Unscripted SaaS

When someone asks ChatGPT, Claude, Perplexity, or Gemini "what's the best X?", a few brands get named — and most companies have never checked whether they're one of them. Somya Goyal built Stellarcast to change that: a platform that watches how your brand shows up in AI answers, diagnoses why you're missing, ships the fix, and then proves the needle moved. After 15+ years in quality engineering — eight of them at Accenture, then leading QA and building validation frameworks for AI systems — Somya is a first-time founder building Stellarcast in the open from India. In this conversation with host Jeremy Rivera, she breaks down why AI citations are a different game from search rankings, why "AI is confidently wrong" about so many brands, and how a continuous monitor-diagnose-execute-prove loop beats the one-time audit. In this episode The monitor → diagnose → execute → prove flywheel — and why most tools "stop at the dashboard" Why citations aren't rankings: AI answers overlap Google's top ten by only ~14% AEO in plain English — make your brand easy for AI to find, understand, and read Why "AI is confidently wrong", citing two-year-old pricing and reviews even after fresh data lands Treating LLM answers as a "liquid surface" — freshness, lag, and query-deserves-freshness in AI CMS integrations (WordPress, Shopify, Webflow) that turn diagnosed gaps into approved, shipped fixes Somya's leap from 15+ years in QA to first-time founder — and the exposure of shipping under your own name Market fit for SMBs and agencies, with the human always in the loop Chapters (00:00) Meet Somya Goyal & what Stellarcast does — a "system of AI records" (02:30) The four-agent loop: monitor, diagnose, execute, prove (05:45) CMS integrations & building in the open with pilot design partners (08:10) How the models behave differently — and why citations aren't rankings (~14% overlap) (12:20) Freshness, lag, and the "liquid surface" of LLM answers (16:05) AI as your least-trained support rep — the rogue knowledge base (19:15) Somya's background: fifteen years in QA to first-time founder (22:40) Market fit: SMBs, agencies, and the human-in-the-loop (25:30) Early access & pricing Notable quotes "Visibility in AI is simple. Make your brand easy for AI to find, understand, and read. That is the new name of AEO, in very simple English."— Somya Goyal"Ranking is different and citation is different. AI citations overlap Google's top-ten links by only fourteen percent."— Somya Goyal"Most brands have never read what AI says about them, and sometimes AI is confidently wrong. It cites data that's maybe two years old — the pricing, the reviews — even after a lot of fresh data."— Somya Goyal Resources & links Stellarcast — AI brand-visibility platform (monitor, diagnose, execute, prove) Somya Goyal on LinkedIn — where she's building in the open When AI is "confidently wrong" the fix is filling the gaps with human-certified, expert-sourced content Monitoring your brand's AI footprint and entities is exactly where SEO advisory...

Otter and Fireflies do not fail at African languages. They discard them. Unknown languages get filtered out as noise or approximated to the nearest English word. Tobiloba Sulaimon, a self-taught developer from Lagos, built AuTrans to transcribe Pidgin, Yoruba, Hausa and Igbo as they were actually spoken. It started as a fix for a meeting the tools could not handle. Key takeaways The differentiator is what incumbents treat as an edge case. Not accuracy, but a product decision to drop what it does not recognise. Africa first is a constraint, not a slogan. Limited funding cannot cover many languages at once, so splitting attention means doing none of them well. Self-taught to shipping in roughly two years. YouTube, a first paid client, then a role where being the only developer forced the pace. He refuses features users ask for. Explicitly so the product never becomes a jack of all trades. First version shipped in about a week. The scope was small because the problem was his own. On this page The gap the incumbents left open Africa first, and why it is a constraint Self-taught from Lagos Refusing the features people ask for Chapters and timestamps People, ideas and sources mentioned Questions this episode answers Go deeper The gap the incumbents left open Tangible things were said and their machine did not capture that. And I thought that since I am a software developer, maybe I could build something that would solve this problem.— Tobiloba Sulaimon Mainstream tools filter unrecognised languages out, or map them onto the nearest English word. The output looks like a transcription failure. It is closer to a design choice, and it is the kind of choice a large company can make for years without anyone internally noticing. That is the general lesson worth taking from this episode even if you never touch transcription. Find the thing your category's incumbents have classified as an acceptable failure. Africa first, and why it is a constraint We are African first. We are focusing on ourselves first, so I’m trying to solve this problem within Africa first before we move on.— Tobiloba Sulaimon This is not positioning. Limited funding cannot cover the continent's language count, so attempting breadth would mean doing all of it badly. The narrowness is what makes the quality possible, and the quality is the entire product. Self-taught from Lagos Everything I know today I learnt on my own. I am a self-taught software developer.— Tobiloba Sulaimon HTML, CSS and JavaScript from YouTube after high school. A first paid client build. Then a front-end role at a game studio where being the only developer meant learning fast or shipping nothing. The first version of AuTrans took about a week.<...

Tom Girgash quit his job two or three weeks before this conversation to build Taproot, a memory layer that keeps your knowledge in local files and pipes it into Claude, ChatGPT, Codex and Claude Code over MCP. Jeremy Rivera talks with him about grounding AI in your own work, finding words for a brand-new category, and building distribution without an ad budget. Follow Tom on X at @TGIRGS. Tom took the long way into software: an Ohio State degree, a sales role selling bearings and hoses in the field, an information-systems master's at the University of Cincinnati, and a short stint as an ERP consultant at an accounting firm. Taproot started as an MVP he built for one man named Tim, who wanted one place to hold and query all his knowledge. Read the full written recap: Tom Girgash on building Taproot, the memory layer for solo AI operators. What we cover Why "past chats" are not memory, and why you can't tell whether what surfaced is true One knowledge base, many tools: why MCP makes cross-tooling the point The mom test, and landing on "memory layer" after months of messaging that didn't land About 20 signups in the first couple of weeks, Mac-only for now The "firm brain" idea for teams, parked while he serves solo operators first Pricing to a pain point: the user about to jump from the $20 tier to $100-200 AppSumo-style launches as a paid feedback audience Why distribution, not code, is the hard part Distribution is the last moat Tom's open problem is the one most bootstrapped founders share: "I'm not in a position right now. I want to spend money on ads," so he needs an organic distribution engine. Reddit and forums help with validation but punish anything that smells like selling. He also names a trap of the moment: with AI you can do so much that it's easy to spend weeks tweaking a website instead of talking to users. We dig into that tension in Cracking distribution is the hard part, and Nick Eubanks makes the same case in distribution as the only moat. Jeremy brings lessons from running product at Raven Tools: the interaction between pricing and churn, why "they left because of price" is not actionable on its own, and why a launch platform like AppSumo can buy you a committed early user base. He also maps where marketers went after the Twitter exodus: Threads, Bluesky and Slack communities. Tom's line on where this is all heading: "I think being human, being as human as possible, connecting with your audience on a very human level, it's going to be something that's going to be pretty large going forward." More founder stories from the show: Founder stories and bootstrapping. Try Taproot free for 30 days at taproothq.com/pricing. Hosted by Jeremy Rivera.

Fifteen years building SaaS across European scale-ups, including the unicorn Brevo, compressed into two words. Malith Gamage bootstrapped Zapdigits, a client-reporting tool for marketing agencies, and his survival advice is not a framework. It is do not die. Stay alive three or four years and you are fine. Everything else in this episode is the operational discipline that makes that possible. Key takeaways Survival is the strategy, not a precondition for one. No in-house hires, infrastructure scaled only when needed, and a year of AWS credits used as a cash-flow cushion. White labelling sits on every plan, not the top tier. Agencies have to look more put-together than a client’s internal marketing team, so branding is the product rather than an upsell. He ships AI visibility reporting and openly doubts it. Ask the same question five times and get five answers pulled from five places. Domain authority did not predict who got cited. His own unpromoted, zero-authority domain was the source models pulled from, while the promoted one was not. A lifetime deal run as a feedback engine. The value was buyers who kept sending feature requests, not the one-off revenue. On this page Do not die Your AI is only as smart as your schema Shipping a feature he does not fully trust White labelling, and a lifetime deal as a feedback engine Chapters and timestamps People, ideas and sources mentioned Questions this episode answers Go deeper Do not die Some main advice is don’t die. Like as long as you can stay for like three, four years alive, then you’re good.— Malith Gamage Unromantic, and more useful than most strategy. The failure mode for a bootstrapped tool is almost never being out-competed on features. It is running out of runway during the years when nothing is visibly working. So the discipline is structural: no in-house hires, infrastructure scaled only when demand actually arrives, and a year of cloud credits treated as a cash-flow buffer rather than free compute to burn. Your AI is only as smart as your schema Your AI is smart as your schema. As long as you give the correct data and the correct description, then you will get good answers back.— Malith Gamage He draws the parallel to MCP without prompting, and it is the reason he pipes context in before asking anything: pull the Analytics data first, describe it properly, then ask the question. The quality ceiling is set at the data-description step, not at the model. Shipping a feature he does not fully trust I don’t even know sometimes if you ask the same question five times you get five different answers they pull out from five different places.— Malith Gamage Zapdigits aggregates brand visibility across several models and averages it into a score for agency clients. He will t...

Guest Bio Kevin Urrutia is a software engineer turned serial entrepreneur with 20+ years of experience building tech products. He's worked at Silicon Valley companies including Mint.com and Zaarly, and founded Voy Media, a digital marketing agency that has generated over $50 million in revenue. His latest venture, Magic Rinku, tackles one of SEO's most persistent challenges: automating internal link building at scale. Connect with Kevin: Email: [email protected] Twitter: @danest Website: Magic Rinku Episode Summary In this unscripted conversation, Kevin shares the journey of building Magic Rinku from personal frustration to SaaS solution. We dive deep into the technical challenges of WordPress integration, the strategic use of AI versus traditional algorithms, and unconventional marketing approaches that are actually working in 2025. Kevin reveals how he discovered 350 out of 400 articles on his own site had zero internal links, the technical nightmare of supporting multiple WordPress page builders, and why cold email and Reddit still outperform traditional SaaS marketing for developer tools. Key Topics Covered The Genesis Story How Kevin's personal pain with internal linking across 8-10 affiliate sites led to Magic Rinku The validation process through Reddit and direct customer conversations Why most SEO professionals are missing huge linking opportunities due to broken manual processes Technical Deep Dive WordPress ecosystem challenges: Supporting Elementor, Beaver Builder, Gutenberg, and Classic Editor The 30-second delay problem and why bulk operations are complex in WordPress Database structure differences across page builders and how they affect plugin development Error handling and retry systems for WordPress API integration AI Implementation Philosophy Strategic use of AI vs. traditional algorithms (RAKE, ENG tagger) Two specific AI use cases: contextual understanding and smart sentence generation Why 30% of AI suggestions are perfect, 50% need tweaking, and 20% are unusable The explainability problem and showing confidence scores to use...

In this conversation, Jeremy Rivera of Community Clean Links and SEO Arcade interviews Angshuman Rudra, a product manager at TapClicks, discussing the company's marketing operations platform, the intricacies of ETL processes, and the challenges of product management in a SaaS environment. Angshuman shares insights on customer needs, market trends, and the importance of sales feedback in shaping product development. He's been heading up the creation and product management of their new TapDataMax product, and shared a lot of detail on the way Tapclicks views the "movement" of data. The discussion also touches on the evolving terminology in marketing technology, the role of AI in data management, and the future roadmap for TapClicks, including new features and integrations.

In this conversation, Jeremy Rivera interviews Bryan Shankman, co-founder and CEO of Tool Desk and LeadTruffle discussing the origins of their SaaS products, the challenges of pricing and customer support, and the strategies for marketing and future-proofing their business. Bryan shares insights on building a successful startup in the home services industry, the importance of customer engagement, and the risks associated with relying on a platform like Jobber. In this conversation, Bryan Shankman discusses the evolution of his SaaS product, Tool Desk AI, focusing on the challenges and opportunities presented by platform dependencies, the innovative features of their AI-driven lead qualification tool, and the streamlined onboarding process for home service businesses. He also shares insights on marketing strategies and offers valuable advice for aspiring SaaS entrepreneurs.takeaways Tool Desk started with a focus on home services and lead qualification. The initial product was inspired by a need for ringless voicemail systems. Pricing is strategically set at $149/month for the Jobber plug-in. Customer support is highly personalized, with direct communication from founders. The company is 100% bootstrapped, allowing for thoughtful financial decisions. Cold outbound marketing has been effective for customer acquisition. Building a product that fills gaps in existing platforms is crucial. Platform risk is a significant concern for businesses relying on third-party services. Future-proofing involves diversifying product offerings and reducing dependency on a single platform. The founders prioritize building something people want and scaling thoughtfully. The value proposition of Tool Desk AI is to fill marketing automation gaps. Platform dependency can pose significant risks for SaaS businesses. Tool Desk AI acts as a 24/7 sales representative for capturing leads. AI technology is being utilized to enhance lead qualification processes. Onboarding for home service businesses is designed to be frictionless and quick. The AI is trained on each customer's website to provide tailored interactions. Ev...

In this conversation, Jeremy Cohen, founder of Vooz an Omegle alternative in the anonymous video chatting space, discusses the journey of creating bootstrapping and self-motivating. He shares insights on the unique selling propositions of Vooz, including AI moderation and user engagement features. The discussion covers the challenges of bootstrapping a startup, marketing strategies for user acquisition, and the importance of building a strong team. Jeremy also emphasizes the significance of taking action and starting with design, as well as the potential for monetization and growth in the SaaS space.Take Aways   Vooz aims to innovate in the anonymous video chat space with AI moderation. User engagement features like screen sharing and games are key to Vooz's strategy. Bootstrapping has been essential for Vooz's development and team formation. Finding motivated co-founders can be challenging but crucial for success. SEO and PPC are primary marketing strategies for user acquisition. Understanding how pricing strategy impacts SaaS marketing decisions Influencer partnerships can drive initial traffic but should be complemented by SEO efforts. Monetization opportunities are abundant once a user base is established. Starting with design helps in attracting talent and building a vision. AI moderation is a significant technical achievement for Vooz. Taking action and starting somewhere is vital for aspiring entrepreneurs.

Guest Introduction Mark Williams-Cook: Director of Kanda (formerly Candour SEO agency) Creator of Also Asked, a search intent research tool Publisher of Core Updates Newsletter Host of SEO Patent Podcast 20+ years of SEO experience Key Topics Covered Current Google Search "Meta" Google's algorithm favors brand-like signals over traditional relevance Example: The Vatican website ranking for CBD gummies after being hacked Mark describes this as "a bandaid for the shortfalls that have been magnified in Google's algorithm because of Gen.ai" Content Creation Strategy Video-first approach: "Start at video and work backwards" using transcription tools Particularly effective for client information: "If you are having to get information, especially from clients, sometimes trying to get clients to write an article is like getting blood out of a stone" Allows natural expertise to flow: "They will happily talk your ear off" Tools turn video into written content without "regurgitating stuff" User Knowledge Levels Google has patents for understanding user expertise through search patterns Demonstrated in People Also Ask (PAA) results showing different questions based on perceived knowledge Example: Car dealership knowledge levels Novice: "Red car under $20k near me" Intermediate: "2024 Honda Civic MPG vs Toyota Corolla" Expert: "Civic Type R torque curve comparison" Google Query Classification Google classifies queries into distinct semantic categories These classifications determine which SERP features appear Additional classifiers like "local intent" and "explicit local intent" work alongside these categories Mark is develop...