Build Log #1: The Starting Line

This is the first entry in our build log — an honest, public account of building MCP Analytics. Every week or two, I'll share where we are, what's working, what's failing, and what we're learning. Real numbers. No spin.

Here's where we start.

What MCP Analytics Is

MCP Analytics is a statistical analysis and reporting platform. You upload data — a CSV from Shopify, a marketing spreadsheet, a research dataset, anything tabular — choose an analysis type, and get back a complete interactive report. Charts, statistical tests, diagnostic checks, plain-English insights, and the R code that produced it all.

The same kind of deliverable a data science consultant would produce in two weeks for $5,000. We generate it in 30 seconds for a few dollars.

Two ways to use it: a web app for anyone (upload, click, read), and an MCP protocol server for developers and AI agents (run analyses from Claude, Cursor, or any MCP-compatible client).

The Numbers — Honest

People are finding us organically — through search, AI referrals from ChatGPT and Claude, and MCP directory listings. The growth is early but the trajectory is clear.

Why I Think This Works

Julius AI has 2 million users and $1M in annual revenue selling AI-generated data analysis. Their product writes new Python code every time you ask — different code, different results, 3.0 out of 5 on Trustpilot. Users complain about fabricated data and inconsistent outputs.

We do the same job with a fundamentally different approach. Every analysis uses validated R modules — the same statistical packages used in published research. Same data in, same report out, every time. The report includes the R code so anyone can verify the results independently.

The market is proven. People are already paying for AI data analysis. I just need to get my version in front of them.

What I Built (And How)

Here's the part that's unusual. I built this platform mostly with AI agents.

The analysis modules — each one a validated R script implementing a specific statistical method — are built by autonomous Claude agents. I write a specification describing what analysis to perform. The agents write the R code, test it, verify the output, and deploy it to production. A new module goes from spec to live in under 24 hours. No data scientists hired.

The daily operations — error monitoring, traffic analysis, lead review, content generation, QA — are run by 62 automated skills that I trigger through Claude Code. Think of them as specialized workflows: "/error-check" reviews the last 24 hours of logs. "/traffic" pulls analytics. "/deploy-api" pushes code to production with zero-downtime reload.

I run this like a 10-person company. It's one person and a very sophisticated harness of AI agents, hooks, and automation.

I'm going to open-source parts of this operational system — particularly the task management framework — because I think other solo founders could use it. More on that in a future log entry.

What We're Improving

Content-market fit. Our statistical method articles bring traffic — 200K+ monthly impressions — but the audience is researchers, not buyers. The shift to business-outcome content is already underway. Different audience, same product, much better alignment.

First-run experience. New users land on a dashboard and need to figure out what to do. We're adding pre-loaded sample datasets with one-click analysis buttons — see a complete report in 30 seconds, before you even upload your own data. Eliminate every decision before the first payoff.

In-product feedback. Every report now has per-slide ratings and issue reporting. When multiple users flag the same problem, the fixer agents kick in automatically. The product improves itself from usage.

What We're Doing Now

We're shifting from statistical method content to business-outcome content. Instead of "what is logistic regression," we're writing "how to analyze your Shopify churn" and "prove your marketing ROI to your CFO." The people who search for those things have budgets.

We're building a tutorial series teaching businesses how to use AI for their own analytics. The first project: we replaced our Google Analytics dependency with a self-hosted analytics database, built entirely with Claude Code. That build process is now a multi-part tutorial. We teach by doing, and the platform is the working example.

Every analysis module has a free tool page where anyone can upload a CSV and get a real report. No signup wall on the first report. The report is the demo — if it's good enough, the pricing page is one click away. We're running our own business data through our own platform and publishing the results, because if we're going to sell data analysis, we should be using it ourselves.

We're also showing up in the communities where our users hang out — r/shopify, LinkedIn, the Anthropic Discord — with genuinely useful analysis. Not product spam. Real answers to real questions, backed by real data.

Why This Log Exists

Building a SaaS in 2026 with AI agents doing most of the engineering is new territory. The operational system running this company — 62 automated skills, 25 hooks, 5 specialized agents, daily intelligence reports, self-healing error resolution — isn't something I've seen documented elsewhere. I'm going to open-source parts of it because I think other founders can use it.

This log is the honest record. What works, what doesn't, what the numbers say. If you're building something similar, maybe this saves you some time. If you're curious what it looks like to run a company with AI agents, this is it.

The Experiment: Can You Manifest Growth?

There's a concept in goal-setting circles called manifesting — writing down your goals as if they've already happened, then working toward them with that certainty. Some people swear by it. Others call it wishful thinking.

We're going to test it. With data.

Here are our stated targets for the next 90 days:

These aren't wishes. They're based on current trajectory, the marketing plan described above, and what we've seen competitors achieve at similar stages. But they're also commitments — written down, published, dated.

At the end of 90 days, we'll run the actual numbers through our own platform. Time series analysis on signups. Trend decomposition on traffic. Churn analysis on user retention. The same statistical methods we sell to customers, applied to our own business.

Did writing it down and working toward it with certainty produce different results than the baseline trajectory predicted? We'll find out. And we'll publish the analysis — because that's what we do.

Next entry in two weeks.

Follow Along

This is part of the Build Log — a regular series documenting how we're building MCP Analytics.

• Try MCP Analytics — upload a CSV, get a free report
• Follow on LinkedIn for weekly updates
• Next entry: Log #2 — what happened in weeks 1-2