A few months ago, What started as a single page of AI prompts, rules, and rough ideas has grown into a 30,000-plus-word investment research system, built through months of testing, rewriting, challenging assumptions, and applying it to real companies. Originally, I was looking for one specific type of opportunity: smaller, underfollowed companies solving important bottlenecks before the broader market fully recognised them. As I kept building and testing it, I realised I did not want every company forced into that same mould. I wanted a system that could analyse almost any public company on its own terms, whether it was an early-stage growth company, an established compounder, a turnaround, a defence contractor, a semiconductor company, or something completely different. What I call a framework is basically a structured stock-research system. It is not a screener that simply filters companies using a few financial ratios. Once I choose a company, the framework guides the entire research process: what evidence needs to be found, which sources deserve the most weight, what assumptions need to be challenged, how the business and competition should be analysed, how dilution and valuation should be modelled, what the strongest bull and bear cases are, and what evidence would improve or weaken the final ratings. I used multiple AI models as research partners and critics throughout the process. I would build something, have another model attack it, take the strongest criticisms back into the framework, test them, reject what did not hold up, keep what improved it, and repeat that cycle over and over. rewriting, auditing, and applying it to real companies. The framework tries to separate three questions that often get mixed together: How strong is the actual company? How attractive is the stock at its current price? What evidence would improve or weaken the rating? This is the first public WICK Research deep dive using that system, focused on Rocket Lab (RKLB). It covers the business, customer demand, execution, competition, capital structure, dilution, the Iridium transaction, valuation, scenarios, professional bull cases, risks, and the evidence that could change the ratings. The opening dashboard gives the quick version, but the full underwriting is underneath it. The sections can be opened individually for anyone who wants to dig deeper into the research, assumptions, numbers, sources, and reasoning. I also want to be clear that this is still very early. The framework and public format are not finished products. Plenty could still change as I test the system on more companies, compare the ratings with real outcomes, and learn where the process is too strict, too loose, confusing, or simply wrong. That is a major reason I’m sharing it now. I’m looking for honest feedback on the research, the ratings, the design, what feels unclear, what feels unnecessary, and what may still be missing. Full RKLB deep dive: https://rklb-wick-research.chooch81.chatgpt.site Independent research. No compensation was received from the company or any third party. The analysis is frozen at the stated price and date and is not personalised advice.
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