Andréa Jones has published 400 podcast episodes while raising two kids under five. Her system is not hustle - it is infrastructure built from necessity.

The podcast is her content hub. Every idea starts as a voice note in Google Notes, gets structured in a ChatGPT project trained on her proprietary Mindful Marketing framework, audience personas, and email voice. She records in Riverside, then runs the audio through Cast Magic - a repurposing tool that generates 20 pieces of content per episode: show notes, social quotes, one-liners, newsletter drafts, and thread-style posts trained on her voice.

She layers these outputs into two Airtable calendars - an editorial calendar for signature content and a social media calendar she populates months in advance during high-energy windows. Content from episode 399 might not hit social until two months later.

Andréa also runs Uncommon Marketing Agency, where she builds AI-powered interactive web experiences for brands - including an 8-bit style game for Niagara Falls tourism that replaces the generic results you get from ChatGPT or Claude with a curated, closed-loop brand experience.

Her strongest take: marketers need to get "elegant" with prompting. The inputs need to be as detailed as the outputs you expect - audience personas, objection handling, awareness levels. Most people skip this and get generic results.

1. One podcast episode produces 20+ content pieces through Cast Magic - Andréa uploads each episode's audio and gets titles, timestamps, social quotes, one-liners, newsletter drafts, and thread-style posts - all trained on her voice. She edits but never starts from blank.

2. Content batching on energy cycles beats daily consistency - She populates her Airtable social calendar months in advance during high-energy windows, then coasts during low-energy periods. Her calendar currently runs through June with repurposed podcast content.

3. ChatGPT projects trained on your framework cut outline time from 2 hours to 20 minutes - She uploaded her Mindful Marketing framework transcripts, audience personas, offer positioning, and email voice into a single ChatGPT project she uses for every episode.

4. Your AI inputs need to be as long as your expected outputs - Her client's sales page read like generic ChatGPT because the prompt didn't include audience personas, objection handling, or awareness levels. Context engineering is the differentiator.

5. Closed-loop AI experiences beat open web for brand marketing - Uncommon Marketing Agency builds interactive web games that surface only the brand's curated content, avoiding the noise and dated information that LLMs pull from the open internet.

• OnlineDrea - Andréa's personal brand: courses, podcast, and the Do Less Market Better Kit (free course)
• Uncommon Marketing Agency - Gamified and interactive AI-powered marketing experiences
• Mindful Marketing Podcast - 400 episodes on anti-burnout marketing strategies
• Cast Magic - AI podcast repurposing tool (generates 20+ content pieces per episode)
• Riverside - Podcast recording and transcript-based editing

Key TakeawaysLearn More

Six to nine targeted bottom-of-funnel pages will outperform 50 top-of-funnel blog posts for pipeline - and Lashay Lewis has the client data to prove it.

Lashay breaks down the exact four-element framework she uses to build bottom-of-funnel content: pain points, features, benefits, and capabilities - stacked like Legos in a specific order. Pain leads because high-intent buyers need to connect immediately. Features follow because they solve the pain directly. Capabilities prove the features actually work.

She walks through live examples from clients like Teal and Conveyor, showing how she reverse-engineers sales call transcripts to extract the exact language buyers use - then maps that language to contextual AI search queries. The distinction matters: Google search is keyword-based ("best resume builder"), but AI search is context-based and persona-driven ("I'm a job seeker struggling to show impact across multiple resumes").

Lashay explains why she's skeptical of prompt volume tracking tools - if queries are essentially one-of-one, traditional volume metrics break down. Instead, she expands surface area by mapping synonyms and predicting before/after queries around a core topic.

She also shares her three-year founder journey from consultancy to failed product pivot and back again - including how muddied positioning nearly killed her business before BOFU.ai found its footing.

1. Pain points must lead every bottom-of-funnel page - High-intent buyers need to feel understood in the first few seconds. Leading with product history or "what is" definitions is a top-of-funnel mistake that kills conversion on BOFU pages. Lashay sees 8-minute read times on articles that lead with pain.

2. AI search is context-based, not keyword-based - Someone typing into ChatGPT writes a paragraph about their situation, not three keywords. Your content needs to match that contextual query by including persona, category, pain points, and capabilities - not just keyword-stuffed headers.

3. Sales call transcripts are the most underused content asset in SaaS - Your buyers' language is not your internal language. The gap between how your company describes itself and how the market talks about the problem is where positioning breaks. Sales calls close that gap.

4. Your competitors' pages about you shape your AI search presence - Lashay shows how Perplexity pulled a competitor's two-out-of-five rating of Teal into a citation. What you have public-facing matters because AI pulls from competitor alternative pages to describe you.

5. Prompt volume tracking is likely broken - If AI search queries are essentially one-of-one natural language strings, traditional search volume metrics don't apply. Expand surface area by topic, not by keyword. Map synonyms and before/after queries around a core topic instead.

• BOFU.ai - B2B SaaS content marketing consultancy focused on bottom-of-funnel content and attributable pipeline
• Built from the Bottom (Substack) - Lashay's deep dives on content strategy, AI search, and building in public
• Teal - Resume builder used as a live case study for the BOFU framework
• Conveyor - Security questionnaire automation platform, client example for AI search results
• Perplexity - AI search engine referenced for competitor citation behavior
• Fletch (Anthony Perry), Rob Kaminsky - Product marketers whose homepage positioning frameworks inspired Lashay's content approach

SuperMarketers: Build your AI search visibility system at https://supermarketers.ai

Connect with Gen: Follow for weekly breakdowns on AI visibility and content systems -> https://www.linkedin.com/in/genfurukawa

Connect with Lashay Lewis: Follow her for deep dives on bottom-of-funnel content and AI search strategy -> https://www.linkedin.com/in/lashaylewis

Key TakeawaysLearn MoreCTAs

Steve Toth has spent 15 years in SEO and now runs one of the sharpest AEO/GEO consultancies in B2B. His core argument: stop tracking where your brand ranks in LLM responses. Start measuring whether LLMs represent you accurately.

His Trust Alignment Framework scores how well ChatGPT, Perplexity, and Gemini answer questions about your product across six pillars - vertical, company size, comparisons, pricing, integrations, and features. The gap between your "sales-grade answer" and the LLM's answer is your visibility problem.

Steve walks through live demos showing how ChatGPT Deep Research and Perplexity surface follow-up refinements - and how collecting those refinements across 5-8 runs reveals which deal-breaker topics matter most in your category. He also shares a Claude project that clusters Google Search Console keywords by intent, giving B2B teams a proxy for LLM search demand when no reliable prompt volume data exists.

The conversation covers how each model cites differently - ChatGPT prefers general pages, Google AI Mode pulls specific passages from case studies and UGC - and why passage-level optimization matters more than page-level. Steve closes with his Spellbook case study: 90% non-branded organic traffic growth by targeting emerging keywords in the legal AI space and capitalizing on competitor sentiment gaps.

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Key Takeaways

1. LLM leads convert 4-5x higher than Google traffic - ChatGPT referral visitors spend 4-5x more time on site and convert at 4-5x the rate. These buyers arrive pre-educated with specific deal-breakers already defined. Your sales team closes them faster.

2. Stop tracking brand mentions in LLMs - measure representation accuracy instead - The Trust Alignment Framework compares your ideal sales answer against what the LLM actually says across six pillars (vertical, company size, comparisons, pricing, integrations, features). The delta is your real visibility gap.

3. LLM prompt volume tools are unreliable - use intent clustering as a proxy - Every word added to a prompt makes it less likely to be searched twice. Steve built a Claude project that clusters Google keyword data by intent and aggregates volume across the entire cluster, giving directional demand signals for AEO prioritization.

4. Each AI model cites sources differently - ChatGPT favors first-party "ultimate guide" pages. Google AI Mode pulls specific passages from case studies and UGC. Claude uses the Brave search index. Optimizing for one model does not guarantee visibility in others.

5. Passage-level optimization beats page-level for AI Mode - Google AI Mode uses a passage ranking index, not a page ranking index. It looks for 100-300 token excerpts that support its reasoning chain. You can pepper relevant content across case studies, homepages, and comparison pages rather than building one monolithic page per topic.

Learn More

- SEO Notebook - https://seonotebook.com - Steve's weekly SEO newsletter, running since 2019

- AI Notebook - https://ainotebook.com - Weekly newsletter focused on AEO/GEO strategies

Connect with Gen:

- www.supermarketers.ai

- www.linkedin.com/in/genfurukawa