The useful signal is buried.
Odds, schedules, game events, prediction markets, and trader activity live in different places. Users spend too much time checking and too little time understanding what changed.
About Kam AI
Better sports research starts with a clearer view.
Kam AI helps users find meaningful market signals, ask natural-language follow-ups, and keep games, traders, watchlists, and portfolio decisions in a focused iPhone flow or a persistent iPad and Mac workspace.
It does not promise winners. It helps users spend less time moving between disconnected sources and more time understanding what changed, what may be stale, and what deserves another look.
- Product
- Sports and prediction-market research
- Platforms
- iOS, iPadOS, and macOS
- Approach
- Founder-led and source-grounded
The problems
Market research asks users to do too much assembly.
A number without context is easy to misread.
A price can move because of new information, market disagreement, or stale data. Kam keeps freshness, source coverage, and missing context close to the answer.
Generic chat loses the object that matters.
Users should be able to open a game, trader, watchlist item, or portfolio thesis and ask a natural follow-up without rebuilding the question from scratch.
Research disappears after the decision.
Watchlists and portfolio notes give the original reasoning a permanent home, so users can review what changed and learn after the result.
How Kam helps
One continuous decision loop.
The product connects four simple jobs. Each one stays tied to the same underlying game, market, trader, or saved thesis.
- 01
See
Start with current signals, games, markets, and the items already saved to your workspace.
- 02
Ask
Use natural language to investigate a move, compare sources, or follow up on the selected object.
- 03
Track
Keep games and markets in a Watchlist, and preserve portfolio theses instead of relying on memory.
- 04
Review
Return to the evidence, result, and original reasoning before making the next decision.
How Kam is different
A research layer between the board and the decision.
Sportsbooks are built to show their markets and accept wagers. Prediction markets express tradable crowd probabilities. Odds screens optimize comparison. General chat optimizes conversation. Kam focuses on the work between them: detecting, investigating, organizing, and reviewing.
Across market types
Sportsbook lines and Polymarket probabilities can be reviewed together without pretending they are the same product or price.
Across product surfaces
Summary, Chat, Game details, Watchlist, and Portfolio keep the selected game or research object in context.
Across time
The question is not only “what is the price now?” but also “what changed, what did I believe, and what can I review later?”
The honest limits
Better context does not remove uncertainty.
Kam does not guarantee winners, place wagers, execute prediction-market trades, or replace your judgment. Market prices can move quickly and source coverage can be delayed or unavailable.
The native product launches on iOS, iPadOS, and macOS through Apple distribution. The website offers a bounded preview, not the persistent standalone workspace.
When Kam cannot support a market claim with the current materialized read and its receipts, the product should say so rather than fill the gap with a confident guess.
Founder & CEO
Building the product from signal to decision.
John Yu is building Kam AI to make fragmented market research feel coherent, trustworthy, and easier to revisit.
He works across the native iOS, iPadOS, and macOS experiences, AI workflows, market-data systems, documentation, and public preview so each part of the product supports the same decision loop.
Kam grew from a straightforward belief: users should not need a wall of tabs or a generic chatbot to understand the market. The product should surface what matters, explain its limits, and help the user keep a record of the decision.
John remains directly involved in product support and user feedback. If something is confusing, missing, or could make the research workflow more useful, contact him directly.
Kam AI is being built for Apple devices.
Explore both native workflows, or request early access to Kam on iOS, iPadOS, and macOS.