> ## Documentation Index > Fetch the complete documentation index at: https://docs.simplefunctions.dev/llms.txt > Use this file to discover all available pages before exploring further. # Desk/pod pilot > A focused institutional pilot for prediction-market intelligence, monitoring, risk review, and execution workflows. SimpleFunctions is prediction-market infrastructure for desks, pods, and agents: market intelligence, calibrated feeds, monitoring, risk review, and execution workflows. The desk/pod pilot is a focused way for an institutional team to evaluate where prediction-market state belongs in its workflow. It starts with one real operating question and one real consumer: a trading desk, market-making team, quant pod, data platform, product team, or internal agent. The goal is not another generic dashboard. The goal is to find the part of the workflow where prediction-market state becomes useful enough to route, monitor, or automate. ## 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? SimpleFunctions is built for that layer above the venues. ## 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 | The output is designed to help the team decide whether SimpleFunctions should be used as a data layer, workflow layer, agent layer, or execution-adjacent infrastructure. ## 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 No broad vendor evaluation is required to begin. The pilot starts by finding one real workflow where prediction-market state might matter. ## 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.dev](mailto:patrick@simplefunctions.dev) ## Related docs Use SimpleFunctions market state inside existing products and internal tools. Agent-readable event-probability state, deltas, and focused world feeds. The object between reasoning and venue execution. QuoteEngine, paper mode, inventory skew, spread, and operating gates.