- Reliable AI research requires clear sources, approved rules, and traceable inputs.
- Missing evidence and unfinished work must remain visible.
- Corrections must become procedures to improve future research.
We built our AI-enabled research process for a fairly ordinary reason: consistency. Running it taught us that consistency was only the beginning.
The harder task was preserving all the small distinctions that make a piece of research trustworthy enough to inspect.
Read one company in March and another in September and it is surprisingly easy to end up with two impressions rather than two comparisons. The questions drift. The emphasis changes. What one company taught you affects what you notice in the next.
So, we formalized the research process. Evidence from filings would remain separate from calculations. Calculations would follow defined rules. Investment judgement would remain identifiable as judgement. Every company would pass through the same questions in the same order.
That was the design. Then we started running it.
The experience is still modest: one quarterly cycle across roughly twenty companies. But what surprised me was that consistency was not the hard part. Separating evidence, calculation and judgement was necessary, but not sufficient.
The process also had to preserve things we had barely thought about: which source governed, whether a statement was provisional, whether an action had actually happened, who owned a piece of metadata, and whether unfinished work still looked unfinished.
Most of the failures were mundane. That made them more useful.
