Luna AI Shopping Assistant Development

@C0RETECHIES

Hi there, I understand you need to integrate a conversational shopping assistant, Luna, into your existing SmartGuide AI suite. The workflow will involve routing user queries via your MCP to a new RAG service. This service will vectorize the query, retrieve relevant smartphone data from a vector store, and use a generative model to formulate a response aligned with Luna's persona, using function calling to query your live stock service or execute bookings. Technical approach: - Python/LangChain for the RAG pipeline. - Sentence-transformer model for embeddings, stored in a ChromaDB or FAISS vector store. - Persona-driven system prompts with function-calling logic for stock/booking tools. - Containerize the new service with Docker for your K8s environment. Relevant systems: - Dexter (Internal Project): AI conversational companion built with custom persona management. - AI-Powered Chatbot Backend: Production backend with contextual memory and API integrations. Implementation strategy: We'll start with an MVP of the RAG pipeline, tuning for accuracy against your evaluation set. We'll then integrate the live stock and booking functions, writing comprehensive unit tests to ensure we meet the 80% coverage target before deployment. Regards, Rohit

@devendrathakur12

I would love if I get the chance to work on your project. Building Luna into your stack as a real, persona-consistent advisor is the kind of work I genuinely enjoy. I can build the RAG pipeline in Python with LangChain, embeddings in OpenAI or Cohere, and Pinecone or pgvector for the vector store, wired into your FastAPI gateway and MCP node. Luna's prompt strategy keeps her tone consistent while pulling live catalogue, benchmarks, and stock for context-aware picks, and confirms bookings into your reservations table. Docker deploy, 80%+ tests, and clear accuracy evaluation. Best, Dev S.

@pinkeegupta

Hi, I have reviewed your project requirements and I’m confident I can deliver accurate, data-driven, and scalable solutions for your needs. I bring 9+ years of combined experience in Python development, Data Science, Data Analytics, and Business Intelligence, helping clients turn raw data into meaningful insights and actionable dashboards. My Core Expertise Includes: Node js , React Js, Mongo , Blockchain, crypto currency Python Development: Pandas, NumPy, Scikit-learn, FastAPI, Flask, Django Data Science & Machine Learning: Data cleaning, EDA, predictive modeling, AI/ML solutions Data Analytics: Statistical analysis, reporting, automation, data mining Power BI: Interactive dashboards, DAX, Power Query, data modeling, KPI reporting Databases & Big Data: SQL, NoSQL, SparkML AI & Frameworks: TensorFlow, PyTorch, Cursor, Calude, gemini, nano, chatgpt. I focus on clean code, clear insights, performance optimization, and business-oriented outcomes. I ensure timely delivery and transparent communication throughout the project lifecycle. Let’s connect to discuss your requirements in detail and define the best approach for your project. Looking forward to working with you. Regards, Anju

@CodeAnchors

Greetings, I understand the intricacies of integrating Luna, the personalized shopping assistant, into your existing SmartGuide AI Agent & Management Suite. My expertise in Mobile App Development, API Development, and Generative AI positions me strongly to tackle this project. You can view relevant projects in my portfolio: ⭐⭐ https://www.freelancer.com/u/CodeAnchors ⭐⭐ My approach involves structured requirement confirmation, controlled planning, clean implementation, rigorous testing, and transparent milestone-based progress tracking. By focusing on persona design, RAG configuration, and accuracy metrics, I ensure reliable execution and long-term results. Could you share more insights on your top priority among persona design, RAG configuration, or accuracy measurement? Let's discuss further in an open chat to finalize the project scope seamlessly. Best regards, Muhammad Anas Khan

@emilesd617

Hi, I am a full-stack AI developer with 8 years of rich experience in software development. I am familiar with Python, FastAPI, Generative AI, Conversational AI, Prompt Engineering, RAG, Vector Databases, MCP, API Development, Docker, Kubernetes, and TypeScript. I have experience building AI agents that connect product data, live APIs, vector search, and booking workflows into a reliable conversational flow. For Luna, I would design the persona/prompt strategy, build a RAG pipeline over the smartphone catalogue and benchmark data, connect real-time stock and reservation APIs through the FastAPI/MCP layer, and add unit tests plus accuracy checks against your evaluation set. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@alizain721

Most projects stall not for lack of models but for weak grounding: without a tight RAG + realtime tool layer, recommendations drift from catalogue truth and bookings fail quietly. My approach is pragmatic: implement a RAG pipeline that retrieves product chunks from a vector store, runs a lightweight reranker, and routes tool-calls (stock check, booking) as deterministic actions the LLM can invoke. Prompts include a persona system prompt for Luna plus constrained response schemas so bookings and confirmations are atomic and auditable. I recommend Python for the service layer (FastAPI hooks you already expose), embeddings via OpenAI or small SBERT for on-prem, vector DB such as Milvus or Pinecone, Redis for short-term caching, and the MCP node as the orchestration point. UI interactions or small hooks can be TypeScript. I’ll ship Docker + K8s manifests, pytest unit tests, and evaluation scripts that measure precision@1, recall, and booking transaction integrity to verify the ≥90% and <2s SLAs. Design keeps persona rules separate so tone updates don’t touch core logic. I built PicMe (marketplace + in-app bookings) with FastAPI and booking webhooks—similar transactional needs and edge-case handling. Quick question: is your labeled evaluation set already staged with ground-truth product IDs and query-to-booking examples so I can calibrate the RAG/ranker? If yes, I’ll draft a kickoff plan and timeline.

@solutionsplayers

As a seasoned full stack developer, my proven expertise in PHP, Laravel, React JS, and Flutter aligns seamlessly with your project requirements. My 6+ years of experience and a solid portfolio in building scalable software solutions makes me confident in delivering an interactive and efficient shopping assistant in Luna that will remarkably leverage Generative AI with Retrieval-Augmented Generation. I am already familiar with working on APIs and the logic they require to bring about real-time access to various services, something that's critical for Luna to perform optimally. On top of that, I bring along an excellent understanding of Persona Design - a crucial aspect for Luna's conversational persona development as well as RAG Configuration. Identifying with your need for accuracy measurement approach, I assure you that I will have it well-handled. Also, my meticulous attention to detail further solidifies my commitment to clean coding guidelines ensuring well-tested code functionality at all times. This is not just another project for me; every collaboration is an opportunity to build something great—together! Hence, I look forward to joining forces with you and aiding your aim: an enhanced shopping experience through an engaging AI assistant powered by futuristic technology. Let’s connect today to discuss how I can shape the intricate parts of this project concordantly with your vision!

@billybryan98

Hello, I’ve read your Luna AI Shopping Assistant mission and I’m confident to wire Luna into your SmartGuide stack as a persona-first conversational layer. I’m an independent developer with hands-on experience in Python, FastAPI, and Generative AI-driven chat experiences, and I’ll tailor Luna to be context-aware with your catalogue, stock service, and reservations webhook. I will design a Retrieval-Augmented Generation pipeline using your product data, benchmarks, and availability endpoints. I’ll select a vector store (FAISS or Pinecone) and craft prompts and embeddings to ensure persona consistency and 90%+ recommendation accuracy on your eval set, while keeping real-time stock checks under 2 seconds. I’ll implement the booking flow so confirmed reservations update the reservations table and trigger the webhook, with thorough unit tests and Docker/K8s deployment scripts. I’ll deliver clean Python (or TS for UI hooks) with clear README and maintainable docs. Next steps: I can start after access is granted; I’ll provide a concrete timeline and measurable tests. Best regards, Billy Bryan

@craigd43

As someone deeply rooted in both the art and science of coding, I bring an expansive and adaptable skillset that fits seamlessly with your Luna AI Shopping Assistant project. My experience ranges from being proficient in Graphic Design to Mobile App Development, allowing for me to have a holistic outlook on your project. Although my Resume may not shout out FastAPI or any persona-specific development, my knowledge and proficiency in Python guarantee a solid delivery. For the personalized shopping-assistance layer wiring you require, where the core function is to offer context-aware product recommendations, query live stock service in real-time and create & confirm customer bookings, I can strategically employ my FastAPI skillset to connect Luna into your existing stack effectively. I'm keen on maintaining the prompt strategy, RAG pipeline and embedding choices sound, since this will ensure a highly reactive Luna who stays persona-consistent. I'd also love to share my approach on persona design, RAG configuration and measurement of accuracy to forge a stronger alignment. Given the chance to work with you on this Luna AI project, you won't just get a developer but an invested partner who's committed to timely and high-quality results that exceed all expectations. So let's sync our calendars!

@nicolasalexandro

Hey — saw your post about building the Luna AI Shopping Assistant. A common snag here is making sure the AI stays responsive while handling async tasks in the dashboard. Quick question before I suggest a plan: Is your current SmartGuide AI built on a specific framework, or open to integrating new tech for better async performance? I’ve developed AI assistant dashboards with smooth async handling and intuitive UIs before. If you send over your spec or current setup, I’ll take a look and share what’s doable.

@joshlenp

Your existing architecture already has the right foundation, which means I can focus directly on building Luna into a production-ready AI shopping assistant rather than rebuilding infrastructure. I specialize in FastAPI, AI orchestration, RAG pipelines, async systems, and conversational AI. I can integrate Luna with your MCP server and FastAPI gateway to deliver: Context-aware smartphone recommendations Real-time stock checks Reservation and booking confirmations Persona-consistent conversational responses My approach would use a hybrid RAG architecture with semantic retrieval + metadata filtering to improve recommendation precision and meet the 90%+ accuracy target. I’d recommend Qdrant or pgvector for vector search, async FastAPI orchestration for low latency, and Redis caching for faster inventory lookups. Luna’s persona would be implemented using structured prompting, retrieval grounding, and response guardrails to ensure she remains enthusiastic, accurate, and consistent across conversations. For bookings, I would implement: Live stock validation Reservation creation Webhook triggering Error handling and retries I would also provide: Docker/Kubernetes deployment configs Unit + integration tests with 80%+ coverage README documentation CI/CD-ready structure Once I receive API docs, schemas, MCP specs, and staging access, I can begin implementation immediately. Regards,

@dataspro

Nice to meet you ,The requirements of your project match my areas of work and skills, to introduce myself. My name is Anthony Muñoz and i am the lead engineer for DS Pro IT agency. I have worked for over 10 years as a Full-Stack and software development engineer and have successfully done multiple jobs. It will be a pleasure to work together to make your project. Feel free to discuss about the project with me, greetings.

@RadoslavPompura

Hello, I can integrate Luna into your existing SmartGuide AI Agent & Management Suite as a personalized smartphone shopping assistant. Since your UI, admin dashboard, FastAPI gateway, async engine, and MCP server are already in place, I will focus on building the RAG-based recommendation layer, live stock lookup, and booking workflow. My approach is to design Luna as an enthusiastic but accurate advisor, with prompts that keep her persona consistent while grounding every recommendation in your smartphone catalogue, benchmarks, and availability data. I would use a vector store such as pgvector, Qdrant, or Chroma for product retrieval, then call the live stock API directly for real-time availability under the 2-second target. For bookings, Luna will validate the selected product, check stock, create the reservation, confirm it to the user, and ensure the existing webhook is triggered. I will also add tests, Docker/K8s scripts, and README documentation. Recommendation accuracy will be measured against your evaluation set using top-k match, constraint matching, and product relevance.

@Foraxis

⭐ I handled a similar project ⭐, Happy to show you what works before you commit. I developed a personalized shopping-assistance layer aligning Luna with conversational logic. Specializing in performance, security, and user experience, I ensure Luna offers accurate recommendations, real-time stock checks, and seamless booking confirmations. Understanding the project's complexity, my focus is on implementing Generative AI with Retrieval-Augmented Generation. Let's chat for a free consultation to discuss your project needs further. Worst case, you walk away with a free consultation and a clearer understanding of your project. Kind regards, Curtley

@kuldeepsharma30

Hello, This project aligns strongly with my experience in AI-driven conversational systems, RAG pipelines, FastAPI integrations, and enterprise-grade assistant orchestration. My implementation approach for Luna would focus on three layers: • Persona orchestration and conversational consistency • Retrieval-Augmented Generation pipeline optimization • Real-time action execution (stock + booking workflows) Planned architecture: • Embedding pipeline using product specs, benchmark feeds, and availability data • Vector search layer (pgvector, Qdrant, or Pinecone depending on scale/performance goals) • Prompt-engineering layer focused on maintaining Luna’s enthusiastic smartphone-advisor personality while preventing hallucinations • FastAPI integration for stock validation and reservation execution • MCP integration to keep conversational state synchronized across workflows For recommendation quality, I would implement: • Evaluation datasets with benchmark scoring • Retrieval relevance testing • Response grounding checks • Persona consistency validation • Latency monitoring for stock and booking operations Tech stack can include: • Python + FastAPI • LangChain/LlamaIndex-style orchestration • OpenAI/Claude-compatible models • Docker/Kubernetes deployment • Automated unit/integration testing I can also structure the RAG pipeline for future expansion into accessories, bundles, upselling, and multilingual support without major refactoring. Regards, Vk

@ivicab6

Hi, I've built personalized shopping-assistance systems using AI and have integrated them into existing tech stacks. With experience in FastAPI, Generative AI, and RAG pipelines, I can wire Luna into your SmartGuide suite to offer real-time, context-aware recommendations and bookings. Let's start with a small test task to ensure we align before diving into full implementation. Looking forward to working together. Best Regards, Ivica

@ammark477

One thing most freelancers miss with complex integrations like this is the importance of understanding both the existing architecture and the persona's role in user experience. For Luna, it’s not just about providing recommendations,it’s about making every interaction feel natural and responsive. I plan to wire Luna into your stack by designing a prompt strategy that will shape her responses based on the context of the user’s queries. With your FastAPI setup, I can pull product data in real-time and ensure stock checks are lightning-fast, all while maintaining that desirable persona consistency. I've worked extensively with AI-driven applications, particularly in e-commerce, where personalization and speed are crucial. My experience spans deploying clean Python code and ensuring high accuracy in recommendation systems. For measuring accuracy, I suggest implementing a test suite right away to continuously evaluate and refine the model. This way, we can hit that 90% accuracy mark together. I'll need about 14 days to deliver the initial working version. Want me to sketch a quick action plan so you can see the approach?

@pedrowilberthr2

Hello, I am interested in building out the Luna AI shopping assistant layer and integrating it into your existing SmartGuide AI Agent + MCP + FastAPI architecture. Since your core system is already in place, my approach would focus on extending it rather than rebuilding anything. I would design Luna as a persona-driven orchestration layer sitting on top of your current MCP server, with a clear separation between conversation logic, retrieval, and tool execution. For the RAG pipeline, I would use your existing product catalogue and benchmark feeds to generate embeddings and store them in a vector database optimized for low-latency retrieval (for example pgvector or a managed vector store depending on your infra). The retrieval layer would be tightly integrated with your FastAPI gateway so that stock checks and product queries remain real-time and consistent. For persona design, I would implement a structured system prompt combined with state-aware conversation memory, ensuring Luna remains consistent in tone (enthusiastic smartphone advisor) while still strictly grounding recommendations in retrieved product data. Tool calls (stock checks, booking reservations) would be handled via function-calling style routing through your MCP node. To meet your performance targets, I would optimize caching for frequent product queries, ensure embeddings are precomputed and indexed properly, and keep stock API calls asynchronous with timeout handling to guarantee sub-2-second responses.

@ApieTech

Hi — read through Luna's scope. Persona-consistent RAG agent over a smartphone catalogue with live stock + booking actions tied through your MCP node — the moving parts are the prompt strategy, retrieval relevance, and tool-use for the booking webhook. We'd start with a small evaluation set so we can hill-climb that 90% accuracy bar rather than guess at it. Stack: pgvector or Qdrant for embeddings, hybrid BM25+vector retrieval, structured tool calls for stock and booking, and tight prompt/persona scaffolding so Luna stays in character across multi-turn flows. We've shipped two production RAG assistants recently — retail catalogue and a medical knowledge base, both with tool calls and FastAPI gateways. Ping me — happy to walk through the prompt + retrieval plan and our accuracy methodology before we start.