Series: Building in Public Journal | Week 4 of 4

There is a difference between a tool and a product. A tool runs once. A product has to keep working when a user signs in, runs an audit, reads the result, asks what to fix, exports the work, comes back next week, and gives feedback when something feels off. Week 4 was the week LITV AI SEO Agent 2.0 crossed that line. That is the distinction that defined Week 4. And it is the most honest thing I can say about what this sprint taught me.

If you are joining this series for the first time: I have been building the LITV AI SEO Agent 2.0 live during a four-week sprint called the Women in AI Accelerator, run by Build Club and hosted by Annie Liao and Caroline Ciaramitaro. Week 1 was architecture and choosing not to build the easy version. Week 2 was auth, staging, and the first real code commit. Week 3 was the unglamorous compliance and layers of backend work that holds the entire structure together. Week 4 was where the loop had to stick and the product around the engine had to materialise.

From Engine to Product: The Shift that Defines What Shipped in Week 4

The audit engine was always the centrepiece of the build. It crawls publicly accessible URLs, analyses them across Technical SEO, SXO, GEO, and AEO signal layers, and returns a more structured intelligence report. It runs in a loop: it enters a website, processes the allowed pages, aggregates the scoring, delivers the output, and waits for the next request.

What Week 4 made clear is that the engine being operational is not the same as the product being usable. The harder work is everything around the output: access control, delivery, data handling, error states, mobile behaviour, admin workflows, and the feedback path that tells you what users actually experience when the engine does its job.

That surrounding layer is what Week 4 was for. Here are the five highlights that defined it.

5 Key Highlights from the Week 4 Build

1. The Audit Loop Held and What that Means

This is the technical win I am most proud of from the entire sprint. The audit loop has to function at three stages: initial entry into the target URL, mid-stage signal processing across the four audit frameworks, and final output generation into a structured report from the layers of audit and visibility. If it breaks at any one of those stages, the failure is clear as day. If it holds across all three, I have the evidence that the architecture is sound.

The loop broke three times during Week 3 before it held consistently. Two breaks were in the signal extraction layer, where edge cases in structured data markup were not being handled correctly. Each break was fixed directly in the engine’s core logic, then patched through to the surface. The loop now holds on standard sites and on slow-crawl days where the pipeline is under more stress and on downtime days.

An AI-powered engine that fails silently is not an engine. It is a liability that breaks the loop and can run simple autofixes.

2. The Dashboard became Usable, unlike a Scratch Surface

The dashboard had to stop being fragile. Week 4 addressed the layout across desktop, tablet, and mobile views — tightening panels, making wide issue tables scroll inside their own containers, and making the interface feel calmer and more consistent across different screen sizes.

Accessibility hardening was also part of this. This is not the glamorous part of an AI product build. But it is the part users feel immediately, and the part that determines whether someone who is not technical trusts the product enough to use it a second time.

3. The Fix Pack became a part of the Workflow, Not as a Download

The Fix Pack is the action layer of the audit. It takes the findings from the Technical SEO, SXO, GEO, and AEO report and turns them into a prioritised set of recommendations that a user can actually work through.

In version 1.0, Fix Pack output was just a static download. Week 4 moved it towards a proper execution workflow. Paid users can now work with PDF and CSV outputs through clearer delivery paths, and the product is materialising to support the rhythm I care about most: audit, identify the gaps, prioritise the fixes, validate the key blockers, and re-audit for better indexing to take place. That cycle is the actual value proposition of the product. A dashboard full of scores with no path forward is just anxiety in a UI.

4. Telegram Delivery Got a Proper Verification Flow

Telegram delivery has been on the roadmap since early in the build. The idea is straightforward: users who want their audit results and Fix Pack outputs delivered directly to Telegram can connect the LITV bot, @litv_agentic_bot, through a clean verification step rather than manually pasting chat IDs or guessing what to do inside the bot. Week 4 tightened the backend and frontend around that flow. This matters for non-technical users who would otherwise abandon the setup at the first point of ambiguity.

5. Beta Users will Soon Be Inside the Loop

Beta licence keys are prepared for Build Club testers to access the paid-tier dashboard experience during the upcoming testing window. A standalone beta feedback form was built intentionally separate from the main product navigation to capture product feedback.

Final Conclusion: Nothing is Final When You are Building Something Serious

The Women in AI Accelerator is a four-week sprint, and the LITV AI SEO Agent 2.0 is not a four-week product. The sprint gave me structure, community support, accountability, and the enforced function to ship what I had been planning since before I joined the accelerator. But nothing about this build is 100% finished because nothing about the problem it solves is finished.

Why is it Not Finished?

AI search is evolving. The platforms that mediate it are evolving. The signals that determine citation and visibility will shift, affecting some of them before the end of this year. A product built to audit those signals has to be built with the assumption that the audit criteria will change. That is not a caveat. It is the full product and design brief.

The comprehensive audit and visibility engine inside LITV AI SEO Agent 2.0 was always the centrepiece of the build. But an engine without a usable product around it is infrastructure, and not a service. Week 4 was where the two finally met and they clicked.

What’s Coming for Demo Day

TBC.

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

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.