If you've spent any time around prediction market founders, you've heard the pitch: "We're building the Bloomberg Terminal for prediction markets." It's a useful analogy. Bloomberg gives professional traders a unified view of market data, news, analytics, and communication tools. Prediction market traders need the same thing. We use the comparison ourselves.
But the analogy breaks down in ways that matter for how you actually build the product. We learned this the hard way in the first few months of building Previa, and the lessons changed what we prioritized.
What Bloomberg actually does
Bloomberg's genius isn't the data. It's the workflow. A fixed income trader doesn't use Bloomberg because it has bond prices — every data vendor has bond prices. They use it because the entire workflow of finding bonds, analyzing yield curves, reading relevant research, monitoring news, and executing trades happens in one environment.
The terminal isn't a dashboard. It's an operating system for a specific kind of professional work.
What prediction market traders actually do
When we started talking to traders — and we talked to a lot of them, from people running $500 on Kalshi to a fund manager allocating seven figures across platforms — we noticed the workflow wasn't what we expected.
We assumed the primary pain point was "I need better analytics on individual markets." That's true, but it's not the main thing.
The real pain point is the cross-referencing. A prediction market trader's job is fundamentally about connecting dots across disparate information streams. Did the GDP report affect the recession markets? Does this Supreme Court ruling change the sports betting regulation contracts? What does the latest polling data mean for the state-level election markets?
No existing tool does this connection work. Traders do it manually, across multiple browser tabs, every single day. That's the actual Bloomberg-shaped hole.
What we built (and what we didn't)
The first version of Previa was too focused on market data presentation. Clean price charts, nice volume displays, pretty platform badges. It looked great. But we were essentially building a slightly better version of what Kalshi and Polymarket already show.
The turning point was when we started building the AI intelligence layer — specifically, the news-to-market mapping pipeline and confidence scoring engine.
Here's the difference: instead of showing a trader that "the Fed rate market moved 3 cents," Previa shows them why it moved, which other markets were affected by the same catalyst, and how our AI assesses the probability shift relative to what the market is pricing.
That's the difference between showing data and making it useful.
What the prediction market terminal actually needs
After six months of building, here's our take on what this product category requires:
Cross-platform aggregation that doesn't feel like aggregation. Traders shouldn't have to think about which platform a market is on. Kalshi and Polymarket contracts should live in one unified feed, filterable by what matters: category, volume, time to expiry, AI confidence score.
AI that explains itself. Confidence scores are only useful if you can see the reasoning. A number without context is noise. Every Previa score comes with a breakdown: how much weight came from market price vs. news signals vs. sentiment vs. historical patterns, plus a plain-English summary of the AI's assessment.
Alerts that are actually smart. "Price crossed $0.65" is a dumb alert. "Price crossed $0.65 following a high-impact news catalyst that shifted our confidence score by 12 points" is a smart alert. The difference is whether the alert tells you something you can act on.
Portfolio intelligence, not just portfolio tracking. Showing a trader their P&L is table stakes. Showing them that three of their positions are exposed to the same macroeconomic risk factor, and that a news story just broke that affects all three — that's intelligence.
Where we are today
Previa's core product is live in early access. The market scanner covers every active contract on Kalshi and Polymarket. The AI pipeline processes news in real time, maps it to markets, and generates confidence scores. Alerts fire when positions or watchlist items are affected by material information shifts.
We're not done. The social intelligence layer — monitoring Twitter, Reddit, and Telegram for prediction market signal — is in development. The portfolio tracking integration with Kalshi's API and Polymarket's on-chain data is rolling out. And we have plans for AI-generated deep research reports that go beyond quick insights into thorough, citable analysis of specific markets and themes.
The Bloomberg Terminal took decades to become the Bloomberg Terminal. We're not naive about the scope of what we're building. But the prediction market industry is growing at a pace that makes the timeline much shorter. $44 billion in 2025 volume. Analysts project $222 billion in 2026. The traders putting capital into these markets today need better tools today, not in ten years.
That's what we're building.