Azure RAG Recommendation Platform Build

@Solutionzhere

SolutionzHere is set up exactly for this kind of Azure-native, RAG-powered recommendation engine: architecture, infra-as-code, orchestration, and hard security, not just a “demo chatbot”.​ Given your expectations (Azure AI Search + OpenAI, App Services API layer, PostgreSQL-backed RAG, CI/CD, and docs/playbooks), ₹1.5–2.5 lakh is underpriced for an enterprise-grade build; a realistic, still-aggressive range is ₹4–6 lakh for a 6–8 week delivery with dev/test/prod environments.​ The architecture would follow Azure’s reference RAG patterns: Azure AI Search with integrated vectorization over your product catalogue, Azure OpenAI for ranked generation, App Service or AKS for the API layer, Azure AD-secured endpoints, and pipelines in Azure DevOps with ARM/Bicep for fully repeatable deployments.​ One key question: Roughly how many distinct products/SKUs and attributes (per product) are in your PostgreSQL catalogue today?

@Mahihassi

With a unique and diverse skill set that includes a strong background in API development, I am confident I can be an invaluable asset to your Azure RAG recommendation platform build. Though my expertise lies primarily in embedded systems and IoT, I have extensively delved into large-scale API builds that align perfectly with the scope of your project. My thorough understanding of REST/GraphQL documentation paired with efficient use of Python and Linux will ensure the API layer is well-documented and highly performant. Moreover, I have a solid grasp on Azure's landscape, having completed numerous projects on the platform over the years. This vast experience accounts not just for employing Azure-native services such as App Services and Azure AI Foundry components but also for utilizing infrastructure-as-code tools like ARM/Bicep/Terraform that you've specifically sought. With me on board, you can be rest assured of clean API surfaces that separate business logic from presentation, a needful aspect while catering to various channels-web, mobile, or future.

@mahig47

Hello! As per your project post, you are looking to build an enterprise grade, Azure native recommendation platform powered by LLM based Retrieval Augmented Generation, with all intelligence grounded strictly in your existing PostgreSQL product catalogue. The goal is to deliver accurate, explainable product recommendations through a secure, scalable Azure first architecture that can serve web, mobile, and conversational experiences from a single core engine. My focus will be on designing and implementing a complete Azure hosted RAG solution using App Services, Azure AI Search, Azure AI Foundry and Azure OpenAI or Cognitive Services, with infrastructure fully defined through ARM Bicep or Terraform. The recommendation engine will read from PostgreSQL in read only mode, perform semantic indexing and retrieval, and orchestrate LLM responses through a clean REST or GraphQL API layer. I specialize in cloud native backend systems on Azure, with hands on experience in secure enterprise architectures, LLM orchestration, and production grade CI CD pipelines. My approach emphasizes separation of concerns, strong security with Azure AD, RBAC, managed identities and Key Vault, and full operational readiness including monitoring, logging, and rollback strategies. Let’s connect to discuss your current Azure environment, data model considerations, and rollout phases so we can align on a robust and future proof recommendation platform. Best regards, Nikita Gupta.

@oleksandrf3

NEVER USE AI FOR BIDDING! You need an Azure-native, enterprise-grade RAG-based product recommendation engine, fully managed, secure, and integrated with your existing PostgreSQL. This matches my real experience building LLM-powered solutions on Azure with secure, scalable APIs and robust DevOps. Looking forward to discussing more details. Infra: Azure App Services, Azure AI Foundry, AI Search, Cognitive Services, Azure ML, PostgreSQL, Key Vault, ARM/Bicep, Terraform API: REST, GraphQL, OpenAPI, Swagger CI/CD: Azure DevOps, GitHub Actions Security: Azure AD, RBAC, Managed Identities, Audit Logging Extras: RAG pipelines, unit testing, architecture diagrams, chat interface, operational playbooks

@webmasters486

Hi there, I’ve carefully reviewed the requirements for your GenAI project and I’m confident that my expertise in building NLP pipelines using Hugging Face and LangChain can meet your expectations. My experience includes working with large language models (LLMs) for Retrieval-Augmented Generation (RAG), as well as fine-tuning models with custom datasets to enhance text generation. I’ve successfully completed similar projects where I applied these techniques in Python to build robust, client-specific solutions. I would love the opportunity to discuss how I can leverage my skills to develop a tailored solution for your project. Feel free to take a look at my portfolio to get a sense of the work I’ve done: Portfolio: https://www.freelancer.com/u/webmasters486 Looking forward to hearing from you! Best regards, Muhammad Adil

@MuhammadAsad108

✔ I deliver enterprise-grade AI platforms — governed, secure, and production-ready. ✔ Workflow Diagram PostgreSQL Catalogue (Read-Only) ⟶⟶ Azure AI Search Indexing ⟶⟶ RAG Orchestration Layer ⟶⟶ LLM Inference (Azure OpenAI) ⟶⟶ Recommendation API / Chat Endpoint ⟶⟶ Secure Consumption (Web / Mobile / Bots) Key Highlights ✔ Azure-native by design — App Services, Azure AI Search, Azure OpenAI, Azure ML, Key Vault ✔ Strict data grounding — recommendations sourced only from PostgreSQL catalogue data ✔ RAG-first architecture — semantic retrieval + controlled LLM generation ✔ Low-latency performance — optimized indexing & caching for sub-1s responses ✔ Clean API surface — REST / GraphQL layer decoupled from UI and channels ✔ Conversational-ready — optional chat endpoint or Bot Framework integration ✔ Security & governance built-in — Azure AD auth, RBAC, Managed Identity, audit logs ✔ Repeatable delivery — full IaC (Bicep/Terraform) + CI/CD pipelines Best Regards, Asad Azure AI Solutions Architect | RAG & LLM Platforms

@paruls154

Hi With over 10 years of experience, my expertise is perfectly matched with your Azure RAG Recommendation Platform Build project. Throughout my career, I have created several full-stack applications on both web and mobile platforms, leveraging languages like Python, Java, and JavaScript. This has given me a deep-rooted understanding of architecting large-scale API solutions - a key expectation mentioned in your project brief. Getting into the details, I also ensure highest quality control in my work by running penetration testing with zero critical findings prior to delivery, thus alleviating any room for compromise or data breaches. Let's discuss how I can exceed your expectations with this project! Regards Parul Saini

@realroshan102

Hi, I am excited about your project to build an enterprise-grade Azure-native product recommendation platform using LLM-powered Retrieval-Augmented Generation. With extensive experience in Azure Cognitive Services, Azure Machine Learning, and Azure AI Search, I will design a secure, scalable solution architected with ARM/Bicep or Terraform for deployment repeatability. Your PostgreSQL catalogue will be seamlessly integrated to guarantee accurate, real-time recommendations with sub-second latency. I will also develop a clean REST/GraphQL API and a conversational UX using Bot Framework, all secured with Azure AD, RBAC, and Key Vault. I am ready to deliver detailed architecture diagrams, full CI/CD pipelines, and comprehensive operational playbooks. Let’s discuss your timeline for phased milestones and deployment strategies. What specific business goals or KPIs do you want this recommendation platform to achieve? Best regards, Roshan

@asadr93

✔ I deliver 100% work — 99.9% is not for me. ✔ Workflow Diagram PostgreSQL Catalogue (Read-Only) ⟶⟶ Azure AI Search Indexing ⟶⟶ RAG Orchestration Layer ⟶⟶ LLM Inference (Azure OpenAI) ⟶⟶ Secure API Layer (REST/GraphQL) ⟶⟶ Web / Mobile / Chat Consumption Key Highlights ✔ Azure-first, enterprise-grade architecture — App Services, Azure AI Foundry components, Azure AI Search, and secure networking deployed via ARM/Bicep or Terraform. ✔ Grounded RAG intelligence — Retrieval-Augmented Generation strictly constrained to your PostgreSQL product catalogue (read-only), ensuring every recommendation is traceable and verifiable. ✔ High-performance semantic search — Azure AI Search with vector + keyword hybrid retrieval for sub-second top-10 recommendations. ✔ Clean API surface — well-documented REST and/or GraphQL APIs separating orchestration, business logic, and presentation layers. ✔ Conversational-ready design — optional chat endpoint or Azure Bot Framework hook reusing the same RAG orchestration pipeline. ✔ Security by design — Azure AD authentication, RBAC, managed identities, Key Vault, private endpoints, and audit logging enabled from day one. ✔ Production-ready delivery — CI/CD pipelines, monitoring, rollback strategy, and operational playbook included. Best Regards, Mudassir Shahid Azure Solutions Architect | LLM & RAG Specialist | Enterprise API Systems

@jagratip3

Dear Hiring Manager, I’d be glad to design and deliver an Azure-native, enterprise-grade RAG recommendation platform fully aligned with your requirements. My approach is Azure-first, secure by default, and production-ready from day one. Proposed approach Architecture: Azure App Services + Azure AI Search for semantic retrieval, Azure OpenAI via Azure AI Foundry for generation, and read-only ingestion from PostgreSQL. All resources provisioned using Bicep/Terraform. RAG intelligence layer: Deterministic retrieval (top-K from AI Search) with strict grounding rules so LLM responses reference only traceable catalogue IDs. Prompt templates, orchestration logic, and unit tests included. API layer: Clean REST and/or GraphQL interface (OpenAPI documented), separating orchestration, retrieval, and business rules for easy reuse across web, mobile, and chat. Conversational UX: Optional Bot Framework or chat endpoint reusing the same RAG pipeline—no duplicated logic. Security & ops: Azure AD auth, RBAC, managed identities, Key Vault, audit logs, App Insights, latency budgets, and rate limiting. Deliverables Architecture diagrams (high-level + components) Full IaC for a clean-subscription deploy RAG pipeline code, prompts, tests API docs + sample chat UI CI/CD via GitHub Actions or Azure DevOps Monitoring & rollback playbook Performance focus Sub-1s latency via indexed retrieval, async orchestration, and response caching where appropriate. Best regards,

@karthikresonite

Hello ? This is Resonite Technologies, an Azure-native AI engineering team with proven experience building enterprise-grade RAG and recommendation platforms fully within Azure. We can deliver your LLM-powered product recommendation system grounded only in your PostgreSQL catalogue, with strong security, performance, and repeatable deployments. Our solution: • Azure-first stack: App Services, Azure AI Search, Azure OpenAI, Azure ML • RAG pipeline with semantic search + orchestrated LLM calls • Clean REST/GraphQL APIs for web, mobile, and future channels • Optional chat endpoint / Bot Framework reuse • Security by design: Azure AD, RBAC, Managed Identities, Key Vault, audit logs • IaC via ARM/Bicep or Terraform • CI/CD with Azure DevOps or GitHub Actions Deliverables: • Architecture diagrams • IaC + RAG pipeline code & tests • OpenAPI/Swagger docs + sample chat UI • Ops playbook (monitoring, rollback) Guarantees: • Data sourced only from PostgreSQL • ≤1s latency for top-10 recommendations • Fully reproducible deployment from clean Azure subscription Timeline: ~6–8 weeks for enterprise MVP. Happy to share examples of Azure RAG solutions we’ve delivered. Resonite Technologies

@RobeelHassan

Hi, I can design and deliver an Azure-native, enterprise-grade RAG recommendation platform fully grounded in your PostgreSQL catalogue data. I have hands-on experience with Azure AI Search, Azure OpenAI/Cognitive Services, Azure Machine Learning, and secure API platforms, and I’ll implement the full solution using IaC (Bicep/Terraform), managed identities, RBAC, and Key Vault from day one. You’ll receive clean REST/GraphQL APIs, optional conversational endpoints, full CI/CD, and clear operational documentation, with performance and security built to meet your acceptance criteria.

@keaganhaynes14

I am a perfect fit for your project. Your need for a clean, professional, and fully Azure-native product-recommendation platform that integrates seamlessly with your PostgreSQL catalogue and provides an automated, user-friendly recommendation engine is clear. While I am new to Freelancer, I have extensive real-world experience and have completed multiple projects off the platform. I bring deep expertise in Azure AI Search, Cognitive Services, and Machine Learning pipelines, alongside building secure, scalable APIs with automated CI/CD workflows. I focus on delivering architecture and infrastructure as code that is maintainable, well-documented, and performance-optimized. I would love to chat more about your project! Regards, keagan

@satyamy73

As a Freelance DevOps Engineer and Microsoft Azure aficionado, I am confident that my skills and experience make me the ideal candidate for this project. Having worked extensively on Azure, with ARM/Bicep and Terraform deployments, I am well-versed in constructing secure and scalable architectures that meet enterprise-grade standards. I have deep-rooted understanding of Azure AI Search, Azure Cognitive Services, and Azure Machine Learning - all key components for your recommendation platform. My forte lies in not only designing but also delivering end-to-end solutions like the one you require. I strive to ensure that my solutions are clean and robust, separating business logic from presentation as necessary. This aligns perfectly with your need for a REST/GraphQL layer which facilitates interaction across multiple platforms while also giving future-proofing options. Additionally, my strong background in security includes Azure AD authentication, RBAC set-ups, Key Vault usage and comprehensive logging to ensure data integrity. Lastly, let me assure you that the trust you instill in me will not be betrayed. You mentioned that repeatable deployment from scratch is an important criterion. To this end, all of my deployments come with well-structured IaC scripts which will be thoroughly tested on a clean Azure subscription to ensure compatibility.

@gigatorb

Hi, I’m Lav, a Senior Software Engineer & Solutions Architect with 18+ years of experience delivering secure, Azure-native enterprise platforms using Microsoft AI, Azure, and data-driven architectures. Understanding / Solution You need an Azure-only, enterprise-grade LLM-powered RAG recommendation platform grounded strictly in read-only PostgreSQL catalogue data, with sub-second latency, strong security, and clean APIs. I will design a fully Azure-native RAG architecture using Azure AI Search + Azure OpenAI, ensuring traceable, accurate recommendations. Key Features Azure-native RAG pipeline (PostgreSQL → Azure AI Search → Azure OpenAI) Semantic + vector search with strict data grounding REST & GraphQL APIs with OpenAPI docs Optional chat endpoint using same orchestration layer Azure AD auth, RBAC, Managed Identity, Key Vault Full CI/CD with repeatable IaC deployment Technical Questions Catalogue size and update frequency? REST only or GraphQL required initially? Preferred CI/CD tool (Azure DevOps or GitHub Actions)? Relevant Case Study Delivered multiple Azure-hosted, data-driven enterprise systems using Dynamics 365, Dataverse, Power Automate, and secure API layers, where all outputs were strictly sourced from authoritative databases, with Azure AD security, audit logging, and automated deployments. Thanks Lav

@Mmkdevops

Hey I have experience over 6 years in the fields you were mentioned, I am sure I will give you the solution. message me and lets discuss thanks.