Guides
Step-by-step walkthroughs, from your first decision to scheduled, automated ones, then use cases: practical patterns with ready-to-run examples.
Getting started
Your first decision in the Playground
Ask three trading questions about a stock with nothing but its symbol, read the probabilities, and turn one of them into a decision. No code and no API key.
7 steps · about 5 minCall the API from your code
Create an API key, keep it out of your code, send your first request and handle the answer, in the language you use.
6 steps · about 10 minSave, share and reuse queries
Keep the requests you use often, share them with your team, and turn any of them into code.
5 steps · about 3 minFinancial decisions
Decide on a stock with just its symbol
Ask trading questions about a US stock or ETF without sending any market data: the server fills in the latest completed session.
4 steps · about 5 minRead probabilities and choose thresholds
A probability is not a signal until you decide where to act. Here is how to pick that line and check it against what really happened.
5 steps · about 8 minAutomation
Send a Slack alert when a rule fires
Answer the questions, test a rule on the answers, and post to a Slack channel only when it is true.
6 steps · about 10 minSend decisions to your own system
Deliver a signed JSON payload to your order system, risk system or automation tool (Zapier, Make, n8n) whenever a rule fires.
6 steps · about 15 minRun a watchlist every day after the close
Ask the same questions about every symbol on a list, on a schedule, and get one digest message per run.
5 steps · about 10 minProduction
Use cases
Practical patterns with ready-to-run examples. Open any of them in the Playground, then adapt the questions to your data.
Tag financial news for trading signals
Turn each headline or news item into structured fields: is it about this ticker, what kind of event, which direction, and how material, ready for a screener or a model.
See the example →AI safetyGuardrails for LLM apps
Screen every prompt (and reply) for jailbreaks, harmful requests and self-harm in ~100 ms, then pass, review, block or support.
See the example →Customer supportSupport ticket triage and routing
Ask every routing question in one call (team, urgency, refund, frustration), then route in code and send uncertain tickets to a human.
See the example →Search and RAGFilter retrieved passages before your LLM answers
Check each retrieved passage for relevance, real answer evidence, conflicts and prompt injection, so only usable text reaches the answering model.
See the example →VerificationVerify claims and citations against a source
For each claim an LLM (or a writer) makes, check whether the cited source supports it, contradicts it, or says nothing, and only auto-accept confident supports.
See the example →