AI E-Commerce Sales Boost Platform

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@WOFZahid

Hi, One line in your acceptance criteria worries me more than the build. A 5% CTR uplift isn't something a developer controls. It depends on your catalog size, your traffic and how clean the event data is. I can build the engine and the test. I can't promise the number. The A/B test has a related problem. Detecting a 5% relative lift needs a lot of traffic before the result means anything. On a modest store that pilot runs for months. Worth checking your monthly sessions before anyone commits to it. On latency, 200ms at P95 is fine, but not by running a model on each request. You precompute candidates per user overnight, serve them from Redis, and re-rank live using the current session. Real-time applies to the signals, not the training. Also, collaborative filtering needs existing interaction data. Deep learning doesn't fix an empty log. A new store needs content-based recommendations first. Straight with you, I'm a full-stack developer who has shipped one Python ML build, not an ML engineer. I'd be a good fit for the pipeline, the widgets and the dashboard. How many sessions a month does the store do?

@KarthikTechCon

Hello, I'm Karthik, with over 15 years of experience in building scalable e-commerce solutions. I understand your goal of enhancing online revenue through an AI-driven recommendation engine. My approach will focus on creating a robust platform that seamlessly integrates with popular stacks like Shopify and WooCommerce. Key highlights of my proposal: - Personalized Recommendation Engine: I will develop a system that analyzes user behavior and contextual signals to deliver tailored product suggestions, ensuring relevance and increasing sales. - Scalable Data Pipeline: I will implement a real-time data pipeline to capture events and serve predictions with low latency, optimizing for performance. - Admin Dashboard: A comprehensive dashboard for A/B testing and analytics will be included, enabling you to monitor CTR, AOV, and revenue attribution effectively. Deliverables will include a production-ready storefront, source code repository, and detailed documentation. I’m committed to meeting your performance targets and ensuring a seamless deployment. Looking forward to collaborating on this exciting project! Best, Karthik

@ammarahmed20

By choosing me, Ammar Ahmed, you're securing a dedicated professional with an unwavering commitment to crafting automated solutions that drive tangible results. My 40+ production deployments across various domains and my comprehensive understanding of AI and e-commerce makes me an excellent fit for your project. I have a proven track record of optimising sales performance through personalised recommendation engines and I am confident I can help you achieve a minimum 5% uplift in click-through rate. In conclusion, my passion for building AI agents that actually work for clients is what sets me apart in this competitive field. My project history reflects a mindset focused on practicality and impactful deliverables that align with my clients' true needs. With me on board, you'll gain a partner who delivers high-quality work on time - every time. Let's boost your sales through relevant recommendations while crafting an infrastructure that'll withstand all scales of traffic peaks!

@tyronet2

The recommendation system should be a modular revenue service, not an AI feature coupled tightly to one storefront. I propose storefront adapters for Shopify, WooCommerce or React; an event API capturing impressions, views, carts, purchases and context; batch plus streaming pipelines; and a Python/FastAPI recommendation service backed by PostgreSQL, Redis and an analytics store. Phase one would establish data quality and benchmark popularity, content-based and collaborative models before introducing deeper models only where they demonstrate measurable gains. Cold-start users and products will be handled through metadata, contextual ranking and controlled merchandising rules. Predictions will be cached and precomputed where appropriate to maintain the 200 ms P95 target. The admin dashboard will support experiments, audience splits, exclusions, overrides and revenue attribution. Delivery includes automated tests, load testing, model monitoring, Docker/IaC, API and schema documentation, and staged deployment. I would plan 10–14 weeks for an MVP and pilot. Which commerce platform, monthly traffic, catalogue size and historical event data are available?

@mahads31

As an experienced digital product specialist, I’m eager to leverage my comprehensive skill set in data analysis, PHP, and website design to drive your vision of an AI-driven e-commerce sales platform to reality. With a knack for transforming complex requirements into efficient and scalable systems, I’m confident in my ability to not only develop a smart personalization recommendation engine for your target customers but also offer end-to-end solutions to ensure smooth integration with popular e-commerce stacks like Shopify and WooCommerce. Furthermore, I am well-versed in AI technologies such as TensorFlow and deep learning models that align perfectly with your vision of leveraging data fully to provide highly targeted and pertinent product suggestions. A robust architecture is pivotal to executing this effectively which is where my expertise in building scalable data pipelines becomes crucial. I can build a clean codebase with automated tests and infrastructure scripts that ensure reproducible, efficient deployment. Finally, choosing me means choosing a collaborative partner who can provide more than just technical expertise. I believe in forging long-term relationships by offering iterative feedback, incorporating datasets seamlessly, and engaging closely with you throughout the process. My utmost priority is your satisfaction so let's embark on this journey together and lift your online revenue beyond expectation.

@mengw3

I've spent 20+ years in full-stack and applied AI, including e-commerce platforms that serve each shopper a real-time, personalized product feed from their behavior, which is exactly this. A real-time recommendation stream is won on the data path and latency: capturing behavior as it happens, turning it into relevant suggestions, and serving them fast enough that they feel live. What I'd do: - Event capture of views, clicks and purchases feeding a recommendation engine. - A model that mixes collaborative and content signals, tuned to lift revenue not just clicks. - A low-latency API serving personalized product streams into your storefront. - Clean analytics so you can see what the recommendations are actually doing to sales. You get a storefront that adapts to each shopper in real time, measured against real revenue. One thing to confirm: is this a new build or a layer on an existing store (Shopify, WooCommerce or custom), and how big is the catalog? I can start right away.

@Preetbegra

Hello, I can build an AI-driven recommendation platform focused on increasing CTR, AOV and revenue through personalised product suggestions. I’d use a scalable React/Node.js architecture with an event pipeline, recommendation API and ML layer using collaborative filtering plus contextual signals, with cold-start strategies for new users/products. I can deliver on-page and email recommendation widgets, real-time/batch data processing, an admin dashboard for A/B testing and rule overrides, analytics and attribution, plus Docker-based deployment, automated testing and complete API/data documentation. I’ll also design the system to integrate cleanly with Shopify, WooCommerce or custom stores and target the required sub-200ms P95 latency. Thanks, Pritpal Kaur

@ankits0192

Hello, Your main challenge is not simply adding recommendations—it’s making them relevant enough to measurably increase CTR, AOV, and revenue while staying under 200ms P95. I understand you need: • Real-time + batch event/data pipelines • Personalized recommendations using behavior, purchases & context • Collaborative filtering/deep learning with cold-start handling • Shopify/WooCommerce/custom-stack integration • Admin dashboard for A/B testing, overrides & attribution analytics • Production deployment, testing, IaC and documentation My approach is to build the recommendation layer first, establish clean event/data schemas, train and evaluate models, expose low-latency APIs/widgets, then validate performance through controlled A/B testing and load testing. I can work across PHP/eCommerce, React/Node, ML/data pipelines and WooCommerce integrations, with a focus on scalable production implementation rather than a prototype. A couple of key questions: 1. Which storefront/platform should be the first integration target? 2. Do you already have historical browsing, purchase and product/catalog data? 3. What is the expected peak event/request volume? Once confirmed, I can propose clear milestones and a realistic delivery plan. I’d be happy to discuss the architecture and help turn the recommendation engine into a measurable sales-growth system. Best regards, Ankit

@mikes126

The key here isn’t just building an AI recommender—it’s proving that it actually increases revenue. I’d build Phase 1 around measurable recommendation quality, <200ms P95 serving latency, and a clean A/B testing setup so you can verify the uplift against your static control. My approach: Capture browsing, cart, purchase, and contextual events through a scalable batch + real-time pipeline. Start with collaborative filtering/content-based recommendations, then add deep-learning models where they improve relevance and cold-start performance. Serve recommendations through a low-latency API that can plug into Shopify, WooCommerce, or a custom React/Node storefront. Build widgets for product pages and email recommendations. Provide an admin layer for A/B tests, business-rule overrides, CTR/AOV/revenue attribution, and model monitoring. Containerize the system and provide reproducible deployment + automated tests. I’d structure the implementation so the recommendation engine remains platform-independent rather than locking the intelligence into one storefront.

@jaroslavcaprata

The core challenge is not simply generating recommendations, but building a measurable recommendation system that can ingest shopper behaviour, serve relevant products under 200 ms, and prove whether those recommendations actually increase revenue. I would separate the platform into event collection, feature/model pipelines, low-latency recommendation serving, storefront widgets, and experiment analytics. For phase one, I would start with a strong baseline using behavioural signals, collaborative filtering, popularity/context rules, and product metadata before introducing deeper models where the available dataset justifies them. This also gives a reliable cold-start path for new users and products. The serving layer would expose a clean API that Shopify, WooCommerce, or custom React storefronts can consume, with caching and precomputed candidates where needed to maintain P95 latency. The admin side would support A/B experiments, manual merchandising overrides, CTR/AOV tracking, and revenue attribution. I would containerize the services, add automated tests and load testing, and document schemas, APIs, deployment, and model retraining. One point I would treat carefully: a 5% CTR uplift should be an experiment target, not guaranteed before seeing traffic and dataset quality. What historical event volume and product/catalog size will be available for the initial model?

@muhammada742

Hello, I reviewed your project for AI E-Commerce Sales Boost Platform and I’d be glad to work on it. Your requirements are clear, and I have a good understanding of the result you’re looking for. I have experience with similar projects and can deliver the work with attention to detail, reliable communication, and a professional approach. You can also review my profile and portfolio: https://www.freelancer.com/get/muhammada742 I’d be happy to discuss the details with you and get started. Best Regards, Muhammad Abdul Samee

@maheshverma6831

Hello! I can build the AI recommendation engine with real-time event tracking, collaborative filtering, low-latency APIs, widgets and A/B analytics for Shopify, WooCommerce or React/Node. Let’s discuss details here.

@rohank2502

More of your visitors should leave having bought, because the products on screen felt picked for them. I can start right now. The hard part is getting the picks right, so in 24 to 48 hours you get a live sample on your products. You click, you judge, then we finish the rest. We track what people view and buy, show matching products on the page and in email, and give you a simple panel to test two versions. You keep the one that sells more. Which store should I use for the first live sample?

@tagztech76

Hello! I am an experienced full-stack and machine learning developer with a strong background in building high-performance eCommerce platforms and real-time Recommendation System architectures. To achieve your goal of boosting revenue through targeted, AI-driven product suggestions, my approach will focus on the following: Recommendation Engine & ML Layer: Utilizing proven libraries like PyTorch or TensorFlow to implement collaborative filtering and deep learning techniques (mitigating cold-start problems) to process user interactions and deliver sub-200ms prediction latency. Scalable Data Pipeline: Engineering robust batch and real-time event-streaming pipelines to capture user behavior smoothly and serve recommendations seamlessly. Seamless Integration & Admin Dashboard: Developing extensible widgets compatible with standard stacks (Shopify, WooCommerce, custom React/Node) alongside a comprehensive admin dashboard equipped for A/B testing, custom rules, and performance analytics (CTR, AOV lift). Robust Deliverables: Providing clean, production-ready source code, automated test suites, infrastructure-as-code scripts (Docker/Kubernetes), and thorough documentation including an API spec and setup guide. I am confident in meeting your acceptance criteria, including the P95 latency target and pilot A/B test lift. I would love to discuss your dataset provisioning and start building this out right away!

@shailender18

Hello, I understand you need a production-ready AI recommendation platform focused on increasing e-commerce revenue through real-time, personalized product suggestions. I have 6+ years of experience in eCommerce, machine learning, data analysis, AI integrations, and scalable web application development. I can build an end-to-end recommendation engine that captures browsing, purchase, and contextual events through a scalable batch/real-time pipeline. I’ll implement collaborative filtering and ML-based ranking with TensorFlow/PyTorch, along with cold-start strategies for new users and products. The platform can integrate with Shopify, WooCommerce, or custom React/Node stores through APIs/SDKs and provide low-latency recommendation widgets for product pages and email campaigns. I’ll also develop an admin dashboard for A/B testing, rule overrides, CTR, AOV, conversion, and revenue-attribution analytics. I’ll design the serving layer to target sub-200ms P95 recommendation latency, use caching and optimized inference, and validate performance through load testing. I’ll provide automated tests, Docker/infrastructure configuration, API and data-schema documentation, and a reproducible deployment setup. Best Regards, Shailender

@camilob44

The 5% CTR uplift in your acceptance criteria is decided by what you pick as the static control, not by the model. Beating an arbitrary list by 5% is easy. Beating a well-built bestseller list by 5% on a catalogue without months of interaction history is not, because collaborative filtering has nothing to learn from yet. So milestone one fixes the control and instruments the measurement before any model ships, and the A/B harness reports lift with a confidence interval rather than a single number. Rs 37,500 does not cover a storefront build, Kubernetes IaC, an admin dashboard and a walkthrough video. What it does cover is the part that produces the revenue. Phase one: event capture (view, add-to-cart, purchase) into Postgres, a content-based recommender that works on day one, an item-to-item collaborative layer that takes over as history accumulates, and a FastAPI endpoint holding P95 under 200ms off a precomputed candidate cache. The widget drops into WooCommerce or Shopify through a script tag. I have built the ingest-to-serve half of this before: a Go pipeline doing OAuth, incremental sync and deduplication into PostgreSQL, and a ClickHouse rebuild that cut a 21.7M-row table to 458K rows through argMax aggregation. Rs 30,000, 21 days, three milestones.

@bhargav922002

Hi , Good afternoon! I’ve carefully checked your requirements and really interested in this job. I’m full stack node.js developer working at large-scale apps as a lead developer with U.S. and European teams. I’m offering best quality and highest performance at lowest price. I can complete your project on time and your will experience great satisfaction with me. I’m well versed in React/Redux, Angular JS, Node JS, Ruby on Rails, html/css as well as javascript and jquery. I have rich experienced in Website Design, Recommendation System, Deep Learning, PHP, Data Analysis, Machine Learning (ML), Shopping Cart Integration and eCommerce. For more information about me, please refer to my portfolios. I’m ready to discuss your project and start immediately. Looking forward to hearing you back and discussing all details.. Please keep me informed

@citysceneglobal

Hi, I’d be excited to build your AI-driven recommendation platform focused on increasing e-commerce revenue through highly relevant, real-time product suggestions. I have experience with React/Node, e-commerce integrations, scalable APIs, data pipelines, and AI/ML solutions. I can develop the recommendation engine using browsing behavior, purchase history, and contextual signals, with collaborative filtering and deep-learning approaches to address cold-start challenges. I’ll also build low-latency widgets, an admin dashboard for A/B testing and analytics, and integrations for Shopify, WooCommerce, or custom platforms. The solution will include clean source code, automated tests, Docker/Kubernetes-based deployment, documentation, and API/data specifications. I’m comfortable working toward your <200ms P95 latency and measurable CTR uplift targets.