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">That is the role of the forward-deployed engineer (FDE). An FDE doesn\u2019t walk away once the deployment is done. They implement the solution in the customer\u2019s environment, customize it to a specific use case, and operationalize it to achieve the intended results.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In simplified terms, an FDE:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">is well aware of the business requirements and concerns.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">is a connection between internal engineering and outside customers.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">ensures that solutions deliver significant business value.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">recommends further development of the product to accommodate the client\u2019s requirements<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">makes sure that security and governance controls are in place<\/span><\/li>\n<\/ul>\n<h2><b>The prototype to production gap: what holds enterprises back<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">A prototype exists to prove that an idea can work. This idea should be working day and night and in every setting. Many promising ideas stall in the gap between the two.\u00a0<\/span><\/p>\n<h3><b>Prototypes aren\u2019t working in the production environment<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Prototypes are designed to run on a small set of data in ideal conditions. In production, the same solution has to accommodate requests coming from different users and process a larger dataset. They should be able to support business-critical workflows where any error has real consequences.\u00a0<\/span><\/p>\n<h3><b>Enterprise systems multiply the complexity<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">There is no plugin to add AI capabilities into CRM, ERP, or any traditional application. Every solution brings its own set of data formats and asset rules. This integration is often one of the biggest problems between a successful demo and a live deployment. Reliable <\/span><a href=\"https:\/\/multiqos.com\/data-engineering-services\/\"><span style=\"font-weight: 400;\">data engineering services<\/span><\/a><span style=\"font-weight: 400;\"> can help businesses prepare and integrate the data needed for enterprise AI applications.\u00a0<\/span><\/p>\n<h3><b>Security and governance controls\u00a0<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">A prototype with errors might be able to bypass security and governance checks. However, the same thing is not possible for a system in the production environment. There are certain moving parts such as access rules, audit trails, and compliance regulations that should be fixed in the prototype stage. Implementing these controls in the production stage is cumbersome and also introduces delays.\u00a0<\/span><\/p>\n<h3><b>User adoption is important\u00a0<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">A feature-rich AI solution is considered to be a wasted effort if the employees don&#8217;t know how to use it. The solution must fit into the existing workflows, tools, and habits, and benefits should be clear too. Without user adoption, it is impossible to drive intended value from the AI solution.\u00a0<\/span><\/p>\n<p><a href=\"https:\/\/multiqos.com\/contact-us\/\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-20087\" src=\"https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/khgmdhztcw925dxkxht8.webp\" alt=\"Explore the key differences between FDEs and solutions engineers to find the right fit for your business.\" width=\"1400\" height=\"418\" srcset=\"https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/khgmdhztcw925dxkxht8.webp 1400w, https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/khgmdhztcw925dxkxht8-430x128.webp 430w, https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/khgmdhztcw925dxkxht8-1024x306.webp 1024w, https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/khgmdhztcw925dxkxht8-150x45.webp 150w\" sizes=\"auto, (max-width: 1400px) 100vw, 1400px\" \/><\/a><\/p>\n<h2><b>Why Every AI Enterprise Needs a Forward Deployed Engineer?<\/b><\/h2>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-20085\" src=\"https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/ceo76ylgddjayzkxbf2w.webp\" alt=\"Why Every AI Enterprise Needs a Forward Deployed Engineer?\" width=\"2048\" height=\"1896\" srcset=\"https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/ceo76ylgddjayzkxbf2w.webp 2048w, https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/ceo76ylgddjayzkxbf2w-356x330.webp 356w, https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/ceo76ylgddjayzkxbf2w-1024x948.webp 1024w, https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/ceo76ylgddjayzkxbf2w-1536x1422.webp 1536w, https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/ceo76ylgddjayzkxbf2w-150x139.webp 150w\" sizes=\"auto, (max-width: 2048px) 100vw, 2048px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">AI enterprises are acknowledging the fact that a great model is only half the battle won; the real challenge lies in making the solution work in the client\u2019s infrastructure. If your AI project needs dedicated implementation expertise, you can <\/span><a href=\"https:\/\/multiqos.com\/hire-forward-deployed-engineers\/\"><span style=\"font-weight: 400;\">hire forward-deployed engineers<\/span><\/a><span style=\"font-weight: 400;\"> to help move solutions into production.\u00a0<\/span><\/p>\n<h3><b>Enterprise AI needs deployment, not a download\u00a0<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Organizations have moved from generic chat assistants to autonomous agents. These agents should be able to manage supply chains, automate compliance checks, or process legal documents within their own technology stack. Achieving these involves integration with data schemas, legacy infrastructure and other controls which were not designed while considering AI. No off-the-shelf deployment can handle this. An FDE can.\u00a0<\/span><\/p>\n<h3><b>Renewals are lost during deployment<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Closing the deal is only the first hurdle for any AI vendor. The customer is indeed lost or won only when the product is deployed. If a customer\u2019s implementation drags or fails, the customer is unlikely to stay, and they will spread the negative review as well. This is where a Forward Deployed Engineer steps in and turns a risky rollout into a successful implementation. They don\u2019t just ship the software; they own the customer\u2019s success beyond go-live.\u00a0<\/span><\/p>\n<h3><b>Security and compliance can\u2019t be an afterthought<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Highly regulated industries such as healthcare, financial services, defense, and legal can\u2019t just plug in AI and start using the system. Data residency, audit logging, role-based access control, and output model governance should be engineered in the customer\u2019s environment, and none of this can be handled by subscribing to an <a href=\"https:\/\/multiqos.com\/ai-development-services\/\">AI service<\/a>. An FDE is required to handle this opportunity.\u00a0<\/span><\/p>\n<h3><b>Software\u2019s value now lies in execution<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">AI coding tools are making it easy for anyone to build software; therefore, designing solutions is not a lucrative job. The engineers who can integrate applications with the customer\u2019s existing infrastructure, navigate through complex data infrastructure, and deliver AI models that offer measurable business impact are paid more. The competitive edge has moved away from building models and toward deploying them successfully in an enterprise environment.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Palantir was an early pioneer of the Forward Deployed Engineer, and it has paid off. Now, big leaders such as Salesforce, OpenAI, Anthropic, and Google are making their own FDE teams. When every company is in the race to build its own FDE resource pool, the question now is: Can we move forward without dedicated FDEs?\u00a0<\/span><\/p>\n<p><a href=\"https:\/\/multiqos.com\/contact-us\/\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-20088\" src=\"https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/qzxkh5dd6dyergvdzocf.webp\" alt=\"Need help moving AI solutions from pilot to production?\" width=\"1400\" height=\"418\" srcset=\"https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/qzxkh5dd6dyergvdzocf.webp 1400w, https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/qzxkh5dd6dyergvdzocf-430x128.webp 430w, https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/qzxkh5dd6dyergvdzocf-1024x306.webp 1024w, https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/qzxkh5dd6dyergvdzocf-150x45.webp 150w\" sizes=\"auto, (max-width: 1400px) 100vw, 1400px\" \/><\/a><\/p>\n<h2><b>How Forward Deployed Engineers Support Customization?<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">FDEs power customization of AI solutions for different companies, as they are an extension of the client\u2019s team and have first-hand information about their specific problems. Businesses with unique integration requirements can use <\/span><a href=\"https:\/\/multiqos.com\/custom-software-development\/\"><span style=\"font-weight: 400;\">custom software development <\/span><\/a><span style=\"font-weight: 400;\">to connect existing systems with new solutions.\u00a0<\/span><\/p>\n<h3><b>On-site &amp; deep integrations<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Traditional engineers follow a cookie-cutter approach for every client. They design one product for thousands of users. Forward-deployed engineers write custom code that connects APIs directly to unorganized data, legacy systems, CRMs, and approval workflows.\u00a0<\/span><\/p>\n<h3><b>AI &amp; workflow training<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Out-of-the-box API applications are created to cater to the unique needs of any organization. Every client typically needs its own set of prompts, set of connected tools, APIs, and use cases. An <\/span><a href=\"https:\/\/multiqos.com\/blogs\/forward-deployed-engineer\/\"><span style=\"font-weight: 400;\">FDE <\/span><\/a><span style=\"font-weight: 400;\">fine-tunes these layers, enabling AI solutions to scale with the business.\u00a0<\/span><\/p>\n<h3><b>Bridging code and fulfilling needs<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">FDEs have a varied skill set. They blend engineering, product management, and a consulting approach to understand the client\u2019s problems. Then, they prepare a technical scope that solves the customer\u2019s challenge and runs smoothly in the production environment.\u00a0<\/span><\/p>\n<h3><b>The core product feedback loop<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">As FDE works closely with the client, they have direct access to their environment, as a result of which they can identify if there are any cracks in any solution. They pass these findings to the core product team so that they can make appropriate changes to the product.\u00a0<\/span><\/p>\n<h3><b>Standardizing the custom pattern<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Reliable FDE teams don\u2019t build a one-time solution. They turn what they built for one client into a repeatable architecture, shared internal libraries, and standard workflows that can be used by many businesses. As a result, the platform grows into a solution that can handle different uses.\u00a0<\/span><\/p>\n<h2><b>How Do Forward Deployed Engineers Power AI Enterprises to Achieve Desired Business Value?\u00a0<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">An FDE enables enterprises to harness the exact value of the AI solution.\u00a0<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<th><b>Enterprise AI Challenge<\/b><\/th>\n<th><b>What Forward Engineers Do<\/b><\/th>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">AI pilots to reach production<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Design and deploy AI solutions in the client\u2019s environment.\u00a0<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Enterprise systems are difficult to integrate.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Integrate AI capabilities with existing APIs, databases, and knowledge bases.\u00a0<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">AI implementations are not reliable.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Set up evaluation frameworks, monitoring, access controls, and fallback mechanisms<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Misalignment between business and technical teams<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Prepare technical architecture as per the business requirements<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Cost of AI implementation<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Spot deployment bottlenecks, simplify integration, and build solutions that fit your enterprise infrastructure.\u00a0<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Scalability of AI use cases\u00a0<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Design production-ready infrastructures that can easily be replicated into different business operations.\u00a0<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Proving the value of AI<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Create evaluation metrics to measure the impact of AI solutions<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><b>Why AI Projects Fail and How FDEs Help Them Succeed<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Most enterprise AI doesn\u2019t deliver the intended value, but the AI model is rarely the cause. The real problem lies in how it is implemented inside the client\u2019s environment. Whenever a requirement for a software implementation arises, it flows from the sales team to the solution team and the support team. There is no single team handling the entire process; as a result, the details might get lost along the way. Another issue is that the internal teams build solutions, and they don\u2019t have first-hand experience and access to the customer&#8217;s environment. As a result, the chances of software breaking down in a live instance are high.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A Forward Deployed Engineer (FDE) works in conjunction with the client\u2019s team; therefore, they are able to discover the problems early, in the planning or testing stage, so these don\u2019t reach the live environment.\u00a0<\/span><\/p>\n<h2><b>How Do FDEs Accelerate Integration?\u00a0<\/b><\/h2>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-20084\" src=\"https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/uxgoiu7azrolh7mddxst.webp\" alt=\"How Do FDE Engineers Accelerate Integration?\" width=\"2048\" height=\"1394\" srcset=\"https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/uxgoiu7azrolh7mddxst.webp 2048w, https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/uxgoiu7azrolh7mddxst-430x293.webp 430w, https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/uxgoiu7azrolh7mddxst-1024x697.webp 1024w, https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/uxgoiu7azrolh7mddxst-1536x1046.webp 1536w, https:\/\/multiqos.com\/blogs\/wp-content\/uploads\/2026\/10\/uxgoiu7azrolh7mddxst-150x102.webp 150w\" sizes=\"auto, (max-width: 2048px) 100vw, 2048px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">An FDE bridges the gap between AI solutions and the existing infrastructure.\u00a0<\/span><\/p>\n<h3><b>On-site discovery<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Work in tandem with client stakeholders, from business leaders to IT and operations teams, to understand how their day-to-day operations run and which goals AI should support. Then recommend solutions that complement the existing processes finely.\u00a0<\/span><\/p>\n<h3><b>System and legacy integration<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Integrate AI agents, APIs, and databases with the current infrastructure, security controls and legacy enterprise software so that the AI solution can access the system and data it needs. This means that the FDEs are working to accommodate the present systems in the AI solution fabric.\u00a0<\/span><\/p>\n<h3><b>Rapid prototyping and customization<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Build, test, and rework the AI solution in the client\u2019s existing environment only, rather than creating it in a separate lab. Working in the live environment enables the FDEs to try the idea quickly, get the feedback, and work on it again if the need arises.\u00a0<\/span><\/p>\n<h3><b>Feedback loop owner<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Translate field-level usage signals into clear, actionable feedback for the core product and engineering teams. FDEs know which feature is utilized on the ground, where users find difficulty, and what keeps breaking. By sharing the suggestions, they help the core team fix the recurring issues and determine the product roadmap.\u00a0<\/span><\/p>\n<h3><b>Handover &amp; scaling<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Train the client\u2019s internal team to ensure that the systems keep running smoothly once the deployment team stops supporting. This includes making the client\u2019s team aware of every product feature and hardening the pipeline so that the business\u2019s growing needs are catered to.\u00a0<\/span><\/p>\n<h2><b>Conclusion\u00a0<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">AI doesn\u2019t deliver value when it is built. Significant business impact is derived only when the AI models work well in the production instance. Most of the AI efforts are stalled in this phase only. Forward Deployed Engineers act as a bridge in this situation. They work inside the client\u2019s environment, design the AI solution to complement the business processes, and ensure that the compliance and security controls are met.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Ready to accelerate the AI solution from pilot to production? Connect with MultiQoS today and close the gap. <\/span><br \/>\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [{\n    \"@type\": \"Question\",\n    \"name\": \"How do FDEs improve AI ROI?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"An FDE aligns AI solutions with high-value business cases and measurable metrics. They eliminate deployment hurdles and optimize AI solutions to work with the real business workflow.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"Why are FDEs in demand?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"More and more organizations are moving beyond the AI pilot stage. Therefore, they need professionals with the capabilities to solve deployment, customization, and integration challenges.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"Will FDE remain relevant as the AI platforms mature?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Yes, the role will certainly remain relevant. As the AI platforms mature and become easy to deploy, the focus of FDE will shift to improving the integration and customization of these solutions.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"How is FDE different from traditional consulting?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Traditional consulting is more about strategy and communication. FDEs are skilled at building, integrating, customizing, and improving AI solutions.\"\n    }\n  }]\n}\n<\/script><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Imagine this: An AI pilot has impressed the leadership team in the demo. The models deliver accurate results, dashboards look clean, and everyone agrees that this should be made live. Now the real work begins. The AI pilot needs to be connected with the legacy CRM and ERP and traditional datasets. It also needs to [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":20083,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[32],"tags":[],"class_list":["post-20080","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml"],"acf":[],"_links":{"self":[{"href":"https:\/\/multiqos.com\/blogs\/wp-json\/wp\/v2\/posts\/20080","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/multiqos.com\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/multiqos.com\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/multiqos.com\/blogs\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/multiqos.com\/blogs\/wp-json\/wp\/v2\/comments?post=20080"}],"version-history":[{"count":8,"href":"https:\/\/multiqos.com\/blogs\/wp-json\/wp\/v2\/posts\/20080\/revisions"}],"predecessor-version":[{"id":20093,"href":"https:\/\/multiqos.com\/blogs\/wp-json\/wp\/v2\/posts\/20080\/revisions\/20093"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/multiqos.com\/blogs\/wp-json\/wp\/v2\/media\/20083"}],"wp:attachment":[{"href":"https:\/\/multiqos.com\/blogs\/wp-json\/wp\/v2\/media?parent=20080"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/multiqos.com\/blogs\/wp-json\/wp\/v2\/categories?post=20080"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/multiqos.com\/blogs\/wp-json\/wp\/v2\/tags?post=20080"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}