The Alchemy of Finance

There’s a sort of open secret in finance: we’re incredible at collecting data and terrible at using it. Everyone loves to say, “data is the lifeblood of the business.” Is it? It mostly just sits there, gushing aimlessly. There’s more data than ever before (prices, trades, alternative signals) but very few people seem to know what to actually do with it. The AI-powered solutions are starting to look like the silver bullet.

Let’s be honest: everyone has long known the real prize was never about hoarding terabytes of data. It’s about finding the signal in the noise: the one insight that actually changes your mind, your position, or your P&L. The trouble is, finance runs on duct-taped infrastructure. Jupyter notebooks and Excel spreadsheets were fine when data was measured in gigabytes but can’t scale up elegantly into galaxies. I doubt a typical investment analyst uses even 1% of the data they have (and pay handsomely for).

The advent of LLMs showed us information could suddenly be harnessed at scale. Consuming the entire internet worth of information made critical queries like “please put together an agenda for a one-day visit to Budapest” suddenly possible. LLMs make information processing look almost trivial.

That’s why everyone is now frantically racing to reinvent themselves. They’re busy building the very infrastructure that Reflexivity already has: engines that don’t just collect data, but use it to think and generate insights. It’s no longer enough to sell ingredients; the customer wants a platform to turn them into a meal.

And the ingredients aren’t just unstructured text. Buried in internal .csv files are billions of untouched time series, collecting dust and opportunity cost. Reflexivity operates in a new paradigm where data pipelines, Knowledge Graph-linked datasets, analytics engines, and AI models all work together: generating signals, forecasts, and narratives in real time. We don’t just use data. We own the machinery of insight itself and use it to power our clients’ idea generation and risk management.

For decades, having access to data was itself valuable. But once data volume exploded beyond what humans can process, raw data stopped being a scarce resource. Insight is what's scarce. The ability to connect dots, contextualize, and anticipate what happens next. This will ultimately make truly valuable data actually more valuable - premium content will finally earn a premium - because users will have the infrastructure to use it.

We're moving from "here's the data, good luck" to "here are the insights, here's the evidence." It's synthesis-as-a-service rather than information-as-a-service.

The golden age of (using) data is just beginning.