One place your desk writes things down.
A shared wiki for each of your teams — process notes, research conventions, onboarding, post-mortems. Everyone on the team reads and writes the same pages.
No credit card required.
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Per team, not per person
Each team gets its own wiki. Membership is the permission — no sharing to set up.
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Pages in a tree
Nest pages under each other, with breadcrumbs and a sidebar that always shows where you are.
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Notebook-style editor
Rich text or Markdown — headings, tables, task lists, code blocks. Paste a chart straight in.
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Wiki links
Type [[Page title]] to link pages together. A red link is a page nobody has written yet.
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Every edit kept
Version history on every page, with one-click restore, and a trash for deleted pages.
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Search across the wiki
Find a page by anything in its title or body.
Create a team, then start the first page.
Ask the market a question. Get a calculated answer.
The AI is not a chatbot bolted onto a document store. It calls the same analytics engine that powers every screen on this platform — so what comes back is a number it computed from raw history, with the command that produced it.
86,000+ instruments
Global equities, ETFs, funds, options, FX, commodities, crypto, economics, filings, transcripts and news — one normalised symbol universe with adjusted history.
A real analytics engine
Screening, backtesting, technicals, options analytics, correlations, seasonality and factor models — computed on demand from raw prices, never a stale cache.
It shows its working
Answers arrive with the charts, tables and tool calls behind them, so you can check the number instead of trusting a paraphrase.
Your own documents
Upload filings, decks and research. Ask across them and the answer cites the page it came from.
Agents and workflows
Multi-step research that runs the platform's tools for you — screen, pull the history, compute, compare, then write it up.
MCP, CLI and API
The same command catalogue from Claude, your own agent, a shell or your pipeline. The answer on screen is the answer your job gets at 4am.
You ask
“How does NVDA usually trade through earnings?”
It calls
→ ka.options_expected_move(NVDA)
It answers
NVDA has averaged a 9.2% absolute move on the day after earnings and closed higher 67% of the time. Two in three reactions land between −4.2% and +16.3% — the distribution is skewed right, not symmetric.
Every figure computed live from our own history — not scraped, not summarised.
Or start with