Why "good enough" accuracy isn't good enough - and the engineering philosophy that drives Raylu

The Triage Problem

Much of the skill of being an elite investor comes down to one thing: triaging time across deal opportunities.

The reality of investing is that there's always more questions to ask, more diligence to do, more conviction to build. But you face two hard constraints: limited human capital to devote to any single deal, and pressure to move fast enough to win the deals that matter.

AI represents an incredible opportunity here. Imagine a system that could red-light and green-light deals - helping you triage where human focus should go. In theory, it's the silver bullet for this age-old problem.

In practice, there's one thing standing in the way: hallucination.

The Miss That Haunts Every Fund

Nothing makes an investment committee angrier than a "miss."

A miss is passing on a deal for the wrong reasons. Not paying attention when you should have. Or simply never seeing a company that a competitor saw, won, and is now watching take off.

All great investors are hyper-competitive. They're painfully conscious that truly exceptional deal opportunities are rare. And they're deeply unhappy when a GP they know, often all too well, lands the one that got away.

This aversion to the miss permeates fund culture. There's no greater mistake. It's the thing that will make your boss mad.

But here's the question that keeps AI vendors up at night: what happens when AI causes the miss?

Who's to blame? In reality, it's the associate or VP whose name was attached to the deal, regardless of how little they intervened compared to the AI system. The human takes the fall.

The True Cost of Hallucination: Irrelevance

So what is the real cost of hallucination in financial AI?

Irrelevance.

The second any associate or VP sees a hallucination from their AI system, trust is broken. The fear that their neck is on the line for these mistakes becomes visceral and real.

And once that trust breaks, a question emerges that kills the entire value proposition: What good is AI doing the work if I have to redo all that work by hand to verify it?

This is why so many AI tools in finance end up as expensive shelfware. They demo well. They impress in the pilot. And then they sit unused, because no one is willing to stake their reputation on outputs they can't trust.

How We Think About This at Raylu

At Raylu, we've internalized a simple truth: in financial AI, accuracy isn't a feature. It's the product.

This shapes everything we build. Here are the three core tenets we follow:

1. Being Right Comes Above All Else

Engineering always involves tradeoffs between competing forces. In AI, those forces are typically cost, speed, and accuracy.

Our philosophy: it doesn't matter how expensive or how slow a system is, build it for accuracy first.

This has massive implications for how we operate.

At the product level, it means our systems aren't instantaneous. We design interaction patterns around the reality that agents can take minutes to complete their work. We've invested heavily in helping users queue and parallelize tasks, view intermediary results, and let Raylu work in the background while they do other things.

At the business level, it means we have real constraints on pricing flexibility. We leverage the largest frontier models, at their maximum capacity, multiple times over to answer even a single question. It's extremely costly. When customers compare us to non-AI tools and expect non-AI prices, we often can't serve that price point. It's a structural reality of building AI that actually works.

These are significant sacrifices. But they're necessary sacrifices for AI in finance.

2. Better to Not Answer Than Be Wrong

Every AI system in Raylu has purpose-built escape hatches that allow the model to opt out of answering when confidence is low.

This is harder than it sounds.

By the nature of how LLMs are trained, through reinforcement learning that rewards helpful responses, they are fundamentally people-pleasers. They want to answer, no matter what. Getting an LLM to say "I don't know" is genuinely difficult.

LLMs are also, by the nature of how transformers work, quite poor at grading their own confidence. They struggle with calibration and granularity.

We often spend 2-3x the engineering effort getting models to effectively decline to respond than we do getting them to give great responses. It's deeply counterintuitive. But it's essential for fighting hallucination.

3. Show Every Inch of Your Work

The final piece of our approach is reducing the human workload required to verify AI outputs.

Every piece of information in Raylu is tied to two things: reasoning and citations.

Reasoning: We force our AI agents to provide detailed, step-by-step analysis of how they reached a conclusion. A one-word answer like "yes" will have several hundred words of analysis explaining exactly how the system got there.

This pattern is expensive in multiple ways. Model output is the majority of model cost, and we have to make this reasoning consumable and interactive in every part of our product. But it's worth it.

Citations: We tie reasoning to the underlying source documents the AI used to reach its conclusion.

Citations have become commonplace in AI tools. But good citations are still rare.

It's not helpful to show a user four citations, each pointing to a thousand-word page of dense information, and say "here's what you need to read to verify my work." At that point, the user might as well have answered the question themselves.

Doing citations right, where every sentence in the reasoning is tied to a specific sentence in underlying source material, is genuinely hard and genuinely expensive. The LLM has to do far more work to achieve this level of traceability.

But that's exactly what we do at Raylu. The hard things matter.

A Note to Teams Building Financial AI

We're seeing a wave of new teams building AI for finance. We love it, the more innovation in this space, the better.

But it's critical that these teams internalize just how much higher the bar for accuracy is in finance than in almost any other domain.

In most applications, a 90% accurate AI is impressive. In finance, a 90% accurate AI is dangerous. The 10% of errors will erode trust completely, and trust, once lost, doesn't come back.

Building for accuracy requires discipline. It requires active sacrifice. You will ship slower. You will charge more. You will lose deals to competitors who cut corners.

But if accuracy isn't part of your core DNA from day one, you'll build something that demos well and dies in production.

At Raylu, we made this choice early. It's shaped every product decision, every architectural tradeoff, every pricing conversation we've ever had.

And it's why funds managing over $500 billion in AUM trust us with their most sensitive workflows.