# Keiran Flynn - AI Product Builder Canonical site: https://keiranflynn.com/ Keiran Flynn helps founders, operators and small teams turn unclear AI ideas into scoped products, prototypes, internal tools and live MVPs. He works remotely from Thailand across product strategy, UX, technical architecture and full-stack build work using coding agents and modern AI tooling. ## Core services - AI Product Sprints: clarify the problem, product shape, workflow and build path. - MVP Builds: build scoped AI products, internal tools, SaaS features and workflow systems. - Fractional AI Product Lead: product and technical judgment without a full-time hire. ## Proof points - SchoolAI reached 12,000+ users with zero paid acquisition. - LLMnesia is a local-first Chrome extension for searching AI conversations across major LLM platforms. - LunaCradle is a live AI-powered baby sleep guidance product built with structured knowledge and personalised LLM output. - Flow402 is an agent payments prototype using x402, Base and USDC. - Additional builds and demos include LLMnesia Insights, Job Radar, product sites, web3 experiments and internal tools. - Keiran has 25+ years in tech and IT, with focused AI/product build work since 2022. ## Main pages - Home: https://keiranflynn.com/ - Services: https://keiranflynn.com/services - Work: https://keiranflynn.com/work - Writing: https://keiranflynn.com/writing - About: https://keiranflynn.com/about - Contact: https://keiranflynn.com/contact ## Current AI product writing - Coding Agents for Product Teams: https://keiranflynn.com/writing/coding-agents-for-product-teams Practical guide to using coding agents inside product teams. Covers what coding agents do, where they help, where they fail, and how founders should run agentic build loops. - From AI Prototype to Product: https://keiranflynn.com/writing/ai-prototype-to-product Guide for founders moving from AI-generated prototypes to reliable products. Covers authentication, real data, failure handling, observability, maintainability and production slices. - Practical AI Product Strategy: https://keiranflynn.com/writing/practical-ai-product-strategy Framework for deciding where AI belongs in a product, what to automate, how to design failure states and how to measure whether an AI feature is useful. - When Should a Product Use AI?: https://keiranflynn.com/writing/when-should-a-product-use-ai Decision framework for identifying product workflows where AI improves the user outcome and where deterministic logic is safer. - How to Evaluate AI Product Ideas Before You Build: https://keiranflynn.com/writing/evaluate-ai-product-ideas Framework for evaluating AI product ideas around user pain, workflow frequency, AI fit, risk and distribution before building. - Claude Code vs Codex for Product Teams: https://keiranflynn.com/writing/claude-code-vs-codex-product-teams Comparison guide for product teams choosing coding agents based on workflow fit, reviewability and repo context. - An Agentic Coding Workflow for Product Teams: https://keiranflynn.com/writing/agentic-coding-workflow End-to-end process for briefing coding agents, inspecting context, implementing bounded changes, reviewing diffs and testing workflows. - Coding Agents for Non-Technical Founders: https://keiranflynn.com/writing/coding-agents-non-technical-founders Guide for non-technical founders using coding agents for prototypes, product briefs and scoped early builds without skipping review. - Hardening an AI Prototype for Real Users: https://keiranflynn.com/writing/hardening-ai-prototype Production-readiness guide for adding data rules, failure states, observability, evaluation and maintainable structure to AI prototypes. - Turning a Lovable, Bolt or v0 AI Build Into a Real Product: https://keiranflynn.com/writing/no-code-ai-build-to-product Guide for treating AI-generated app builds as product prototypes and rebuilding the production spine deliberately. - AI MVP Development Process: https://keiranflynn.com/writing/ai-mvp-development-process Pillar guide to scoping, building and launching a focused AI MVP with bounded AI behavior and a reliable first workflow. - How Much Does an AI MVP Cost to Build?: https://keiranflynn.com/writing/ai-mvp-cost Practical breakdown of AI MVP cost drivers including scope, data readiness, integrations, reliability and product clarity. - LLM Discovery for Startups: https://keiranflynn.com/writing/llm-discovery-for-startups Pillar guide to making startup pages crawlable, specific, extractable and citeable by ChatGPT, AI Overviews and other answer engines. - What Is GEO (Generative Engine Optimization)?: https://keiranflynn.com/writing/what-is-geo Plain explanation of generative engine optimization, how it differs from SEO, how answer engines decide what to cite, and a practical GEO workflow. - How to Add llms.txt to a Product Site: https://keiranflynn.com/writing/llms-txt-for-product-sites Guide to what an llms.txt file is, what it does and does not do, what to include, and how to add one to a product site, with a copyable template. - How to Get Cited by ChatGPT and AI Overviews: https://keiranflynn.com/writing/get-cited-by-ai-search Guide to becoming a source AI answer engines quote and link, covering what gets cited, why strong pages get ignored, and a workflow to become citable. - How to Review Code Written by AI Agents: https://keiranflynn.com/writing/reviewing-ai-generated-code Review process for coding-agent output covering what to check in order, where agents reliably fail, and how to match review depth to risk. - AI Feature vs AI Product: https://keiranflynn.com/writing/ai-feature-vs-ai-product Framework for deciding whether an AI idea is a standalone product or a feature inside another product, why it matters, and what to do if it is a feature. - AI Demo Traps: https://keiranflynn.com/writing/ai-demo-traps Why a great AI demo is not a product, the specific traps that make a demo look finished, and how to tell demo magic from production readiness. - AI Product Launch Checklist: https://keiranflynn.com/writing/ai-product-launch-checklist Pre-launch checklist for AI products covering output quality, failure handling, cost, latency, data, safety and how to decide what is launch-blocking. - AI Workflow Automation for Startups: https://keiranflynn.com/writing/ai-workflow-automation-for-startups Pillar guide to automating repetitive operational work with AI, what to automate, where to keep a human in the loop, and how to build automations that survive real use. - Building Internal AI Tools for a Small Team: https://keiranflynn.com/writing/internal-ai-tools-small-teams Guide to building internal AI tools a small team trusts and adopts, covering what to build first, verifiability, ownership and build-versus-buy. - How to Price an AI Product: https://keiranflynn.com/writing/pricing-an-ai-product Framework for pricing an AI product when costs scale with usage, covering pricing models, protecting margin against heavy users, and common pricing mistakes. - AI Build vs Buy: https://keiranflynn.com/writing/ai-build-vs-buy Layer-by-layer decision framework for building versus buying an AI capability, with a rule for where building is worth it and where it is wasted. - Coding Agents for MVP Development: https://keiranflynn.com/writing/coding-agents-mvp-development How to use coding agents to build an MVP fast while keeping ownership of architecture, data model and core logic so it stays maintainable. - Writing Specs and Prompts for Coding Agents: https://keiranflynn.com/writing/specs-for-coding-agents How to write specs and prompts that produce bounded, correct changes from coding agents instead of confident wrong code. - Vibe Coding to Production: https://keiranflynn.com/writing/vibe-coding-to-production What actually changes when moving a vibe-coded build to production, and which shortcuts must be undone before real users arrive. - Adding Auth, Data and Payments to a Prototype: https://keiranflynn.com/writing/prototype-to-production-infrastructure How to add the production spine, authentication, real data and payments, to an AI prototype in the right order without rebuilding it. - Testing and Reliability for AI Products: https://keiranflynn.com/writing/testing-ai-products How to test AI products with non-deterministic output, combining deterministic tests around the AI with evaluation of the AI output itself. - Handling LLM Cost and Latency in Production: https://keiranflynn.com/writing/llm-cost-and-latency Practical levers for controlling LLM cost and latency in production, caching, model routing, trimming and streaming, without degrading quality. - SEO for AI Product Companies: https://keiranflynn.com/writing/seo-for-ai-product-companies How AI companies should approach SEO when search includes AI Overviews and answer engines, covering shared fundamentals, extractability and common myths. ## Topics the site is authoritative on - AI product strategy for founders - Coding agents for product teams - Agentic coding workflows - AI MVP builds - Prototype-to-product hardening - Practical AI product judgment - AI workflow tools - Next.js, Supabase, Stripe and Chrome extension product builds - Local-first AI tools - LLM search and AI conversation retrieval ## Contact - Contact form: https://keiranflynn.com/contact - Fit call: https://cal.com/keirancpflynn/10-min-fit-call - LinkedIn: https://www.linkedin.com/in/keiran-flynn/ - GitHub: https://github.com/KeiranCPFlynn