Why now
Prediction markets are moving from novelty to market structure. They now sit at the intersection of macro, politics, crypto, weather, sports, litigation, private markets, and real-time sentiment. For institutions, the hard part is not finding a venue. The hard part is turning fragmented event markets into a usable operating layer:- Which event probabilities matter to this desk or pod?
- What changed, and was the move meaningful?
- Is the signal usable as market intelligence, alternative data, or execution input?
- Where should a human review, risk gate, or approval step sit?
- Which parts should be consumed by internal tools or AI agents?
Pilot shape
The pilot is intentionally narrow. It can begin read-only, with no platform migration and no sensitive position data required for the first pass.| Pilot module | What it tests |
|---|---|
| Market intelligence | Whether prediction-market state can improve the team’s view of catalysts, regimes, and event risk |
| Monitoring | Whether relevant markets, probability moves, cross-venue differences, and stale prices can be surfaced in time |
| Data/API integration | Whether the team can consume prediction-market state through an API, SDK, feed, export, or internal tool |
| Risk and review | Where human approval, escalation, and risk ownership should sit before any action |
| Execution workflow | Whether a market view can become a structured intent, dry-run action, or approved execution handoff |
| Agent-native workflow | Whether internal agents can read, price, monitor, and act on event-probability state |
Who it is for
| Team | Typical question |
|---|---|
| Event or macro desk | Can prediction markets improve how we monitor policy, elections, rates, inflation, geopolitics, weather, or litigation? |
| Liquidity or market-making team | Where are spreads, depth, stale prices, or cross-venue differences creating an operational edge? |
| Quant or alt-data pod | Is event-probability state a usable feature for research, screening, or systematic monitoring? |
| Crypto prime, exchange, or broker | How should prediction-market data and workflow primitives fit into institutional client infrastructure? |
| Financial data platform | Should prediction-market state become a new dataset, feed, signal, or embedded product surface? |
| Agent infrastructure team | What would it take for AI agents to consume market state and operate with reviewable intent workflows? |
What you get
A good pilot produces concrete artifacts that can be routed internally:| Output | Purpose |
|---|---|
| Market-state brief | A concise view of the event markets, probability moves, and catalysts relevant to the team |
| Watch surface | A focused set of markets, themes, and alerts worth monitoring during the pilot |
| Integration path | A recommendation for API, SDK, Agent SDK, feed, webhook, export, or FDE-assisted buildout |
| Workflow map | A clear split between information, recommendation, review, approval, and execution |
| Optional intent flow | A dry-run or approval-based workflow for turning a market view into a structured action |
| Build-vs-buy memo | A practical view of what should be built internally, bought, or handled through SimpleFunctions |
What we need to start
The first conversation should be specific:- the desk, pod, product, or agent workflow you want to evaluate
- the market family, catalyst set, or customer problem worth testing
- the preferred output surface: API, SDK, Agent SDK, Slack-style note, CSV, internal dashboard, webhook, or FDE-assisted build
- whether the first pass should remain read-only or include dry-run actions
- one owner who can tell whether the output is useful
Start a pilot
If you are evaluating prediction-market infrastructure for a desk, pod, platform, or agent workflow, send a short note with the workflow you want to test. patrick@simplefunctions.devRelated docs
Direct API access
Use SimpleFunctions market state inside existing products and internal tools.
World state
Agent-readable event-probability state, deltas, and focused world feeds.
Trade intents
The object between reasoning and venue execution.
Market making
QuoteEngine, paper mode, inventory skew, spread, and operating gates.