What if the cost of curiosity for investors went to zero?

At Reflexivity, we’ve built the most versatile AI-powered investment analytics engine on the planet. Its core reasoning capability emerges from the orchestration of large language models (LLMs) meticulously fine-tuned for financial analysis. These aren’t generic chatbots. They’re precision tools, calibrated to interpret probabilistic market structures, simulate macro shocks, run factor decompositions, and more — all in natural language.

This cognitive layer is supercharged by a proprietary Knowledge Graph, which fuses disparate streams of institutional-grade data: real-time pricing, macroeconomic indicators, earnings transcripts, even geopolitical risk overlays. And atop it all sits Alfred — our AI agent, equally fluent in Bayesian inference and plain English.

When users first interact with Alfred, they’re spellbound. “Wait, it can backtest my trading strategy?” Yes. “Run a Monte Carlo simulation on my portfolio, with correlated drawdowns and nonlinear tail risks?” Absolutely. What starts as skepticism morphs into awe.

And then… silence.

After the first flurry of questions, users stop. They didn’t hit a limit. They ran out of questions.

It turns out, few investors have a mental inventory of complex analytical queries ready to go. After all, for decades, financial reasoning has been mediated by an older generation of tools (think spreadsheets) or arcane statistical libraries. Creativity was constrained by the enormous activation energy required to answer a single question. Why run a vector autoregression when a rule of thumb will do?

This is the paradox of Alfred: its analytical power is immense, and its adoption is bounded not by latency or compute — but by imagination.

Why Rules of Thumb Were Born

Rules like “don’t fight the Fed” or “golden crosses are bullish” weren’t born because they were right — they were born because they were cheap. In a world where rigorous empirical analysis required weeks of work, it was rational to rely on heuristics.

But Reflexivity changes the economics of inquiry. Now, a portfolio stress test costs seconds, not hours. Structural breaks in correlation matrices? Instant. Sentiment-weighted earnings signal from last night’s transcripts? A prompt away.

In this world, failing to ask questions is no longer an inconvenience. It’s negligence.

User Prompting

This is why we’re focused not just on prompting Alfred — but on prompting users, too.

The first step is to make it very easy for our users to think in terms of testable hypotheses, not just gut feel. We can weave these into workflows, interfaces, and nudges. We bring AI support where it’s most needed rather than wait for users to seek it out. But more than that, we’re training investors to reimagine the cost of curiosity.

You just read a breaking news story: the U.S. is slapping heavy tariffs on imported steel. A trader’s first question used to be: Where’s my analyst/economist? Instead, integrated directly with institutional data providers like LSEG, Nasdaq, S&P Global etc., Reflexivity can help:

• “Which of my holdings are most exposed to steel prices?”
• “Who are the top three U.S. importers of steel today?”
• “What is the marginal effect of a 10% increase in steel prices on each stock’s expected return?”

Each question is a doorway into a previously time-consuming analysis. Now, it’s zero-friction.

Push Meets Pull: Content with Teeth

Reflexivity doesn’t just wait for the user to ask the right questions. It pushes content — dynamic, insightful, contextual — and pairs it with reasoning.

That’s where our dual engines come in: Insights (push) and Alfred (pull). The insight pipeline surfaces relevant macro shifts, market anomalies, and policy events. Alfred then becomes the interactive lens through which users can interrogate them. The synergy creates a virtuous loop: content invites curiosity; reasoning refines insight.

Clients love that Alfred answers their questions. But they value even more that Alfred prompts the questions they should have asked.

The Road Ahead

We’ve already started building the connective tissue: tying together passive insights with active reasoning. The roadmap includes features that turn every insight into an invitation — click here to simulate, backtest, or scenario-stress the impact.

In time, this will become second nature. But for now, we are bridging an age-old gap: between the human instinct to simplify, and the machine’s ability to amplify.

The future of investing may belong to those who ask better questions.

And the first one is: Why didn’t we do this sooner?