Webinar: Why Generic AI Gets Mining Data Wrong — And What It Takes to Fix It | Pulse Intelligence

Why does generic AI (ChatGPT, Claude) get mining finance numbers wrong — and what does trustworthy AI actually take? Pulse Intelligence webinar. In Pulse Intelligence's first webinar, Will Coetzer and Scott North unpack why generic AI can't be trusted for mining finance data — wrong sources, wrong currencies, dead citations, and runaway compute cost — and what a validated, closed-loop system actually takes. Includes a live demo tracking ~675 daily market announcements into a structured M&A report, plus an 8-question checklist for judging any AI-generated number before you put it in a boardroom. Why does generic AI (ChatGPT, Claude) get mining finance numbers wrong — and what does trustworthy AI actually take? The core message: a general-purpose large language model is an incredible tool, but it is not a source of truth. Ask it to build a comps table or find the top copper producers and it will pull from whatever it finds — promotional articles instead of exchange filings, the wrong currency, a citation that 404s by the time you open it in the boardroom. The fix isn't a better prompt. It's a closed-loop system where the data has already been extracted, OCR'd, cross-validated and standardised before you ever ask the question. WHAT YOU'LL LEARN What an MCP (Model Context Protocol) is — the two-way connector that plugs a validated dataset directly into Claude, installed in ~30 seconds. Why querying a 100–500 page document every time can cost 100–300x more compute than a closed-loop system that already holds the extracted data. How multi-source, multi-factor authentication cross-validates a number against filings, exchanges and company sources to reach one defensible "true north" figure — with an audit trail of why. Why OCR of tables AND images (not just text) is half the battle on any PFS/DFS technical report. The real hard part: categorising, standardising and normalising across 25 exchanges, multiple reporting standards, currencies and coordinate systems — where a single mismatch becomes a fatal error downstream. The three levels of AI extraction: (1) binary, (2) mathematical, (3) judgment — and why we measure, not judge. Guardrails, reconciliation and "context sealing": why a dedicated agent that does one job every day beats a generic model doing it for the first time, every time. A live demo: a daily prompt that scans ~675 market announcements over 25 hours and returns a structured, source-linked M&A report in minutes. THE 8-QUESTION CHECKLIST — what to ask before you trust any AI number: Pulse's founding survey of 120 mining, finance, private-equity and sovereign-wealth executives ("Chaos to Clarity") found professionals lose up to 17 weeks a year just finding information — and up to 50% of an analyst's time. Generic AI makes the horse run faster. The goal here is a different engine entirely. ABOUT PULSE INTELLIGENCE Pulse Intelligence is AI infrastructure for mining finance — validated, source-traceable data that powers trustworthy origination, comps and diligence. Less searching. More strategising. See it run on your own data — book a demo at https://pulseintelligence.com #MiningFinance #ArtificialIntelligence #MiningInvestment #AI #ChatGPT #Claude #MCP #DueDiligence #MergersAndAcquisitions #Mining #DataIntegrity #FinTech