From Concept to Launch: Building a Founder Networking Platform in 5 Weeks
April 9, 2026
How WebLaunch built HeySEO, an AI-powered SEO analytics platform that connects Google Search Console and GA4 with Claude AI for automated insights and reporting.
Loic Bachellerie
April 9, 2026
Most SaaS products fail at the idea stage. They sound useful until someone has to build them - and then reality sets in. HeySEO is the kind of product that could have died a dozen times before launch: complex OAuth integrations, multi-tenant data architecture, a real-time AI layer on top of live analytics, and subscription billing on day one. We built it anyway, shipped it, and now it has paying users.
Here is how we did it, and what it means for founders who need a technical partner to go from zero to SaaS.
Google Search Console is one of the most powerful free tools available to any website owner. It also demands an uncomfortable amount of manual effort to extract value from.
Every week, SEO managers and site owners repeat the same ritual: export data, drop it into a spreadsheet, calculate click-through rate changes, hunt for pages that dropped, try to remember what changed last month, write a summary for the stakeholder meeting. The data is all there. The insight is the expensive part.
The tools designed to help - enterprise SEO platforms, analytics dashboards, rank trackers - mostly repackage the same data with better charts. They don't think. They don't ask questions. They don't tell you why a page that ranked well last quarter is now losing ground.
The SEO analytics category had a capability gap that AI was purpose-built to fill. We built HeySEO to close it.
The concept behind HeySEO is straightforward: connect your Google Search Console and Google Analytics 4 accounts, then talk to your data the way you would talk to a senior SEO analyst.
Ask it: "Which pages lost the most organic traffic this month and what might explain it?" Ask it: "Are there high-impression, low-CTR queries I should be optimizing for?" Ask it: "Give me a summary I can share with my client."
The AI reads your actual data - not generic SEO advice, not templated recommendations - and responds with context-specific analysis. On top of the chat interface, HeySEO delivers automated weekly and monthly reports directly to your inbox, a full analytics dashboard, and a suite of SEO tools including PageSpeed audits, indexing checks, SERP previews, and on-page analysis.
The goal was to replace two to three hours of manual weekly SEO work with a platform that runs in the background and surfaces what matters.
Every technology decision in HeySEO was made to serve a specific requirement. Here is how the stack maps to the product.
We chose Nuxt 4 as the application framework because HeySEO needed server-side rendering for performance and SEO (a SaaS tool about SEO should rank well), file-based routing to keep a growing feature surface organized, and a component architecture that scales cleanly across a complex dashboard. Vue 3's Composition API gave us the reactive state management we needed for real-time data updates without reaching for a separate state library.
Nuxt's server routes also let us build lightweight API endpoints within the same codebase, which kept the architecture simple during early development while still giving us full control over server-side logic.
Multi-tenant SaaS has hard authentication and data isolation requirements. Firebase Auth handled user management, OAuth flows with Google (required for GSC and GA4 access), and session management out of the box. Firestore gave us a flexible document model well-suited to storing per-user analytics state, AI conversation history, task boards, and team membership - data that is highly relational within a tenant but isolated between tenants.
The real-time capabilities of Firestore were specifically useful for the Kanban task board and collaborative team features, where multiple users on the same account can see updates without a manual refresh.
The AI layer is the product's core differentiator, and the model needed to handle variable-length context, follow multi-turn conversation, and reason over structured analytics data without hallucinating numbers it hadn't been given.
Claude was the right choice here. Its context window is large enough to include meaningful slices of GSC and GA4 data within a prompt. Its instruction-following is reliable enough to maintain a consistent persona and output format across a session. And its reasoning quality is high enough that the answers are actually useful - not just plausible-sounding.
The API integration goes beyond a simple chat wrapper. For automated reports, we built a pipeline that pulls fresh data on a schedule, constructs context-rich prompts, and delivers formatted analysis via email. The AI is doing real work on real data, not generating generic SEO content.
Stripe handled subscription billing from day one, including tiered plans, trial periods, and webhook-based access control. This is non-negotiable for a SaaS product - billing that leaks or has poor UX kills conversion.
Resend powers transactional email, including the automated AI reports and account notifications. Vercel handles deployment with the edge performance Nuxt is optimized for. Sentry monitors production errors so issues are caught before users report them.
This is a production-grade stack. Not a prototype stack. Everything here is chosen for reliability and developer experience in equal measure.
Building an AI analytics platform is harder than building an AI chatbot. The data is live. The context is user-specific. The expectations are high. These were the three problems that required the most careful engineering.
The hardest part of the AI layer was not the API integration. It was prompt architecture.
A general-purpose LLM knows a great deal about SEO. It does not know anything about your specific website unless you tell it. Every AI conversation in HeySEO is grounded with data fetched at query time: the user's recent GSC performance, their GA4 traffic trends, their historical conversation context. We built a context assembly pipeline that selects, formats, and injects the right data for each query without exceeding token limits or degrading response quality.
For the automated reports, the challenge was different: we needed consistent, high-quality output at scale across many users, without manual review. We designed a structured prompt template that guides Claude toward specific output formats - executive summary, key findings, recommended actions - so the reports are useful and consistent, not variable in quality depending on how Claude interpreted the request on a given run.
HeySEO serves multiple users, each with their own Google accounts and data. The data pipeline needed to fetch, cache, and refresh analytics data per-user on a schedule - without cross-contaminating accounts, without unnecessary API calls that hit Google's rate limits, and without slow load times that make the dashboard feel broken.
We built a per-tenant data layer with Firestore that stores processed analytics snapshots alongside raw data access tokens, with refresh logic that runs on a schedule and on demand. The result is a dashboard that loads fast because the heavy Google API work happens asynchronously, not on page load.
Stripe's webhook model means your billing logic is event-driven and asynchronous. A user upgrades, Stripe fires a webhook, your server updates the user's access tier, and they get new features - ideally without any noticeable delay or error.
Getting this right required careful webhook handling: idempotency checks, retry logic, and access control checks at the route level that read from a source of truth that Stripe webhooks write to. We also built a team collaboration layer on top of this, where subscription limits apply at the account level rather than the user level. One subscription, multiple seats, shared analytics - with proper role-based access so team members can collaborate without seeing billing details.
HeySEO launched with the full feature set: AI chat, automated reports, analytics dashboard, SEO tools, Kanban board, team management, and subscription billing across multiple tiers. It connected to real Google accounts on day one through a proper OAuth flow. It sent its first automated report on schedule.
The build went from concept to production in weeks, not months. That pace is possible when the architecture decisions are made correctly upfront, when the technology choices are grounded in the actual requirements of the product, and when the team building it has built SaaS products before.
HeySEO now has paying subscribers using it to manage real websites. The AI reports are going out every week. The data is accurate. The billing works. The product does what it was designed to do.
If you have a SaaS idea that involves real data, AI, and the expectation that it works reliably on day one - that is what WebLaunch builds.
We are not a development shop that takes a spec and returns code. We are a growth engineering studio that makes architectural decisions, chooses the right technology for your specific requirements, solves the hard problems that kill early-stage products, and ships something you can actually sell.
HeySEO is an example of what that looks like in practice. A complex product - multiple OAuth integrations, AI over live data, multi-tenant architecture, subscription billing - built properly and shipped fast.
If you are sitting on a product idea that needs a technical partner to bring it to life, that is the conversation we want to have.
Get in touch with WebLaunch to talk about your project.
Let's discuss how we can help you achieve your goals online.