Prediction Market Analyst AI is a forecasting assistant that delivers probability assessments and fee-adjusted edge analysis for Kalshi, Polymarket, Metaculus, and related platforms. Unlike generic chatbots that lose prior P_fair estimates, this agent tracks assessed contracts, cruxes, and calibration across sessions. Built on Jenova's unified platform, it combines models from OpenAI, Anthropic, Google, and more with search that verifies polls and prices before any assessment. This is analytical research, not financial or trading advice.
Prediction Market Analyst turns a named contract into a fair-probability estimate (P_fair), a comparison to market-implied probability after vig, an edge in probability points, a confidence tier, and a fee-adjusted expected value after spread and slippage. Analysis starts from historical base rates, then adjusts only as far as the evidence quality supports — not as far as the narrative is vivid. Google Search is used to pull current polling aggregates, BLS releases, CME FedWatch prints, and official filings so the number is not invented from stale training data.
Prediction Market Analyst inspects settlement rules before assigning any probability, because a correct view of the world can still lose money if the contract resolves on a different source, date, or edge case. The agent separates “will this event happen?” from “will this contract resolve YES?”, flagging ties, delays, voids, oracle disputes, and wording traps. Search is used to confirm current fee schedules, regulatory rulings, and known settlement fights rather than assuming platform mechanics are unchanged.
Prediction Market Analyst compares the same event across Kalshi, Polymarket, Metaculus, Manifold, PredictIt, and Insight Prediction on price, liquidity, fees, and settlement risk (CFTC-regulated event contracts versus UMA oracle versus community scoring). Related contracts are checked for direct correlation, hedges, conditional probability chains, and cases where logically related prices fail to sum to 100%. Multi-contract work can be exported as a CSV comparison table or a sourced briefing in PDF or DOCX so the book, not a single quote, is the unit of analysis.
Prediction Market Analyst maps a development — a CPI print, a court ruling, an FOMC statement, a hurricane track — onto the contracts it actually moves, including second-order names. When asked what looks interesting, it ranks candidates by confidence in the edge, not by volume spikes, and treats sudden volume as attention rather than direction. Official and model sources sit above news; Reddit and social consensus are treated as narrative tracking, not primary evidence.
Prediction Market Analyst switches into teaching mode when you ask how a number was produced, making Bayesian updating, reference class forecasting, Kelly sizing concepts, and calibration explicit. Biases such as anchoring to the last trade, base-rate neglect, fee blindness, and resolution conflation are named when they are doing work in the analysis. Prior formal assessments are reused on follow-up (“you had this near 60% — what changed?”) so the process, not a single win or loss, is what gets scored.
| Traditional Approach | Prediction Market Analyst |
|---|---|
| Forgets prior P_fair and crux | Tracks assessed contracts across sessions |
| Treats last price as the probability | Separates P_fair from fee-adjusted EV |
| Recites stale polls from training data | Searches official sources before scoring |
| Skips settlement rules and edge cases | Reads resolution criteria before probability |
Unlimited Memory vs. Chatbot Amnesia
Generic chatbots drop the last P_fair, edge direction, confidence tier, and crux the moment the thread is gone. That failure is costly here: the next session re-anchors to the current print (the disposition effect), you cannot tell whether November CPI actually moved your Fed-cut number, and a prediction log never forms. Prediction Market Analyst keeps each formal assessment — contract, platform, P_fair, overpriced/underpriced/fair, confidence, crux, date — and updates it on re-score instead of starting from the market’s last tick.
Multi-Model AI Access
Scoring a contract is two different jobs: first, synthesizing conflicting FiveThirtyEight-style aggregates, FOMC language, and wire copy; second, writing a calibrated probability that does not overweight the most vivid story. Those phases do not want the same model, and single-vendor tools cannot switch — ChatGPT is OpenAI-only, Claude is Anthropic-only. This agent can route messy evidence gathering and careful probability language across OpenAI, Anthropic, Google, xAI, DeepSeek, Qwen, and more without juggling accounts.
Intelligent Tool Integration for End-to-End Workflows
A useful forecast is not a chat reply. The production path is contract and resolution text → Google Search (and Scholar, YouTube, or Maps when the domain needs them) through a six-tier source hierarchy → P_fair versus fee-adjusted market price → scenario table, crux, and “what to watch.” Calendar can hold expiration and data-release dates; Notion, Drive, or Dropbox can hold the track record; CSV/PDF/DOCX export turns the same work into a comparison sheet or briefing rather than a scrollback.
Base rates, fees, and resolution discipline
Specialization here is a set of failure modes generic assistants skip: forming an estimate before looking at the price, computing EV after fees so a +3% gap can still be negative, refusing to treat “this could happen” as “this is likely,” and passing when nothing articulable says the market is wrong. Platform knowledge (Kalshi order-book structure, Polymarket’s UMA oracle, Metaculus calibration culture) is applied only after current rules are checked, because fees and access change.
Kalshi and Polymarket traders
Use the agent to decompose a quote into P_fair, confidence-adjusted edge, and fee-adjusted EV, then to flag resolution risk before size. Time horizon and capital lockup are part of the edge discussion, not an afterthought.
Metaculus and Manifold forecasters
Use teaching mode to practice base rates, Bayesian updates, and verbal-plus-numeric probability language, and to keep a log of assessments versus later resolution. Play-money and community tracks are treated as calibration gyms, not as identical to CFTC venues.
Election, Fed, legal, and geopolitics researchers
Use event-impact workflows to connect a docket, a BLS release, or a diplomatic signal to the contracts that should move, with official data ranked above commentary. The output is a sourced probability range and a named crux, not a recap of the news.
Newcomers learning expected value
Use step-by-step explanations of vig, resolution sources, order-book noise versus thick books, and why market respect is the default. Terms are defined when the user is new, then dropped once the session shows EV and oracle vocabulary.
Anyone holding a correlated book
Use multi-contract review to catch the same underlying bet repeated, inverted hedges, and probability sums that cannot all be right. Pass recommendations are part of the job when liquidity is thin or settlement wording is unacceptable.
Free For exploring Jenova:
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Max ($200/month) For professionals:
Ultra ($500/month) For heavy professionals:
Enterprise ($1,000/month) For businesses:
All plans are billed monthly with no daily usage caps — your full quota is available immediately on each billing date.
Prediction Market Analyst is an AI that scores event contracts: it estimates a fair probability, compares that figure to the market-implied price after vig and fees, and states whether the gap looks like edge or noise. Coverage spans politics, economics, legal outcomes, geopolitics, climate, science, crypto, sports, and culture on venues such as Kalshi, Polymarket, Metaculus, Manifold, PredictIt, and Insight Prediction. You can ask for a short assessment (probability, confidence, crux) or a full scenario table with risks and catalysts.
Prediction Market Analyst stores each formal assessment — P_fair, edge direction, confidence, and crux — so a later re-score is compared to your prior view instead of snapping back to the latest print. Research-heavy polling synthesis and calibrated probability writing can be routed across models from OpenAI, Anthropic, Google, xAI, DeepSeek, Qwen, and more, rather than locking you to ChatGPT’s OpenAI-only stack or Claude’s Anthropic-only stack. Search, Scholar, Calendar, and document export turn a contract question into a source-dated briefing, not an unsourced chat answer.
Prediction Market Analyst uses Google Search as the default fact path for polls, prices, economic releases, and platform rules, with Google Scholar for scientific or methodological markets and YouTube for hearings, press conferences, and debate footage. Reddit and X-indexed search are available as sentiment checks (tier-six evidence, not anchors); Google Calendar can hold expiration, settlement, and data-release dates; Notion, Google Drive, Dropbox, and Gmail can archive notes; PDF, DOCX, and CSV generation produce briefings and comparison sheets. The workflow is contract in, sourced P_fair and fee-adjusted EV out.
No. Prediction Market Analyst provides analytical research on probabilities, resolution rules, and fee-adjusted expected value; it does not tell you to buy or sell. Prediction markets involve risk of loss, and you make your own decisions. Regulatory comments (CFTC rulings, US access restrictions) are descriptive, not legal advice.
No. Prediction Market Analyst cannot attach to live order books, streaming quotes, or venue APIs. It searches for the latest published figures, timestamps them, and tells you to confirm the print on the platform before acting. If a current price cannot be verified, that gap is stated and confidence is cut rather than filled from training data.
Prediction Market Analyst is built around Kalshi, Polymarket, Metaculus, Manifold, PredictIt, and Insight Prediction, including cross-venue price, liquidity, fee-drag, and settlement-risk differences. Structural facts (Kalshi as CFTC-regulated, Polymarket’s UMA oracle, Metaculus community scoring) can be used without search; current fees, product changes, and access rules are verified first. The same underlying event is not assumed to be the same contract once resolution sources diverge.
Prediction Market Analyst will say it could not find current data, refuse to substitute training-set “facts,” and either lower confidence or recommend passing. Must-verify categories include polls, CPI and employment prints, contract odds, court outcomes, crypto prices, and platform fee changes. Silence filled with an old number is treated as a worse error than an explicit gap.
No. Prediction Market Analyst cannot schedule recurring tasks, watch books in the background, or push notices when odds move or new markets list. Analysis runs inside the conversation you start. For dates you already care about — FOMC, expiration, a data release — Google Calendar can store them as a manual watchlist, not as an automated trigger.
A contract can be right about the world and still settle against you if the resolution source, timing, or edge-case language differs from the headline question. Prediction Market Analyst therefore identifies the official source, void/NO/extend rules, and known dispute patterns before it publishes a P_fair. UMA oracle votes, admin discretion, and official statistical agencies are not interchangeable settlement mechanisms.
No. Prediction Market Analyst does not connect to exchange accounts, fills, or bankroll. It only retains contracts it formally scored in conversation (and user-supplied specs or notes), not your betting history. That also means it cannot size positions from your actual book — Kelly discussion stays conceptual unless you provide the inputs.
Yes. Prediction Market Analyst runs with full parity on web, iOS, and Android, so an assessment, resolution-criteria check, or teaching walkthrough can happen while you confirm a quote on Kalshi or Polymarket. iOS: https://apps.apple.com/app/jenova/id6747692301 — Android: https://play.google.com/store/apps/details?id=ai.jenova.app.prod. Agents, memory, and settings sync across devices.
No. If the agent cannot state in a few sentences what the market is missing, the honest output is “fair / pass,” not a forced +EV number. Thin evidence, ambiguous settlement, stale information, or a high-quality reference class that already matches the price are all reasons to stand down. Manufacturing edge to justify the research time is treated as a process failure.
Prediction Market Analyst is part of Jenova, the most powerful AI agent platform in the world. Jenova provides unified access to leading AI models from OpenAI, Anthropic, Google, xAI, DeepSeek, Qwen, and more — alongside intelligent tool integrations for end-to-end task completion. The platform features persistent cross-session memory, automated workflow orchestration, and full parity across web, iOS, and Android. Developed by Azeroth Inc., a New York-based technology company, Jenova serves users in over 70 countries.
For developers, the Jenova Agent API provides a fully-managed agent backend with built-in orchestration, multi-model routing, memory, storage, and MCP tool support — replacing the need to stitch together raw LLM APIs, vector databases, and custom tool servers.
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