The day I stopped pretending we could “hack together” our own data layer was the day a test user almost got liquidated on a completely normal market move.
We’re building a product on a Bitcoin L2 that’s supposed to feel like a smart savings account for people who live mostly in BTC, but want more than just holding and hoping. Think structured yield, some RWA exposure, a bit of cross-chain diversification, all wrapped in a simple interface.
In our pitch deck, it sounded clean. In code, it was chaos.
We had positions that depended on BTC prices from multiple venues. We had yield products whose returns were linked to short-term rates and tokenised treasuries. We had hedging logic that checked volatility and funding. And we had the classic early-stage mistake: a tiny internal “oracle” system glued together with a couple of APIs and way too much optimism.
It worked in demos. Everything works in demos.
Then one night during testing, liquidity on a smaller venue got thin, printed a silly wick, and our in-house feed didn’t handle it well. For a few minutes, our app thought BTC had moved way more than it actually had. One of our test accounts got flagged as undercollateralised and the system started preparing a liquidation.
We caught it before anything fired, but that was enough.
You can’t tell people “trust us with your savings” if your app nearly nukes them over a blip on a second-tier exchange. That was the moment I wrote a note to the team:
we are not building an oracle
we are building on top of one
So we started looking properly.
We knew the usual big names, but we had specific constraints. We live close to the Bitcoin ecosystem, not just EVM. We need feeds that work across multiple chains because our strategies touch BTC rails and other environments. And we know that, long term, we want to bring in more complex data than just spot prices: RWA valuations, rate curves, maybe even document-driven updates.
That’s how we ended up taking APRO seriously instead of just treating it as another ticker on a list.
On paper, APRO describes itself as a decentralised oracle network tailored for the Bitcoin ecosystem, with an architecture that combines off-chain processing and on-chain verification. In normal-people language, that means it acts like a specialised data plant: it chews through messy information out in the wild, does the heavy lifting off-chain where it’s cheaper and more flexible, and then writes a clean, verifiable result back to the chain your contract actually lives on. 
That hybrid setup solved two problems we were wrestling with.
First, speed and cost. You don’t want to run heavy aggregation and AI logic directly on-chain every time you need an update. APRO keeps the smart stuff off-chain, then anchors the result where our contracts can see it cheaply.
Second, reach. APRO already supports price feeds across a bunch of major networks and can publish to multiple chains, not just one. For a product like ours that sits on a Bitcoin L2 but cares about what’s happening on other chains too, that matters a lot. 
We started small.
We took our most basic need — a robust BTC/USD feed that doesn’t lose its mind when one venue goes weird — and wired it to APRO instead of our homemade pipeline. Immediately, our monitoring dashboards calmed down. Where our old system would flicker and spike, APRO’s feed felt more like a consensus than a single opinion.
Then we did the same for a few other assets that matter to our strategies. Over time, we replaced almost every direct “market API” call in our backend with calls to APRO-driven values.
The real turning point, though, wasn’t just about prices.
APRO’s whole pitch is that it’s not limited to simple spot feeds. Because it uses off-chain processing plus on-chain verification, it can support different data models — push for constant feeds like prices, pull for on-demand, heavier queries — and even pipe AI-processed information into the mix. That’s where my brain started spinning a bit faster. 
Our product roadmap includes tokenised short-term instruments and strategies that reference things like current yields, roll schedules, maybe even specific calendar events (rate decisions, maturity dates, that kind of thing). That’s exactly the kind of “rich but annoying” data that’s painful to manage internally and trivial to mess up if you rely on one brittle source.
On a whiteboard, we sketched out two versions of our future:
Version one: we keep bolting more custom logic onto our own data layer forever, adding new APIs every time we bolt on another asset type, patching around edge cases each time something breaks under load.
Version two: we treat APRO as the main bridge between our world and the outside world, and we focus on designing good products around the signals it provides.
Version two won very quickly.
The next thing we had to understand was AT, because that’s the token sitting at the centre of APRO’s network.
AT isn’t just a badge. It’s the fuel and the guardrail.
Projects that need data use AT to pay for or reserve access to the feeds and services they care about. At the same time, node operators and other participants in the network stake AT to run infrastructure and validate data. Doing good work earns them AT. Cutting corners or going malicious puts their stake at risk. 
That loop is what made me comfortable building something serious on top of APRO.
In our old setup, if a data provider messed up, there wasn’t much we could do beyond panic in support chats. With APRO, there’s at least a clear, economic connection between behaviour and consequences. People at the edges of the network are literally putting their AT on the line to keep the data clean.
Once we made the decision to integrate APRO deeply, a few things changed in how we run the product.
We rewrote parts of our risk engine to be “APRO-first.” Instead of asking “what does this one exchange say,” we ask “what does the APRO network say,” then apply our own cushions on top. We shifted monitoring to watch APRO feeds as primary and direct venue prices as sanity checks, not the other way around.
We also changed how we talk about risk to our users.
Before, we would hand-wave “market data from multiple sources” in our docs. Now we can actually say, here’s how reality gets into the system: through a decentralised oracle network that’s designed for the Bitcoin and multi-chain environment, with a token model that rewards people for being accurate and punishes them for being sloppy.
That doesn’t mean nothing can ever go wrong. But it does mean we’ve stopped pretending we can DIY our way through the hardest part of the stack.
On the AT side, we had to decide what role, if any, it should play in our own treasury.
We could have just been a customer — pay for what we use, move on. But that felt too shallow, given how much of our promise to users rests on this network doing its job.
So we allocated a small, deliberate portion of our treasury into AT and wrote down why: if our business depends on APRO for clean data, we should be aligned with the network’s long-term health, not treating it as a black box utility.
That decision quietly affects how we behave.
When APRO announces new chain integrations or more sophisticated oracle types aimed at RWAs and AI-driven apps, we pay attention because it opens up new product ideas for us and potentially more demand for the network overall. When they talk about governance or updates to how AT is used for fees and staking, we look at it not just as external news but as something that touches our risk model. 
From a pure numbers perspective, APRO and AT are still relatively young. The token has a capped supply of one billion, a few hundred million circulating, and has already had the usual wild swings that every new asset goes through. There’s real liquidity now on big exchanges, and daily volume is high enough that it’s clearly on a lot of radars. 
But the reason I’m comfortable writing about it like this has less to do with charts and more to do with how it fits into our daily reality.
On any given day, our users don’t know or care that we’re using APRO. They log in, see their BTC-denominated positions, maybe allocate into a structured product, maybe move some yield back to their main wallet. They don’t see AT. They don’t see feeds. They just see, “this app behaves sensibly even when the market is noisy.”
Inside the team, it’s different.
APRO is on our architecture diagrams. AT is on our treasury dashboard. When we test worst-case scenarios now, we don’t only simulate price moves. We simulate weird data days and ask, “How does the APRO layer react, and what do our contracts do on top of that?”
There’s a kind of layered trust built into that:
We trust APRO to do its job of turning chaos into cleaned-up facts.
We trust our own code to respect those facts sensibly.
We trust AT’s role in keeping the data layer honest over time.
If any of those layers fails, we don’t have an excuse. We chose them.
That’s what I mean when I say this is “organic” for us. APRO isn’t something we slapped on for marketing. It’s the answer to the uncomfortable question we hit during testing: who are we really relying on when our product decides what’s true.
If you strip our stack down to three sentences, it looks like this:
We live on a Bitcoin-centric L2, but we need eyes on the whole market.
We don’t want to build and maintain a bespoke oracle empire.
We’d rather plug into a network whose entire identity is “we obsess over data so you don’t have to.”
That network, for us, is APRO.
And AT is the proof that there are real incentives behind that promise, not just a pretty diagram in a whitepaper.
