AI Mining Project Intelligence Platform Developer (Airtable + AI + Web Scraping + Front-End Interface)

@MashoodurRehman1

I am a seasoned engineer specializing in developing AI-driven intelligence platforms, with extensive experience in web scraping, Airtable integration, and front-end development. My expertise aligns with your requirements for a mining project intelligence platform. I have a solid foundation in Python, OpenAI API integrations, and have worked on similar systems that automate data extraction, categorization, and provide advanced search functionalities. I have successfully implemented data collection systems for industry-specific sources, leveraging Python and relevant scraping frameworks. My experience extends to using vector databases and AI technologies for structured data extraction, which will be instrumental in developing your platform's AI layer for summarizing and classifying mining projects. My work on previous scalable systems includes front-end dashboards using React, ensuring seamless user experiences in complex data environments. I'm interested in discussing how my skills can contribute to your project's success and would appreciate more details on your preferred tech stack and thoughts on a phased implementation.

@ZohaibRoy

⭐⭐⭐⭐⭐ Build an AI-Powered Mining Project Intelligence Platform ❇️ Hi My Friend, I hope you are doing well. I've reviewed your project requirements and see you are looking for an experienced engineer to enhance your mining project intelligence system. Look no further; Zohaib is here to help you! My team has successfully completed 50+ similar projects for building automated platforms. I will utilize efficient web scraping, AI data extraction, and Airtable automation to create a robust and user-friendly interface within your budget. ➡️ Why Me? I can easily build your mining project intelligence system as I have 5 years of experience in web scraping, AI integration, and database management. My expertise includes Python programming, data parsing, and automation. Additionally, I have a strong grip on Airtable API, OpenAI API, and front-end frameworks, ensuring a comprehensive approach to your project. ➡️ Let's have a quick chat to discuss your project in detail and let me show you examples of my previous work. Looking forward to discussing this with you! ➡️ Skills & Experience: ✅ Web Scraping ✅ Data Collection ✅ AI Integration ✅ Python Programming ✅ Airtable API ✅ Database Management ✅ User Interface Design ✅ Automation Systems ✅ Project Monitoring ✅ Data Analysis ✅ API Development ✅ Front-End Frameworks Waiting for your response! Best Regards, Zohaib

@irsolutions

I understand your need to create a cutting-edge mining project intelligence system that leverages AI, web scraping, and a user-friendly front-end interface to automate data collection, extraction, and updating processes. With my background in handling high-complexity systems and AI/ML development, I am well-equipped to tackle the challenges of evolving your current Airtable database into a fully automated, self-expandable platform. My strategic insight for ensuring scalability and security in this project involves utilizing AI extraction layers to automate the extraction of project data from various industry sources, summarize technical information, and classify projects by commodity/stage/location. I have successfully built and scaled Telegram Mini Apps serving over 1 million users, proving my ability to handle projects of this complexity. I am excited about the opportunity to discuss how we can collaborate to bring your vision to life. Please feel free to reach out to me to further explore the roadmap for this project.

@corpmember29

Hello, I will build your AI-powered mining project intelligence platform, automating data scraping from ASX/TSXV/SEDAR+, extracting project insights via OpenAI/Claude, and updating Airtable automatically. I’ll deliver a clean React dashboard with advanced search/filtering and AI prompt capabilities. Stack: Python + Airtable API + OpenAI + React. I propose a staged build for quick MVP delivery and future scalability. Relevant experience includes AI-driven intelligence platforms for natural resources. Timeline: 8–10 weeks. Thanks CHRISTINA

@devendrathakur12

i’ve done very similar recently building AI-driven intelligence pipelines with Python scrapers, Airtable sync, OpenAI extraction, vector search, and React dashboards handling noisy financial/news data at scale. How do you want entity resolution handled when the same project appears across filings, PDFs, and renamed company assets? Do you already have a preferred vector stack like pgvector/Pinecone, or should I design that layer from scratch? I’d recommend separating ingestion, AI extraction, and enrichment into independent queues because mining filings can spike heavily during news cycles. I’d also add source-confidence scoring and extraction versioning so analysts can trace exactly why a project was tagged or flagged. I’ll first build the scraping and normalization pipeline for filings, PDFs, and news sources. Then I’ll integrate AI extraction, Airtable automation, and semantic search with a scalable React interface, followed by validation workflows and staged rollout. Best, Dev S.

@topcreator100

Hi there, I’ve read your AI Mining Project Intelligence brief and I’m confident I can build a scalable platform that turns your Airtable base into an automated, AI-powered intelligence engine. I’m genuinely interested in this project and I have several experiences with similar AI-driven data platforms for mining and resources workflows. My approach is to stitch web scraping (ASX/TSXV/CSE, SEDAR, and news pages) with an AI extraction layer (OpenAI/Claude) to auto-fill Airtable records, plus a front-end dashboard for advanced prompts and saved searches. I propose a staged build with modular data ingestion, AI-driven classification, and a responsive UI, followed by scalable expansion like valuation scoring and deal tracking Best regards,

@seefattechnologi

SURE------I will do it in effective manner as AI /PYTHON Web scrapper Expert -------------- I am highly appreciative to work on this specific task I can do my best. I am an Innovative PYthon/Full stack developer having rich experience with so many successful Tasks. I have some queries to give you accurate time Let’s connect on chat for further discussion and start quickly. Thanks!!

@installerpoint

Hi, Good Day What makes your project interesting is that this is not simply a scraping dashboard or an AI wrapper around Airtable — you're building a structured mining intelligence system where data quality, retrieval logic, and commercial usefulness matter just as much as automation itself. The biggest challenge in platforms like this is usually not collecting announcements, but reliably extracting meaningful project-level intelligence from inconsistent filings, PDFs, and company updates while avoiding duplicate or low-signal data. That’s where a well-structured AI extraction and retrieval layer becomes critical. I’d approach this as a staged build: • Scraping/monitoring pipeline for ASX, TSXV/CSE, SEDAR+, and mining news sources • AI extraction/classification layer using OpenAI/Claude • Airtable automation and structured enrichment workflows • Searchable frontend with prompt-based retrieval and advanced filtering • Scalable architecture for future valuation/deal intelligence modules A vector-search/RAG layer combined with structured Airtable records would likely give the best balance between semantic search and reliable filtering. One thing I’d like to understand first — do you envision Airtable remaining the long-term core database, or eventually transitioning into a dedicated backend as the dataset scales beyond exploration-stage projects? Cheers, M Adeel,

@muneeb6767

I can help evolve your Airtable mining database into a scalable AI-powered intelligence platform with automated scraping, AI extraction pipelines, Airtable synchronization, and a searchable React-based interface optimized for mining/project intelligence workflows. I have experience with Python scraping systems, OpenAI integrations, structured databases, automation workflows, and scalable dashboard architectures, and I’d recommend a staged build process for reliable expansion and long-term maintainability.

@johnallenvillan1

Hello, I’ve gone through your description of the mining project intelligence platform and the need to move from a manually curated Airtable system into a fully automated, AI‑driven intelligence workflow. The level of structure you’ve already established tells me you’re ready for systemisation rather than experimentation. I’ve previously built automated news‑monitoring and extraction pipelines for ASX and TSXV‑listed companies, delivering clean project‑level updates directly into structured databases. I also designed a RAG‑based search layer for a resources analytics firm that enabled targeted, prompt‑based queries across thousands of filings. The biggest challenge here is maintaining data integrity as the system grows - ensuring scraped announcements map correctly to projects without duplication, and designing the extraction layer to handle inconsistent reporting formats across global jurisdictions. I’ll build modular scrapers in Python, add an AI extraction layer using OpenAI/Claude, integrate structured outputs into Airtable via API, and implement a React front‑end supported by a vector database for semantic search. Each component will be isolated, logged, and versioned for traceability. To proceed cleanly, I’d first clarify your preferred hosting environment and whether SEDAR+ access will be automated or manual. Thanks, John allen.

@SmartCodeDev

Hello, Your platform already has the right foundation, and I can help evolve it into a scalable AI driven mining intelligence system with automated data collection, extraction, enrichment, and advanced search workflows. I would approach this as a staged build, starting with robust scraping and ingestion pipelines for ASX, TSXV, CSE, SEDAR+, and company sources, then layering AI extraction, Airtable automation, vector search, and a clean React based dashboard for prompt driven project discovery. I have experience building AI powered scraping and intelligence systems using Python, OpenAI APIs, Airtable integrations, RAG workflows, and structured data pipelines designed for scalability and ongoing expansion. Recommended stack would likely include Python, Playwright or Scrapy, OpenAI, Airtable API, PostgreSQL or vector database integration, and React for the front end. For a solid production ready Phase 1 build, I would estimate roughly 6 to 10 weeks depending on depth of automation and interface requirements, with staged delivery milestones throughout. I’m looking for long term engineering partnerships and would be happy to discuss architecture, scaling strategy, and commercial workflow considerations in more detail.

@huzaifa306

Hello, Your project aligns very well with my experience in AI automation, structured data systems, scraping workflows, and scalable intelligence platforms. I can help build a robust mining project intelligence system that automates data collection, AI extraction, Airtable synchronization, and advanced search capabilities with scalability in mind for future expansion. I have experience working with Python, OpenAI APIs, Airtable integrations, scraping frameworks, automation workflows, and AI-powered search/RAG systems. I would recommend a staged build process starting with data ingestion and AI extraction, followed by automation pipelines and the front-end intelligence dashboard. The proposed stack would likely include Python, Airtable API, OpenAI/Claude, vector database integration, and a React-based front end for scalable search and analytics. My rate is 50 AUD/hour. I would be happy to discuss architecture, timelines, and the best phased approach for long-term scalability. Looking forward to hearing from you.

@alizain721

You already have a tidy Airtable with ~200 hand tagged projects — that structure is exactly what lets us reliably link scraped announcements to the right project instead of creating noise. The real challenge is entity linking and deduplication across PDFs, press releases and filings, then turning messy numeric mentions into clean, queryable fields. I built CrowdAxis, a data ingestion and scoring engine that pulls from 10 external sources, normalises data, and serves models via FastAPI — similar ETL, dedupe and scoring needs. My approach 1. Build source monitors and scrapers for your list plus company pages, ASX, TSXV/CSE, SEDAR; ingest PDFs and HTML with OCR where needed. 2. Run an AI extraction pipeline that does entity resolution, numeric parsing for resource estimates, classification and embedding into a vector store. 3. Sync clean records into Airtable with dedupe logic and timestamped source links. 4. Deliver a React dashboard with prompt based search tying OpenAI retrieval to vector search; expose saved views and filters. Client questions and details Examples Similar systems: CrowdAxis scoring engine and a PDF payroll parser system. Tech: Python, Scrapy, FastAPI, OpenAI/Anthropic, Pinecone or Milvus, Airtable API, React. Timeline Staged build: Phase 1 scraping and AI extraction plus Airtable sync 6 to 8 weeks, Phase 2 frontend and advanced search 4 to 6 weeks. Cost Hourly rate 37.5 AUD, estimated total 18k to 30k AUD depending on scope. I recommend a staged delivery. Quick question Can you share Airtable base access and a short list of priority news sources so I can draft an architecture diagram and phased scope?

@abdulhamidh2

Hello, I have extensive experience building AI-powered intelligence platforms with automated data scraping, extraction, and scalable database integration. For your mining project, I propose using Python with Scrapy for robust web scraping from sources like ASX and SEDAR+, combined with OpenAI API for precise AI extraction of project details. Airtable automation will be handled via its API to ensure seamless updates and synchronization. On the frontend, a React-based dashboard with advanced filtering and prompt-driven search will provide an intuitive user experience. I’ve architected similar systems involving vector databases for semantic search and RAG frameworks to enhance retrieval accuracy. My approach emphasizes modularity for easy future expansion—valuation scoring or deal tracking can be integrated smoothly later. I work full-time and can start immediately, proposing a staged build: initial scraping and AI layers first, followed by Airtable automation and frontend UI. Let’s discuss your timeline and budget to tailor the solution precisely. Looking forward to partnering on this exciting project. Best regards, Abdulhamid

@albertc1996

Hello, Your project is interesting because it combines data collection, structured databases, and AI-driven intelligence, which is exactly the type of systems I enjoy building. Instead of attempting the entire platform at once, I would recommend a staged build approach, starting with the data collection and monitoring layer. A reliable data pipeline is the foundation for everything else (AI extraction, Airtable updates, and the search interface). I can help you build Phase 1: Web Scraping & Data Collection System. Scope I would implement: • Monitoring of sources such as JuniorMiningNetwork, ASX announcements, TSXV/CSE releases, and company news pages • Detection of new announcements and updates • Automated ingestion of articles and PDFs • Extraction of key metadata (company name, project name, date, source link, etc.) • Deduplication logic to prevent duplicate records • Clean structured output ready for downstream AI processing • Logging and basic monitoring to ensure reliability Technical approach: I would build the pipeline in Python, using robust scraping and monitoring logic. The system can be structured so that it feeds clean data into Airtable or a processing layer for AI extraction later, making it easy to expand into the next phases of the platform. I focus on building scrapers that are stable, maintainable, and easy to extend, which will be important as you add more sources in the future. Estimated timeline: Approximately 2–3 weeks for a stable first version of the scraping and monitoring pipeline (including testing and adjustments). Estimated cost: 900–1200 depending on the number of sources included in the first phase. I would also recommend building the system in clear phases, for example: Phase 1 – Data collection & monitoring Phase 2 – AI information extraction and summarisation Phase 3 – Airtable automation and data syncing Phase 4 – Search interface and intelligence dashboard If helpful, I’d be happy to discuss the sources you want to prioritise first and design the scraping architecture so it scales as your database grows. Best regards

@mykhaylob4

✋ Hi there. I can help turn your Airtable mining database into an AI powered intelligence platform with automated scraping, AI extraction, searchable project data, and scalable workflows for ongoing mining research. ✔️ I have solid experience building AI driven data platforms using Airtable APIs, Python scraping systems, OpenAI integrations, automation workflows, and searchable dashboards. In previous projects, I built systems that collected public data, parsed PDFs and announcements, classified records with AI, and synced structured outputs into searchable databases. ✔️ For your project, I would build automated scraping pipelines for ASX, TSXV, CSE, and mining news sources, then connect AI extraction layers to identify projects, resource estimates, jurisdictions, and strategic review signals. I can also build a clean frontend dashboard with prompt based search, filters, saved views, and company profiles. ✔️ My preferred stack would include Python, Airtable API, OpenAI or Claude APIs, vector search, PostgreSQL, and React depending on your final workflow requirements. I would recommend a staged build process so the platform can scale cleanly over time. Let’s chat to discuss the Airtable structure, roadmap, and long term expansion plans. Best regards, Mykhaylo

@emilesd617

Hi, I am an AI and data platform developer with 8 years of rich experience with a background in Python, web scraping, Airtable API, OpenAI API, vector databases, React, and automation workflows. For this project, the most important part is building a scalable intelligence pipeline that can continuously ingest mining data, extract structured insights reliably, and make the information searchable through AI-driven workflows. I can help architect the scraping and AI extraction layer, automate Airtable updates, build a searchable frontend dashboard, and structure the platform for future RAG, scoring, and acquisition analysis expansion with a staged development approach. 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.

@billybryan98

Hello, I’ve read your goal to evolve a 200-project Airtable base into a scalable, AI-powered mining project intelligence platform. I’m a solo engineer with deep experience in data platforms, AI extraction, web scraping, and Airtable automation, built for fast, reliable automation and clean front-end interfaces. I’ve built AI-assisted data pipelines that ingest announcements and PDFs, extract structured project data, and keep Airtable synced in real time. I’ve also designed search interfaces with prompt-based querying, vector retrieval, and reporting dashboards, all while ensuring deduplication and audit trails. I’ll map your existing fields to an automated pipeline, train extraction models to identify resource estimates, and create a front-end that supports complex filter/prompt queries like those you listed, with saved views and profiles. I can deliver a scalable, end-to-end solution, data ingestion, AI extraction, Airtable automation, and a responsive UI, built for staged expansion (valuation scoring, deal pipelines, and market data) while maintaining data integrity and performance. Please feel free to contact me so we can discuss more details. I am looking forward to the chance of working together. Best regards, Billy Bryan

@AlexVydrin

Hi, I’d love to partner with you on the next phase of your mining project intelligence platform and turn your Airtable prototype into a scalable, AI‑driven system. My approach is to treat this as a specialised search and intelligence product: robust scraping pipelines for ASX, TSXV/CSE, SEDAR+ and company sources; an AI extraction layer that pulls out resources, stage, jurisdiction and strategic review signals; and automations that keep Airtable continuously updated without duplicates. On top, I’d design a front‑end dashboard with structured filters and prompt‑based search backed by a vector/RAG layer, so queries like “gold projects in Newfoundland between 50koz and 500koz” feel instant and reliable. I’ve built data and AI retrieval platforms with Python, scraping frameworks, OpenAI/Claude, Airtable API and React, and would propose a staged build so you can see value quickly while we grow this into a long‑term intelligence asset rather than a one‑off tool.