Agentic AI & RAG Platform Development

@Microlent

Designing a modular agentic platform around LangGraph and LlamaIndex requires a very specific approach to state management, especially when balancing multi-agent orchestration with autonomous tool invocation. Your requirement for a hybrid retrieval system with metadata filtering suggests this needs to handle more than just simple vector lookups. I've worked extensively on Python-based deterministic scoring engines where every stage of the reasoning pipeline must be auditable. In a six-milestone project like this, the biggest risk is usually the feedback loop between the reasoning agent and the response generation agent. Setting up a solid evaluation framework early prevents hallucinations from compounding across the workflow. I can help structure the document ingestion framework in Milestone 1 using a strategy that accounts for the specific chunking needs you listed later for Milestone 2. I can share a quick sketch of how I would handle the orchestration logic for the planning agent versus the reasoning agent to keep the system modular. Let's kick off with a small milestone for the initial framework development. You only release when you're happy. ~ Rajesh

@ZohaibRoy

⭐⭐⭐⭐⭐ Build an Advanced AI Platform with Retrieval-Augmented Generation ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for an AI Engineer/Machine Learning Engineer. You don't need to look any further; Zohaib is here to help you! My team has already completed over 50 similar projects in AI development. I will create a modular Agentic AI platform that focuses on intelligent document retrieval and multi-agent orchestration, ensuring high-quality deliverables within your budget. ➡️ Why Me? I can easily build your AI platform as I have 5 years of experience in AI and machine learning. My skills include Python programming, RAG implementation, and working with large language models. Additionally, I have a strong grip on related technologies such as Docker and Git, ensuring a comprehensive approach to your project. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing this with you in chat. ➡️ Skills & Experience: ✅ Python ✅ Retrieval-Augmented Generation (RAG) ✅ Agentic AI Systems ✅ LangChain ✅ LangGraph ✅ LlamaIndex ✅ Vector Databases ✅ Large Language Models ✅ Prompt Engineering ✅ Docker ✅ Git ✅ API Integration ✅ System Architecture Waiting for your response! Best Regards, Zohaib

@opencollar

Hi, I see you need a system that can retrieve info fast and coordinate multiple AI agents without much fuss. I will set up the knowledge base and data intake so your info stays fresh. I will also build a good search system with filters and smart ranking to find what matters most. For the multi-agent part, I will create an easy way for different AI parts to talk and work together. I’ll also link tools and automations to make workflows run smoothly. I believe clear chats, good quality work, and quick results can help your project grow, plus I will support you even after launch. Let’s talk about how we can plan and create something impressive together. Regards, Nick.

@diektech

Hi — Elias here from Miami. I see you're looking to develop an AI and RAG platform, which aims to enhance decision-making and automate processes. This is an exciting venture with significant potential. The real technical challenge often lies in ensuring scalability and maintainability. As the system grows, managing data efficiently and integrating with diverse sources can quickly become complex. Additionally, ensuring reliable retrieval while maintaining a seamless user experience is crucial. My approach would focus on a modular architecture, allowing for easy updates and integrations. I would prioritize implementing robust state management and a clear workflow to facilitate future expansions. This way, we can ensure stability while enabling you to adapt as needs evolve. I've worked on similar systems, particularly in AI-driven applications, where I navigated challenges around data handling and integration with existing infrastructures. A few questions to better understand the scope: Q1 – What specific user roles and permissions are you envisioning for the platform? Q2 – How do you see the integration with existing data sources? Q3 – What are your expectations for scaling the platform in the future? Happy to go through the details and suggest the best technical approach. Looking forward to hearing from you.

@irsolutions

With over a decade of experience in AI systems and high-scale projects, I understand the intricacies of developing a modular Agentic AI platform with Retrieval-Augmented Generation (RAG) like your project demands. My background in handling high-complexity systems, such as scaling for over 1 million users and developing FinTech solutions, directly applies to the challenges of intelligent document retrieval, multi-agent orchestration, and autonomous workflow execution. For strategic insight, ensuring scalability and security in AI projects is key. Leveraging my past success in building Telegram Mini Apps for a vast user base proves my ability to handle complex systems effectively. I am confident that my expertise in Python, AI systems, and Docker align perfectly with the requirements of your project. I am excited about the opportunity to discuss how I can bring my skills to your Agentic AI & RAG platform development. Please feel free to reach out so we can further explore the roadmap for this project.

@ludiac

Hi, Your project aligns closely with my experience building RAG platforms, multi-agent AI systems, agent orchestration frameworks, and production-grade LLM applications. I have developed systems involving: - Hybrid RAG pipelines with vector databases and reranking - LangChain, LangGraph, and LlamaIndex workflows - Multi-agent architectures (planner, researcher, retriever, reasoner, executor) - Autonomous tool-calling and workflow automation - Evaluation frameworks, hallucination detection, and monitoring - Dockerized deployments with complete CI/CD pipelines For this platform, I would implement a modular architecture that allows each milestone to build upon a scalable foundation while maintaining clean separation between retrieval, memory, orchestration, and execution layers. Relevant AI Projects: https://www.freelancer.com/projects/php/Sharepoint-RAG-SQL-GPT-agent/reviews https://www.freelancer.com/projects/php/SQL-RAG-GPT-Agent-with/details I can provide well-documented source code, architecture diagrams, Docker deployment packages, and long-term support throughout the project lifecycle. Thanks.

@intelliresponse

Hello, I carefully reviewed your Agentic AI/RAG platform requirements and understand that this is a research-oriented, implementation-focused system covering ingestion, vector search, retrieval optimization, multi-agent orchestration, tool use, memory/session handling, evaluation, monitoring, Docker deployment, and full documentation. I have experience with Python, RAG pipelines, LangChain/LangGraph, LlamaIndex, vector databases, LLM workflows, prompt engineering, Docker, Git, and modular AI system design. I would approach this in clear phases: architecture/data ingestion, hybrid retrieval and reranking, agent orchestration, tool registry/autonomous workflows, evaluation/monitoring, and production packaging. I’d be happy to discuss a realistic milestone-based payment structure aligned with the six phases.

@Mahihassi

As an experienced AI Engineer and a committed developer who's contributed to many complex AI-driven projects, I believe I'm the ideal candidate for this ambitious Agentic AI & RAG Platform Development project. With deep expertise in Python, Retrieval-Augmented Generation (RAG), Agentic AI Systems, and LangChain just to name a few, I can ensure efficient implementation of intelligent document retrieval, multi-agent orchestration, tool invocation - all while making memory management and workflow execution autonomous. Over the years, I've gained significant proficiency in building scalable backend systems backed by my mastery in Vector Databases and with Large Language Models like LlamaIndex,LangGraph. My work revolves around developing robust architectures using Docker and Git for code management. Prompt Engineering is another skill most relevant for this project which would allow me to build a sophisticated platform that understands context and adaptively retrieves information. Having worked on similar implementations before, I understand the importance of modular design and system efficiency which are crucial for research-oriented applications like this one.

@Herculesz

Hi I can help build your modular Agentic AI and RAG platform covering document ingestion, retrieval optimization, multi-agent orchestration, tool invocation, memory, evaluation, and deployment. I have experience with Python, LangChain, LangGraph, LlamaIndex, vector databases, embeddings, hybrid search, reranking, prompt engineering, Docker, Git, and LLM workflow design. A key technical problem is making retrieval, agents, tools, memory, and evaluation work together reliably without hallucinated answers or unstable workflow execution. I would solve this with a clean modular architecture, structured ingestion pipeline, metadata-aware retrieval, agent communication rules, tool registry, session management, and repeatable evaluation benchmarks. For the agent layer, I can design planning, retrieval, reasoning, and response agents with clear responsibilities and controlled tool usage. I can also add monitoring, hallucination checks, performance reports, Dockerized deployment, architecture diagrams, and complete technical documentation. My focus is to deliver a research-friendly but implementation-ready platform with clean source code and maintainable AI workflows. Thanks, Hercules

@C0RETECHIES

Hi there, I see you're building an autonomous research pipeline. The system will ingest documents into a vector store, after which a multi-agent framework will handle queries: a planning agent breaks down the task, a retrieval agent fetches context using advanced RAG techniques, a reasoning agent uses tools to process information, and a final agent synthesizes the response. This entire flow is an orchestrated, stateful process. Technical approach: We'll build the backend in Python with LangGraph for the multi-agent state machine. We will use LlamaIndex for the RAG pipeline due to its advanced retrieval options and a Dockerized ChromaDB for the vector store. The entire system will be containerized for modularity and easy deployment. Core modules: - Ingestion Pipeline: Automated parsing, intelligent chunking, and embedding of source documents into the vector store. - Agentic Workflow: A graph-based system defining how Planner, Retriever, and Tool-Using agents collaborate to resolve complex tasks. - Hybrid Retrieval Engine: Combines semantic and keyword search with a reranking layer to ensure the most relevant context is passed to the LLM. Relevant systems: - Automation Lead Generator (8-Agent AI Pipeline) - AI-Powered Slack Clarification Assistant (LangChain-based) We will build according to your milestones, ensuring a solid data ingestion and retrieval foundation first. Each component will be modular and testable, allowing us to reliably build the more complex agentic layers on top. The architecture will be designed for future scaling from the start. Regards, Rohit

@mubeenm5026

As the founder and CEO of Web Crest, I bring to your project a decade of hands-on experience in developing powerful digital solutions that perform at scale. Our focus on practical innovation aligns perfectly with your project's need for an Agentic AI platform powered by RAG. Combining our expertise in AI, Python, and Software Architecture, we will deliver a modular and scalable system that meets your evolving needs. Specifically, our proficiency in LangChain, LangGraph, LlamaIndex and Vector Databases directly address the knowledge infrastructure and data ingestion challenges described in Milestone 1. We are also well-versed in designing optimal retrieval and search processes as outlined for Milestone 2. In fact, we have developed several retrieval agents and the related chunking strategy optimization, alluding to our potential to deliver success on Milestone 3 as well. Regarding communication and teamwork, clients admire our effective collaboration model. Our transparent workflow and consistent progress reporting will ensure that you can easily track progress throughout the staggered development process. Moreover, our past clients have always appreciated how well we’ve understood the complex interplay between technology and business impact - allowing us to deliver not only functional projects but real value.

@yuliussmayoru

Hello! Building a modular agentic RAG platform with solid retrieval, multi agent orchestration, and production ready deployment is exactly the kind of Python and LLM systems work I focus on. Here is my brief approach Milestone 1 and 2 I will design the overall architecture and knowledge layer first, including document ingestion, preprocessing, chunking, embeddings, and an initial vector database. Then I will implement hybrid retrieval with metadata filters, query expansion, reranking, and a small evaluation harness so retrieval quality is measurable from early on. Milestone 3 and 4 I will implement a multi agent architecture where a planner, retriever, reasoner, and responder coordinate through a graph style orchestration flow. On top of this, I will add a modular tool registry, external API integrations, task decomposition logic, and session management so agents can execute autonomous workflows rather than single shot answers. Milestone 5 and 6 I will build evaluation, monitoring, and hallucination checks around the RAG pipeline, then add a lightweight monitoring dashboard and optimization passes. Finally, I will containerize the platform, prepare the full source repository, diagrams, installation guide, and user documentation so you get a clean, maintainable codebase at handoff. Warm regards

@Hubready

Hello! I am a Florida-based senior software engineer with extensive experience in AI development, including Java, Python, and Django. I've read your project description carefully and I'm excited about the opportunity to develop your Agentic AI & RAG Platform. With around 15 years in the field, I have a solid grasp of the technologies required and am committed to delivering a solution that meets your goals effectively. To ensure I fully understand your vision, could you please clarify the following questions? 1. What specific functionalities do you envision for the RAG platform? 2. Are there any particular performance metrics or benchmarks you want to achieve during development? My approach includes clearly defining project milestones and ensuring robust communication throughout the process. I’ve successfully built systems that integrate AI solutions, such as intelligent workflow automation and LLM integrations. I have also developed custom APIs and data pipelines for various applications, ensuring they are scalable and maintainable. If you’d like to see relevant examples of my work, I can share links to projects that align closely with your needs. I am looking forward to the possibility of discussing your project further! -James

@Chris9753

Hello, I have reviewed your job description carefully. I can develop a modular Agentic AI platform powered by Retrieval-Augmented Generation (RAG) that meets all six milestones. I will implement knowledge ingestion and preprocessing pipelines, a vector database, optimized retrieval strategies, multi-agent orchestration, tool integrations, autonomous workflows, and monitoring dashboards. Each milestone will include clean, well-documented Python code using LangChain, LlamaIndex, LangGraph, vector databases, and Docker, with thorough evaluation and performance optimization. The final deliverable will be a containerized, production-ready system with full source code, installation guides, architecture diagrams, and documentation. I am experienced in RAG-based AI systems, multi-agent design, and prompt engineering, and can work part-time to consistently deliver high-quality progress across all milestones within your 180-day timeframe. Looking forward to your response, Chris

@shakilan4

Hi, I’m interested in this project and have experience with Python, RAG systems, LangChain, LangGraph, LlamaIndex, vector databases, LLMs, prompt engineering, Docker, and AI workflow automation. I can help design and develop a modular Agentic AI platform with multi-agent orchestration, retrieval optimization, tool integration, evaluation frameworks, and production-ready deployment. Best regards, Shakila Naz

@mahads31

With my extensive experience in building smart and scalable digital solutions, I am fully equipped to deliver a high-quality, research-oriented Agentic AI platform powered by the Retrieval-Augmented Generation (RAG) technology you need for this project. Having worked extensively with Python - one of your desired skills - I have developed a strong proficiency with its retrieval, search optimization and vector database packages. Milestone 3 on Multi-Agent Architecture is one area where my agile and innovative thinking can be key. By creating reliably multi-functional planning, retrieval, reasoning and response generation agents with an efficient agent communication workflow, I'll make sure the platform provides a smooth user experience while maintaining maximum productivity and autonomous workflow execution. To demonstrate my dedication to the quality of deliverables as required by this project, I will provide comprehensive technical design documentation for each completed milestone along with detailed source code repositories. My commitment ensures not only meeting but exceeding your expectations at every stage staring from RAG benchmarking framework all through to final technical report.

@juanpablomacf

Hello, I’m Juan Pablo. I have deep experience building agentic AI platforms with RAG, LangChain, LangGraph, LlamaIndex and vector‑based retrieval, and I can deliver the modular system you’re aiming for. I’ve developed architectures with large‑scale ingestion pipelines, hybrid retrieval, reranking, multi‑agent orchestration, tool‑calling, memory layers and autonomous workflows, always with clean modularity, deterministic behavior and strong documentation. I can structure the platform exactly around your six‑milestone roadmap: starting with the knowledge infrastructure and ingestion layer, then optimizing retrieval, building the multi‑agent architecture, integrating external tools and autonomous execution, followed by evaluation, monitoring and hallucination analysis, and finally packaging everything for production with Docker, Git and full documentation. My approach focuses on predictable pipelines, transparent reasoning, and RAG configurations that minimize hallucinations while keeping retrieval fast and accurate. If you want to explore agentic architectures or RAG optimization methods I can expand. I’m ready to take on the full project and deliver the integrated platform within the defined scope.

@mzdesmag

Hello sir, Did go through your job description and glad to share that I have enormous experience in working with Agentic AI & RAG Platform Development Agentic AI & RAG Platform Development I'm a seasoned programmer and Engineer with quality experience in Flutter, React, Node.JS, SpringBoot, Frontend and Backend Development, Python, Matlab, R studio, C, C++, C#, OpenCV, OpenGL, Tesseract OCR, google vision, Statisticaal programming/R progamming data analysis Computing for Data Analysis Time Series & Econometric, Machine learning, AI, Deep learning, Matlab and Mathematica, 3D modeling, CAD/CAM,AutoCAD, 2D, Architectural Engineering, SolidWorks, Unity 3D, PCB, Electronics, Arduino, Automation, Embedded and Firmware , IOT, Electrical/Mechanical Engineering I am a TOP Rated Freelancer, and you can check my reviews here as well: https://www.freelancer.com/u/mzdesmag. Looking forward to potentially working together on this project. Thanks and Best regards, Adekunle.

@alizain721

You need a research focused, modular Agentic AI platform where RAG, multi agent orchestration, and memory are rock solid from day one — starting with Milestone 1 makes sense because bad ingestion wrecks everything downstream. The real issue is not building agents it is making retrieval reliable and inexpensive while keeping context length and hallucination under control. I built the backend for CrowdAxis where I designed ETL pipelines, real time normalization, and an ML model served behind FastAPI that the frontend relied on for live scoring and decisions. Plan for the six milestones - Draft architecture and data model, pick vector store and embedding strategy, and deliver diagrams - Implement ingestion with chunking rules, preprocessing, and embedding pipeline using LangChain or LlamaIndex - Add hybrid retrieval reranking, metadata filters, and query expansion tests - Build agent orchestration with LangGraph patterns, tool registry, and session memory - Create evaluation benchmarks, monitoring, and containerized deployment Do you have a preferred LLM provider and a sample corpus I can use? If you share a small document set I will prepare an architecture diagram and an ingestion prototype for review.