Prediction markets had a year. $44 billion in trading volume in 2025, up from what felt like a novelty niche in mid-2024. Kalshi and Polymarket now sit at $11 billion and $15 billion valuations respectively. Robinhood, DraftKings, and Fanatics are all pushing into event contracts. The asset class isn't emerging anymore — it's here.
But if you trade these markets, you know the research experience is rough.
The daily grind of a prediction market trader
Here's what a typical morning looks like for someone actively trading Kalshi and Polymarket:
You check Kalshi's interface for updated odds on a handful of contracts you're watching. Then you flip to Polymarket for the same. The prices are slightly different, the categories don't map cleanly, and neither platform tells you why prices moved overnight.
So you open a news tab. Maybe two. You scan AP, Reuters, a politics newsletter, a couple of crypto feeds. Something happened at 2 AM — a policy announcement, maybe, or an earnings surprise — and you're trying to figure out which of your forty watched markets it actually affects. You're doing this manually, every single day.
If you're really diligent, you'll check Twitter for sentiment. Maybe a Telegram group. Maybe a subreddit.
By the time you've synthesized all of this into something actionable, the price has already moved. Someone who had better information — or faster information — got there first.
What exists today
We looked at every tool in the prediction market ecosystem before we started building Previa.
The analytics tools that exist are fragmented. Some cover Polymarket only. Some focus exclusively on price charts. Some track social mentions. None of them combine the full picture — news analysis, cross-platform market data, AI-generated insights, portfolio tracking, and alerts — into one place a trader can use every day.
It's not that people aren't trying. There are solid projects out there. But the space is in the "50 niche tools, zero platform" phase. It's a bit like crypto analytics before Dune and Nansen consolidated the data layer. There's data everywhere. Nobody's connecting it.
The intelligence layer this market needs
What prediction market traders need is straightforward: know what happened, know what it means for your positions, and know it faster than you would on your own.
That's what Previa does. We aggregate every active market from Kalshi and Polymarket into one feed. Our AI pipeline reads the news continuously — not a daily digest, but in real time — and maps articles to the markets they impact. We generate confidence scores that blend market price, news signals, sentiment, and historical patterns. And we alert you when something material shifts.
It's the difference between manually scanning six tabs every morning and opening one app that's already done the work.
Why now
Three things are converging:
The market is real. $44 billion in volume isn't speculative growth. Kalshi processed $4 billion through its exchange on election night alone. These are real traders deploying real capital, and they need real tools.
AI can finally do this well. In early 2025, LLMs weren't reliable enough for real-time financial intelligence. Today, the combination of better models, faster inference, and more sophisticated retrieval means AI can synthesize multiple information streams into genuinely useful market analysis. Not perfect — but useful, which is the bar that matters.
The platforms want this to exist. Both Kalshi and Polymarket run builder programs with grants and revenue sharing. They understand that better tooling means more trading volume, which means more platform revenue. The incentives are aligned.
What's next
We're building Previa in public, starting with a free market scanner that gives you every prediction market contract across platforms in one clean interface. The AI intelligence layer — confidence scores, news mapping, smart alerts — is rolling out to early access users.
If you're spending more time on research than on actual trading decisions, that's the problem we're solving.
We'll be writing more about how the AI pipeline works, how we think about confidence scoring, and what the prediction market landscape looks like from our side of the data.