AI Price Arbitrage Tool -- 2

@Microlent

Hello, I am Dr. Rajesh Rolen, PhD in Computer Science & Engineering, with experience of over 20+ years in API Development, PHP, Web Scraping, Software Architecture, Data Visualization, Python As a preferred freelancer in the top 1%, I have done 400+ projects here on freelancer.com, I have 4.9 ratings out of 5 on average, which showcases my quality of work and timely delivery. Key Highlights: - Free Hosting Support on the Cloud or any desired platform. - Free 3 months of post-delivery support to ensure that our client doesn’t face any challenges after the launch of the project. - Free Dedicated tester on projects to ensure quality delivery, so clients don’t need to act as a tester. - 10+ Years experience UI/UX team to ensure intuitive UI. Portfolio: https://www.freelancer.com/u/Microlent Please open the chat and send me a message, so we can have a more detailed discussion about the project to give you the project timeline and cost. Thank you for considering my services. I look forward to engaging in a productive conversation and understanding how I can be of assistance in bringing your project to life. Regards Rajesh Rolen

@raphaelokekeze

The single trickiest part of building an AI-driven arbitrage engine is normalising messy SKUs and titles across disparate wholesaler feeds and service-provider listings to accurately calculate actionable spreads. With over 15 years of web development experience, I approach this by combining robust backend data processing with rules-based logic and efficient scraping strategies to clean and match data quickly without violating site terms. I can build your price comparison platform to handle physical products, digital products, and services seamlessly. My core stack includes PHP, JavaScript, SQL, and MySQL, allowing me to deliver custom backend solutions, clean dashboards, and well-documented REST APIs that integrate smoothly with existing ERP or e-commerce systems. I will ensure the system delivers real-time results ranked by potential margin, complete with CSV/JSON exports and clear charts factoring in fees and shipping. Let's get started on your AI Price Arbitrage Tool -- 2 within your timeframe and budget. My bid is $121.13 with a delivery time of 1 days. Next step: share a few target marketplaces to kick off the data acquisition design.

@sabaehsan

Greetings, I appreciate the opportunity to work on your AI-driven arbitrage engine project. You’re looking to create a platform that efficiently compares prices across various categories and provides businesses with actionable insights on where to buy low and sell high. My approach would involve a combination of web scraping and API integration, ensuring compliance with each marketplace's terms. To deliver real-time results, I would implement a robust intelligence layer using machine learning to normalize data and identify opportunities. The dashboard will be clean and user-friendly, with data export options for easy integration into existing systems. Security and scalability will be priorities, deploying on a reliable cloud platform with proper access management. I’m excited about the potential to bring this project to life and help businesses capitalize on arbitrage opportunities. Best regards, Saba Ehsan

@ShaikhAneesa

Hello, I have thoroughly reviewed the project requirements for the development of an AI Price Arbitrage Tool focusing on price comparison across various marketplaces, wholesaler feeds, and service-provider listings. Let's chat and discuss it further. To handle your project, I will start with utilizing a combination of web scraping, official APIs, and partner feeds to acquire data while ensuring compliance with the Terms of Service of each platform. Following this, I will implement a machine-learning intelligence layer to normalize SKUs/titles, identify duplicates, and calculate potential spreads efficiently. My deliverables for this project include a clean dashboard displaying real-time price comparisons across different categories and a documented REST or GraphQL API for seamless integration with existing ERP or e-commerce systems. Before signing-off my bid, I would like to ask a question, i.e., how many team members would have access to the platform simultaneously? Best Regards, Aneesa.

@usmanbashir12100

Hi, I can build the arbitrage engine with a Python-based acquisition and intelligence layer that combines permitted APIs, feeds, and scraping, then normalizes product/service data, detects duplicates, and calculates spreads including fees and shipping. I’ll expose ranked opportunities through a responsive dashboard and REST API, with AWS deployment, role-based access, CSV/JSON exports, and documented integration endpoints designed for the required performance. A few questions: * Which 15+ marketplaces, feeds, and service sources should be included in the initial demo? * Do you already have API credentials or partner-feed access for any of these sources? * Should opportunity calculations account for marketplace-specific commissions, taxes, shipping, and currency conversion? Best regards, Muhammad Usman

@Prutheus

Price comparison across marketplaces, wholesaler feeds and service listings, ranked by margin. The engineering risk here is data acquisition: official APIs where they exist, scraping only where the ToS allows, and a normalisation layer that makes different feeds comparable at all. How I would build it: - Source adapters, one per marketplace or feed, each isolated so a changed page never takes the whole pipeline down - Normalisation into a common product and price schema, including currency, shipping and fees, because margin without those numbers is fiction - Matching layer to decide when two listings are the same item, which is where a model genuinely helps - Ranked dashboard plus a documented REST API for your ERP - Scheduled runs with monitoring I run Airflow data pipelines and scraping of public sources in production for industrial clients. Start with two sources to prove the margin numbers, then scale. One question: which three marketplaces matter most to you? Martin

@denisSolution

Hi, I'm Denis, and I've built systems that pull, normalize, and expose pricing data from multiple sources to power real-time dashboards and downstream integrations. The challenge here is two-fold: reliably collecting clean listings across products and services while keeping latency low, and then surfacing the highest-margin opportunities in a way that teams can act on without digging through noise. I would set up lightweight scrapers or partner with official feeds where allowed, use fuzzy matching to align SKUs across stores, and build a scoring model that weights raw price difference against fees, shipping, and stock levels. The dashboard would expose sortable tables, downloadable reports, and a REST API so your ERP can consume the same data without extra parsing. I’ve done similar work where third-party rate limits and inconsistent HTML forced creative scraping strategies, and I’ll design retries, caching, and fallback feeds early to avoid dead ends. The stack would lean on Python for data, PHP for the API layer, and a simple React front-end for the dashboard. AWS deployment would include basic auth, role separation, and auto-scaling tuned to the query load you expect. I can start working right away. Let's connect and discuss the details. Thanks, Denis.

@topcreator100

Hello, As an experienced developer with a strong background in web and mobile app development, I bring to the table skills that are directly aligned with your AI Price Arbitrage Tool project. Having built custom web applications, RESTful APIs and worked extensively with database design, I am confident in my ability to fulfill your criteria flawlessly. My experience with multiple front-end and back-end frameworks, such as React, Vue.js and Spring Boot, will ensure we choose the best combination of technologies for maximum performance on any cloud platform. Given my knowledge and implementation of machine learning, I am well-suited to creating the intelligence layer for your tool: processing massive data inputs from various sources, normalizing SKUs/titles, detecting duplicates and calculating actionable spreads accurately. Additionally, my history with data visualization utilizing tools such as D3.js solidify your requirement for clear dashboard reports - turning complex information into easily digestible visuals is one of my specialties. To emphasize any arbitrage opportunity convincingly and concisely is key here -- and I can surely deliver this for you. Finally, as an advocate for high-performance solutions, I significantly value security and scalability - essential for any venture involving sensitive data. Utilizing necessary guards like basic auth and role management integrated indigenously would add another layer of security w Thanks!

@Muhammadzeesha59

Hello Sir/MAM I am a Skilled Full Stack Developer. Having rich experience in Java , C++ , C , C# , Python , Eclipse , Sql , Mysql , .Net ,Oracle , Object Oriented Programming , Data Structure , Algorithms, Linux , Windows , Cloud , Azure , Ubuntu , OpenAI , Desktop Applications. Web Development I have a perfect grip on “Artificial Intelligence” “Automation” , and work in “Machine Learning” Deep Learning “Computer Vision ” Object Detection”. My track record as demonstrated in my 100% job completion and 5-star review rating showcases My ability to deliver exceptional results on time and with utmost quality I believe that my skill set makes me the ideal candidate for this project Please come on chat we will discuss more about this I will be waiting for your reply . Thanks and Best Regards

@Zaiyan

I can develop an AI-driven arbitrage engine that compares prices across multiple marketplaces and delivers real-time results ranked by potential margin. My first step will be to determine the best data acquisition methods, ensuring compliance with each site's terms of service. I'm based in Toronto, work quickly, and am always available for updates and questions.

@temoberishvili

Hi there, I see you're looking to create an AI-driven price arbitrage tool focused on comparison. Your goal of leveraging AI for accurate pricing insights is exciting and can really benefit users looking for the best deals. I understand that you need expertise in PHP, Python, and web scraping, along with solid software architecture and data analysis skills. It would be great to clarify how you envision the integration of data sources and what specific APIs you plan to use. A phased approach could help here: starting with a prototype that scrapes a couple of sources, then iterating to refine the AI model and expand data integration. This way, we can ensure the tool meets your expectations without overwhelming risks. Some similar things I've built include a niche SaaS dashboard for a local business, a data visualization tool for a regional service provider, and a web scraper for a pricing analysis project. Looking forward to discussing this further!

@arslanshahid386

Hi, I can help you with this project. I have relevant experience with PHP and can handle the work from development to testing and delivery. I've reviewed your requirements and can provide a clean, reliable, and responsive solution. Let's discuss the details and get started. Best, Arslan Shahid

@dtk507

Hello. I'm Davide, I'm here to help you. I understand you need a price-arbitrage engine that normalizes multi-source pricing and exposes it through a fast API. I'll build the ingestion layer with PHP for the portal, Python for web scraping and data integration, then store normalized offers in PostgreSQL with source-specific adapters, deduplication, and spread scoring. The backend will expose REST endpoints, with GraphQL if your ERP prefers it, and a dashboard for ranked opportunities across products, digital items, and services. I’ll also add CSV/JSON exports, fee and shipping calculations, role-based access, and AWS deployment with caching to keep sample queries under 500 ms. Which marketplace and feed types should the first demo cover so I can shape the ingestion adapters correctly? I can start immediately and you can get good results within 10 days. Looking forward to hearing from you. Best regards, Davide

@ImranAli01

Hi, I got that you are looking for an AI-driven arbitrage engine focusing on price comparison across physical products, digital products, and services. This is what I can help you with, let's chat. My approach is to utilize a combination of web scraping and official APIs to acquire data, implementing machine-learning algorithms for SKU normalization and duplicate detection. The tool will provide real-time results ranked by potential margin, with reports available in downloadable formats and visible charts highlighting arbitrage opportunities, including fees and shipping costs. Additionally, the platform will be secured and scalable, deployed on AWS, GCP, or Azure with basic authentication and role management for safe multi-team usage. As final deliverables, you will receive a platform that compares prices across different categories, delivers real-time results, and presents findings through a clean dashboard and documented API for seamless integration with existing systems. One thing I'd like to confirm before we start: Are there any specific marketplaces or service providers you want to prioritize for data acquisition? Looking forward to discussing this further. Regards, Imran

@Lincolnwhiz

With extensive experience in software architecture and web scraping, I'm fully prepared to design and build the advanced AI-driven arbitrage engine you're looking for. My work with GoHighLevel automation, Zoho ecosystem integrations, Kajabi email templates, Shopify storefront development and Brevo email marketing infrastructure aligns perfectly with the requirements of your project. Your project's data acquisition phase is critical, and I provide multiple highly-effective methods including web scraping and utilizing official APIs or partner feeds - all while ensuring compliance with ToS agreements. My expertise in machine learning will allow me to quickly normalize SKUs/titles, detect duplicates, and calculate actionable spreads giving you the most accurate results when it comes to spotting arbitrage opportunities. Furthermore, my experience deploying systems on AWS, GCP and Azure ensures your tool will not only be secure but also scalable—making it accessible for multiple teams within your company. I understand how important it is to have seamless integration within a safe environment for continuous use. Finally, my work is characterized by clear, patient communication – before and after builds – which enables us to move forward efficiently, and my focus on getting the architecture and automation right the first time aligns perfectly with your need of building a robust tool. With me, you'll find a reliable partner who delivers exceptional results from day one!

@AndrewJSMAN

I’ve built real-time price comparison engines for a logistics aggregator, so this is right in my wheelhouse. I’ll spin up a Python microservice stack with FastAPI for the intelligence layer, Scrapy/Playwright for compliance-first scraping, and Redis for near-real-time price caching. The ML normalization will use spaCy + custom fuzzy matching for SKUs/titles, with margin calculation pipelines that factor in Amazon S3, UPS, and Stripe fee schemas. Frontend will be a lightweight React dashboard served via CloudFront, while the REST API will run under NGINX behind AWS Cognito for role-based auth. I’ll containerize with Docker, orchestrate via ECS Fargate, and validate via Locust load tests before handing off Terraform + OpenAPI docs. I can start immediately. Thanks, Andrii

@tyronet2

You need comparable offers ranked by net margin, not misleading price gaps between different products or services. At Marin Software, I built Python/AWS real-time pipelines and LangChain agents. I’d use FastAPI, PostgreSQL, and background collectors, starting with approved APIs and feeds. Category-specific rules would match SKUs, licences, and service scope, flag uncertain matches, and account for currency, fees, and shipping. Indexed results would serve the dashboard and API without waiting for live scraping, with timestamps showing freshness. I’ll benchmark the 500 ms query target, add role-based access, charts, CSV/JSON exports, and document deployment and integration. I recommend validating matching on a small source set before expanding to all 45. Which sources are mandatory, and do you already have feed or API access?

@HamparProd

Hi, I can build the arbitrage engine, but I’d approach it as a source-adapter platform rather than hardcoding marketplace logic into one scraper. For the initial MVP, I’d use Python/FastAPI with modular API/feed/scraping connectors, normalized product/service records, matching logic and a spread engine that accounts for source price, fees and shipping where available. Exact identifiers such as SKU/UPC/EAN would be matched deterministically, with fuzzy/semantic matching used where listings differ. The dashboard would rank opportunities by estimated net margin, support filtering and CSV/JSON export, while the REST API exposes the same normalized results for ERP/e-commerce integration. I recommend validating the architecture with 3 representative sources first, then adding the remaining integrations as separate milestones because each marketplace has different API, ToS and data constraints.

@melopgabriel

Hi, this project is a strong fit for a price-focused arbitrage engine that can compare marketplaces, wholesale feeds, and service listings in one place. I understand the goal is not just data collection, but fast, reliable spread detection with clear business output. I’ve built systems that combine scraping, API integrations, data normalization, and dashboard/API delivery. For this, I’d keep the first version practical: connect the best available sources, normalize titles and SKUs, calculate spreads with fees and shipping, and rank opportunities by margin. I’d structure it so the same clean data powers both the dashboard and the REST or GraphQL API, with CSV/JSON exports for internal use. I can also set up basic auth, role-based access, and cloud deployment on AWS, GCP, or Azure. If you’d like, I can map the fastest path to a demo that meets your 15-source target. Best regards, Gabriel