spent 11+ hours today deep in prediction market resolutions, UMAs, and venue rule differences for @uselotusai . came away with the same conclusion: the hardest part of prediction market infrastructure is not finding markets that look the same, it’s knowing when they are actually safe to treat as the same market from the outside, cross-venue prediction market infra sounds simple - same event - same headline - same yes/no outcome should be easy to connect, right? not really because similarity is where the trap is a market on @Polymarket and a market on another venue can point to the same real-world event and still carry enough differences in wording, interpretation, or resolution logic to break your assumptions and once your assumptions break, your routing breaks with it that is how you end up with hidden risk, bad abstractions, and failing routes pushed downstream to traders this is also why a lot of builders stay on the surface the UI is easier the dashboard is easier the analytics layer is easier the hard part is doing the slow work underneath: reading the resolution criteria studying how venues define outcomes understanding when two markets are economically similar but not safely interchangeable that is the layer we are building through at lotus because traders should not have to manually inspect venue logic just to get better execution builders should not have to rebuild market interpretation from scratch every time they want to integrate prediction markets and institutions definitely will not scale into this category if the infrastructure underneath it is still fragile most people see similar markets we see the hidden differences that decide whether routing is safe or broken that gap is a big part of what @uselotusai is built to solve.

Imagine loosing because you used an aggregator. A trader doesnt want just the best price, anyone can route to that. This article details the biggest gap you probably missed out when executing that trade.

VCs would write backing ambitious founders building in new categories and at the earliest stage and end up backing founders building the most generic solutions that aren’t scalabler, and leaving the ambitious founders saying it’s too early😅😂. Real life story btw

Hot take: Prediction market infrastructure shouldn’t be priced on freemium or tier-based plans. Especially when your infra isn’t giving users an unfair advantage. It can be gated because not all edge needs to be commoditized, but if you can’t make it free to use while capturing value without pushing the cost onto integrators and their users, what’s the worth?

True, but in prediction markets fragmentation hits makers hard. Traders also get the short end of the stick. Makers find it harder to quote due to adverse selection and the inability to hedge the same position. Informed flow is a problem on its own, and resolution incompatibility makes cross-venue hedging another hurdle. RFQs won’t be efficient for single-venue execution under these constraints, but they are a big unlock for cross-venue trading if markets are resolution-compatible. Makers would then quote tighter because they can hedge their positions.

The reality of building in prediction markets for most teams is the way inability monetize their product to earn enough without increasing churn. You charge a fee, retail traders default to using the venues themselves and the reality is you cant make enough to scale from builders fees or so you now you hit a roadblock. Either sacrifice revenue for you to bootstrap users or even depend directly on what venues offer. While building @uselotusxyz , we found a way through this and now we can easily say, that roadblock is fully resolved. If you are a team currently dealing with this. Send over a DM we got you

Glad to say our thesis papers are now published to the public There’s so much alpha locked in each paper showing how much it takes to solve some of the hardest pains current prediction market infrastructure face and how we solved them. Read that and more: uselotus.xyz/thesis