I built NorrisAI AlphaLens because the research tools that actually work have always been locked behind institutional paywalls. Independent investors deserve the same depth. I spent 40 years in technology learning how to build things that last — and the last few years watching AI become the most significant shift I've seen in that entire time.
I got my start in technology before most people had heard of a personal computer. My roots go back to MS-DOS, building machines from components, and learning how software actually works at the hardware level — not from a tutorial, but from necessity. Through the 1980s and 1990s I watched the PC revolution unfold from the inside, and developed deep expertise in Windows systems through the 2000s.
Over the decades I've worked in local SEO, digital marketing, and software development — always as a builder rather than a bystander. I've written trading bots, automated content pipelines, and web applications. I understand servers, databases, APIs, and the unglamorous work of keeping systems running.
I took a decade away from professional work to care for both of my parents through serious illness — my father for eight years, my mother for two. It was the most important work I've ever done, and it taught me something I carry into everything else: one day at a time is not a cliché. It's the only way hard things actually get done.
I've been an independent investor for years — and I've always been frustrated by the same thing. The tools that retail investors have access to (Yahoo Finance, basic screeners, social media sentiment) are fundamentally different from what institutional analysts use. The research depth, the frameworks, the structured thinking — it's all locked away at Bloomberg Terminal prices or behind hedge fund walls.
When AI reached the point where it could genuinely reason about financial data — not just retrieve it, but synthesize it the way an analyst would — I saw the opportunity clearly. I could build the tool I always wished existed. Fifteen institutional-grade research frameworks, applied to any stock, in plain English, for $39 a month.
NorrisAI AlphaLens is built on Advanced AI, which I chose because Anthropic's approach to building AI that reasons carefully and communicates clearly is exactly what financial analysis demands. I run this as a solo operation out of Memphis, Tennessee — which means every decision is mine, the feedback loop is tight, and I ship fast.
Every framework runs on the same rule: live data or nothing. Before any framework touches a ticker, it pulls the company's current SEC filings, live price data, and current news — not what an AI model happened to learn during training. If a number isn't available from a live source, the framework says so instead of guessing.
That distinction matters more than it sounds. A stock's price, filings, and news are only useful if they're current — and AI training data has a cutoff date, sometimes a year or more old by the time you're reading this. I built AlphaLens specifically so that gap doesn't become your problem. For a concrete example of why this matters, see the case study on SanDisk's 2025 spinoff — a stock whose entire modern identity postdates most AI models' training data, or the SpaceX post-IPO case study for the same problem applied to a newly public company.
No fabricated price targets. No invented earnings. If the data isn't there, the framework tells you that directly instead of filling the gap with something that sounds plausible. The full research process — the same one built into every framework — is laid out step by step in the Complete Stock Research Checklist.
I work a demanding day job and build NorrisAI in the evenings and on weekends. That's not a limitation — it's the context that keeps me honest about what actually matters. Every feature I ship has to earn its place because my time is finite.
I follow AI development closely, including Anthropic's research on AI safety and consciousness. I believe we're at an inflection point — the most significant technological shift of my lifetime — and I'd rather be building at the frontier than watching from the sidelines.
I write up what I've learned as I build — the concepts, the process, the mistakes — in the free investing guides. Most of it started as notes to myself before it became something worth sharing.
One day at a time. It's how I got through a decade of caregiving. It's how I ship software after a full day of work. And it's the right frame for building anything worth building: show up, do the work, and let the days compound.
Run any US stock through the same frameworks I built for myself. No card required with a trial code.
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