The Layer Between

There is a layer of decision-making in financial markets that almost no existing tool was built to sit inside. Above it: institutional tooling, priced at thousands of dollars per seat, built for people whose entire job is full-time analysis. Below it: consumer tooling, built for people the system assumes will either trust an advisor or pick from a chart. The space between those two assumptions is enormous, and that is where almost every individual investor, every junior analyst, and every student fund actually operates.

I started Zarova in January 2025, and LibraNex inside it, because that layer is missing. I know it is missing because I spent the previous fall trying to teach forty people how to operate inside it, without a framework, with a model I built called Libra.

This is what Libra was, what it taught me, and why what came out of it is the company I am now building.

October 2024

I had been Vice President of the Penn State Harrisburg Finance Club since April. The student fund was sitting at around thirty-five thousand dollars. That is not a number that intimidates anyone, but it was real money the club had earned the right to manage, and every analyst pitch that resulted in a deployment was a decision that mattered to people who had trusted us with it.

We had a structural problem. The fund was too small to take meaningful single-stock positions without concentration risk we could not justify, so the club stayed in ETFs. ETFs gave us diversification with exposure. They also gave us a problem: no one had taught my forty incoming analysts how to evaluate them.

Equities have decades of accumulated method. Discounted cash flow models, comparable company analysis, sum-of-parts breakdowns, ratio decomposition. A junior analyst can walk into a room of seniors and defend an equities pitch because the apparatus for the defence exists, and everyone in the room has been trained inside it. ETFs have none of that. The analysis you would find in a major bank's research note or a finance textbook, for an ETF, amounts to "look at the expense ratio, look at the holdings, hope for the best."

I had no time to teach forty people something I did not have a framework for. I had a term ahead of me, an analyst program that needed to produce pitches, and a fund that could not afford the cost of bad ones. So I sat down in October and built the framework instead of teaching it.

What Libra actually was

Libra was an ETF comparison framework. Not a screener, not a backtester, not a recommendation engine. A normalization layer.

The structure was the same for every investment round, regardless of sector. An analyst working a round configured the model by populating it with the candidate ETFs that round was evaluating, plus the relevant benchmark (the S&P 500 by default, but exchangeable for a sector benchmark or any reference index the round called for). The model then forced every candidate through the same set of structural metrics: number of holdings, classification, standard deviation, beta, price-to-earnings ratio, dividend yield, expense ratio, then year-by-year returns over the relevant window with an average computed at the end. Each candidate had its own dedicated sheet, laying out the same metrics side-by-side against the benchmark, plus the top ten holdings of each with weight, longer-window return, and one-year return.

Above the comparison sheets sat what I called the Simulator Dashboard. An analyst could plug in a current share price, a share volume, a capital amount, and the model would project annual expense, effective dividend yield, expected average return, and net profit against a benchmark-deployed equivalent of the same capital. The question Libra forced an analyst to answer was not "is this ETF good." It was a sharper one: "is this a better use of capital than the benchmark, on a normalized basis, given what we can measure."

The version of Libra I still have on my drive is configured for the energy round we ran that fall (XLE, VDE, IYE against SPY), because each round produced a saved instantiation of the framework with that round's candidates in it. But Libra was never an energy-ETF tool. It was the comparison architecture itself. The energy version is just what the architecture looked like the day we pointed it at energy.

The model was nothing technically impressive. A handful of formulas, some colored input cells, a structure. The reason it worked was not the math. The reason it worked was that it standardized the comparison. Forty analysts who used Libra were forced to look at the same things in the same order, in the same units, against the same benchmark, before they were allowed to form an opinion. They could disagree about the thesis. They could not disagree about the data, because they were all working from a shared structure.

It compressed a process that would otherwise have taken about four weeks of one-on-one hand-holding into something closer to a week and a half of structured analyst work. It scaled me across forty people without me having to be in every room. The pitches it informed were defensible, because each one rested on a normalized base layer before the argument began. One of the calls it informed, SOXX, I worked through with an analyst and pitched myself. Through the rest of the term we ran four or five investment rounds. By May 2025, when I graduated, the fund sat at around forty-four thousand dollars. The dollar numbers are small. The arc is the point.

What Libra revealed

If the story ended at the fund, this would be a student-club anecdote. The reason it did not end there is that the longer I worked with Libra, the more clearly I saw that the value of the model was not in any one calculation it performed. The value was in the structure it imposed on the question.

Libra was not doing anything sophisticated. It was pulling data that already existed, in places anyone with an internet connection could find, and laying it out in a way that made the decision legible. There was no proprietary signal. No clever algorithm. The reason it worked was that the data existed everywhere and nowhere usefully.

You could find every piece of information Libra used scattered across five free tools, three subscription tools, two PDFs, and a handful of broker pages. Bloomberg had it, but Bloomberg costs more per year than the entire fund had to invest. TradingView had charts but not the comparison logic. Yahoo Finance had data but no structure. The professional tooling assumes you have a professional process. The retail tooling assumes you do not need one. The space in between is the layer where the actual question lives.

I started seeing the same pattern outside the student fund as soon as I started looking for it. Friends asking me how to think about a stock they had seen on Twitter. Family members trying to evaluate whether an advisor's recommendation made sense for their situation. Retail investors on forums building elaborate arguments for positions on the basis of a single chart and a feeling. None of them lacked intelligence. They lacked structure. They were being asked to make decisions with the same information professionals had, but without any of the apparatus that turns information into a decision.

Libra was a tiny instance of a much larger missing layer.

The layer, named

Here is how I would describe it now, a year and a half later, after sitting with it.

There is a kind of decision in financial markets that the existing tooling skips. It is not the decision to commit. It is the decision to commit *time*. Someone has an idea, or a question, or a candidate. They have a few hours. They want to figure out whether the idea is worth more hours, before they spend them.

That is the first pass. It is what my student analysts needed before they could pitch. It is what a retail investor needs before they read a 10-K. It is what an associate at a fund needs before they pull up the full model. The first pass should be fast, structured, comparative, honest about what is unknown, and explicitly not a recommendation. Its job is to escalate the right questions to deeper work and drop the wrong ones quickly.

The institutional layer skips it because the institutional layer assumes the user already lives inside an analytical process. The consumer layer skips it because the consumer layer assumes the user wants to be told what to do. The first pass is where most people actually start, and almost no tool was built to live there.

That is the layer LibraNex is being built for.

What LibraNex is

LibraNex is a multi-asset analysis platform across equities, ETFs, crypto, and commodities, designed to make the first pass faster, cleaner, and easier to share. You search. You compare across asset classes on a normalized basis. You see what is known, what is not, and what is worth pursuing. You generate a structured report if you need to send your thinking to someone else, or come back to it later.

What LibraNex is not, and will never be, is a recommendation engine. It does not tell you what to buy. It does not tell you what to sell. It does not pretend to know what the market will do. It is a tool for organizing the questions you already have into a form where you can answer them yourself, or escalate them to someone who can.

That distinction matters to me. The version of this product that tells you what to do already exists in a hundred apps. The version that respects the user's intelligence enough to give them the structure and then get out of the way mostly does not. The forty analysts I taught with Libra did not need to be told what to pitch. They needed to be given the scaffolding inside which their own thinking could become rigorous. That is the design principle, and it has not changed.

What Zarova is

I coined Zarova in January 2025. By then Libra had evolved into LibraNex in early form, and what had started as a single spreadsheet for a single fund had become something that needed a company around it. Zarova was the structure I built to hold it.

The thesis Zarova operates on is the one Libra clarified. Across most domains where people make decisions, the *tools* have grown faster than the *systems around the tools*. We have more information than ever, and less structure within which to use it. The result is complexity that does not compound into understanding. Zarova exists to build the systems that close that gap. LibraNex is the first one, because the gap is most expensive in finance, where a bad decision compounds and the tools handed to retail investors are the least proportionate to the decisions they support. The operating philosophy generalizes. First principles, clarity over surface complexity, long-term intent. A system you can understand is a system you can trust, and a system you can trust is one you can actually use.

A month after Zarova first launched publicly in early 2025, I started getting unsolicited LinkedIn messages from working bankers, including an analyst at J.P. Morgan in India, asking how they could be involved. People with no incentive to flatter me, reaching out because what they saw resonated with something they had noticed too. That was the moment I knew the thesis was not only mine.

What comes next

LibraNex enters controlled beta in June 2026. Ten people, in person, finance-literate, with permission to break things. After that, a wider beta. After that, a paid product. After that, the next system in the Zarova ecosystem, built on the same identity layer, the same architectural philosophy, the same principle of clarity over noise.

Libra is still on my drive. I open it sometimes. It is a handful of formulas, some colored cells, a normalized comparison structure, a simulator that asks whether the capital is better deployed against the benchmark or against the candidate. It is not a sophisticated piece of software. It worked because the gap it sat in was real. LibraNex exists because that gap is real far beyond a student fund. Zarova exists because there is more than one gap of that shape, and the company I want to build is the one that closes them one at a time, in order, with the same discipline each time.

Libra was mine. LibraNex is the version of it the world can use. Zarova is the structure that ensures the next one will get built too.

-Arjun