Peret
Financial Services · Prediction-Market Analytics
Finding prediction-market mispricing with research-grounded AI forecasts
Peret aggregates live Kalshi and Polymarket markets, generates an independent research-backed AI probability and shows where that probability disagrees with the market price.
The starting point
Market probability is visible, but it does not explain whether the market may be wrong.
What had to change
Users needed an independent researched probability and a direct comparison.
What couldn't break
Ingestion, forecasting and AI usage had to stay current and sustainable.
Why this was hard
Live data, research, probabilistic reasoning, background processing and cost all have to work together.
What Datics changed
Datics aggregated Kalshi and Polymarket, generated research-grounded forecasts and ranked the edge between the AI view and the market.
Cross-market aggregation
Kalshi and Polymarket read side by side.
Research-grounded AI forecasting
Probabilities backed by background research.
AI-versus-market edge
Where the model and the market disagree.
Research and correlation views
Market detail and correlation exploration.
Platform in action
Opportunity Dashboard
Where the largest disagreements surface.
Market Detail & Research
Where a single market is examined.
AI Search & Correlation
Where markets are searched and related.
Engagement at a glance
Users can compare a research-grounded AI probability against the live market price and see where the two disagree.
- Markets
- Kalshi and Polymarket
- Core output
- Independent AI probability
- Signal
- AI-versus-market pricing edge
Under the hood
- Datics role
- End-to-end AI SaaS product engineering.
- Integrations
- Kalshi and Polymarket market data, Clerk authentication and Stripe billing.
- Deployment
- Cloud-hosted application with background job processing.
- Production boundary
- Freemium multi-tier SaaS with admin observability over AI usage and cost.
Technology
What this unlocked
The research and forecasting pipeline can support additional markets and progressively deeper analysis while keeping the underlying probability comparison consistent.
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