624: Using AI to the MAX with Ed Woodcock

The podcast discusses two AI-powered tools being developed by the guest, Ed Woodcock. The first, Lowdown, is an intelligence briefing system that aggregates and summarizes information from first-party sources such as government websites, APIs, and press releases to provide objective, real-time updates on global topics like AI's impact on jobs and data centers. It uses AI to generate concise, glanceable briefings while maintaining accuracy through rigorous source verification and editorial oversight, functioning like an AI-curated newsroom with an editor-in-chief model to refine content and avoid repetition.

The second tool, Plot Beacon, applies similar AI capabilities to local planning applications in the UK, helping residents understand and respond to proposed developments near them. It aggregates data from fragmented council systems, scores applications by impact, and translates complex legal jargon into accessible insights using a traffic light system. The discussion also covers the use of AI in genealogy research, where large language models help analyze historical records, build family trees, and identify inconsistencies across documents. Throughout, the focus is on using AI to democratize access to public information, empower individuals, and streamline complex, data-heavy tasks across domains such as journalism, urban planning, and personal history.

• What if you built a private AI briefing service for niche industries using primary data sources?

  • Move: Identify 3 underserved professional niches (e.g., urban planners, environmental consultants, insurance brokers) and create a lightweight, automated briefing pipeline using public APIs, government datasets, and AI summarization (Claude or similar). Launch a waitlist with a sample briefing.
  • Why Now?: AI models now handle structured data parsing and summarization reliably, and professionals are increasingly time-poor but need accurate, jargon-free insights - especially in fast-moving domains like climate policy or regulatory changes.
  • Expected Upside: Validate demand for paid private briefings; capture early adopters willing to pay $20 - $50/month for time savings and edge in decision-making, laying groundwork for a scalable B2B intelligence product.

• What if you launched a solo-run AI tool that flags high-impact local planning applications near user-postcoded areas?

  • Move: Use web scraping (where APIs don't exist) to aggregate UK planning applications, apply AI scoring (1 - 10) based on impact factors (size, proximity, land use), and send weekly email alerts with plain-language summaries and traffic-light ratings. Start with one region and expand.
  • Why Now?: Council data is increasingly online (even if fragmented), and AI can now parse legal text and geospatial context effectively. Public frustration with opaque planning processes is high (e.g., Wigan warehouse case), creating urgency.
  • Expected Upside: Build a sticky user base of homeowners and community advocates; monetize via premium alerts or developer lead-gen partnerships, while establishing a defensible data pipeline for civic tech.

• What if you created an AI-powered contract dispute assistant for consumers and freelancers?

  • Move: Build a simple web app where users upload a contract or T&Cs, and AI identifies unfair clauses (e.g., hidden fees, auto-renewals, liability caps), then generates a dispute letter with citations. Start with rental cars, subscriptions, and freelance agreements.
  • Why Now?: LLMs can now reliably extract obligations and risks from dense legal text, and solo professionals/freelancers face recurring battles with unfair terms but lack legal time or budget.
  • Expected Upside: Rapid user acquisition through Reddit, Indie Hackers, and Twitter; monetize via one-off credits or subscriptions, while gathering data to expand into landlord-tenant or SaaS contract audits.

• Build an AI-powered briefing tool using primary data sources (e.g., government APIs, press releases) to reduce media bias and increase credibility.
• Focus on a specific domain (e.g., planning applications, genealogy) and use AI to simplify complex, jargon-heavy information for non-expert users.
• Use familiar tech stacks like Rails when leveraging AI for development, as expertise in the framework improves AI code quality and reduces token waste.
• Implement strict quality control in AI-generated content by adding deterministic checks for coherence, sourcing (3 - 4 sources per claim), and human-style editing.
• Migrate infrastructure to sovereign providers (e.g., European hosts like Hetzner, OVH) to reduce reliance on US-based cloud services and demonstrate operational independence.