Series: Building in Public Journal | Week 3 of 4

There is a particular kind of discipline required to build boring things on purpose. Not that I ran out of ideas, not because the sprint ran out of steam, but because I made a deliberate choice to prioritise what holds the entire structure together over what looks impressive in a demo.

That is what Week 3 was.

If you are joining this series for the first time: I am building the LITV AI SEO Agent 2.0 live during the four-week Women in AI Accelerator run by Build Club. The sprint is a structured programme that pushes you to ship a meaningful product in a compressed timeline in public, and with accountability deposited.

Week 1 was architecture. Week 2 was auth and the first real code commit. You can read both in the Week 1 build journal and the Week 2 build journal. Week 3 was the work nobody photographs.

Why Compliance Week is Not a Consolation Prize

Most build-in-public journals skip this part. They go straight from “auth is live” to “dashboard is beautiful.” The compliance layer sits quietly in between, undocumented and unappreciated until something breaks and suddenly the business is exposed.

I built Week 3 the way I did because I have spent enough time in marketing and communications to know that trust is not earned through features. It is earned through consistency, transparency, and the things users see before they ever click a button. A privacy policy is not a legal formality. 

For the LITV AI SEO Agent 2.0, which audits websites for AI search visibility and generative engine optimisation, credibility is the product. If I am building a tool that helps brands earn trust in AI-mediated search, that tool has to demonstrate exactly the same behaviour it advises.

What Shipped in Week 3

1. Weights Rebalanced and Disclosure Page Published

The AI Visibility Index is one of the core scoring engines inside the LITV AI SEO Agent 2.0. It measures how visible a brand is across the major AI-powered search platforms: ChatGPT, Google AI Overviews, Perplexity, Gemini, Bing Copilot, and Claude.

These weightings reflect current market share and citation frequency data, not arbitrary preference. They will be updated as the landscape shifts.

Every user who receives a specific audit score can see exactly how that score is calculated, which platforms are included, what the methodology is, and when it was last updated. AI search is a citation game. If this tool asks brands to be transparent and citable, the tool itself has to model closely to that behaviour first.

2. Consent and Analytics, Done Properly

Analytics and audits are wired in, but not in the way that fires everything the moment someone lands on the page. A consent gate is in place. EU and UK visitors see a consent banner before any tracking fires. 

This matters more than most product builders acknowledge. Consent-less analytics is not just a legal risk; it is a data quality problem. Traffic data collected without user consent produces unreliable numbers that lead to poor decisions. Building the consent layer properly from the start means the data that does come through is not only clean but trustworthy.

3. Legal Pages Live

Privacy policy, terms of service, cookie policy, and contact details are all live and styled to the light and dark themes. These were not templated and left to gather dust. Each section was reviewed against the actual data flows in the product. What is collected, where it lives, how long it is retained, and who can request deletion. It took longer than expected, and it always does.

4. Single Pro Tier

The Starter tier is gone. There is now one product plan and that is it.

The decision to kill Starter was not spontaneous. The original logic behind a Starter tier was to provide a low-commitment entry point. What it actually created was decision fatigue at the pricing page, a more complex licence webhook to maintain, and a middle-tier ambiguity problem where users could not clearly identify what they were getting for the price differential.

One tier eliminates all of that. The free access level remains for users who want to run a single audit and see their scores. Pro unlocks the full access without upselling the dashboard workflow, full audit, AI Visibility tools and more. The value distinction is clear. The pricing page is now a single decision.

5. Crawler Hardening

On the backend, the crawler that powers the technical audit pipeline received several meaningful updates. The maximum pages per audit is capped. A minimum two-second delay between requests is enforced to ensure the agent is not acting as a hostile scraper against the sites it is auditing. A bug in the robots.txt parser was fixed. Multiple backend tests are passed. The user agent string is now declared as LITV-AISEOAgent/2.0, which is a small but important commitment: the agent identifies itself honestly when it crawls.

Why the Boring Week is the Most “Sweatshop” Week

Here is the honest CMO take on Week 3. Compliance and pricing clarity are not housekeeping tasks you squeeze in before the exciting work. They are the foundation on which every acquisition and retention decision rests.

AI search is a citation game, not a ranking game. I have written about this in the context of AI Overviews reducing organic clicks. The same logic applies here. Brands that want to be cited by ChatGPT, Perplexity, and Google AI Overviews need to be structured, transparent, and machine-legible. A tool that audits for those qualities has to demonstrate them itself. Publishing the methodology is not a nice-to-have as it is a launchpad for orchestrating and architecture decision-making.

Every one of these choices is a signal to the user about what kind of product this is and what kind of builder is behind it.

What is Coming in Week 4

Week 4 is the polished dashboard that is the centre of the universe here. With one-click re-audit from the user’s view, and the AI Visibility Tools that let users see where their brand is and is not being cited across the six platforms tracked by other specifications that I am still considering to add on.Week 3 was the foundation of the scaffolding work. Week 4 is what cleaning up means. In between Claude Code, ChatGPT, LLM models, ChatGPT 5.5 became my sweeper who would occasionally flagged dirty worktrees and the time is up to clean. It is like similarly how I would treat my spreadsheets. To be honest, I cannot just rely on one AI model like Claude Code to handle everything. I’d still need to keep an open mind to allow other models to contribute and that’d eventually lead me to better decision-making. In fact, due to the heavy traffic uses and certain peak hours of Claude Code and Claude, the AI slops can generally occur, and there are occasions when I had to stop using it.

Thanks to Build Club, Annie Liao and Caroline Ciaramitaro for hosting and facilitating this 4-week immersive AI building programme.