Metavulus.ai is being built as the intelligence layer for market prep, trade validation, crypto narratives, journal review, and learning recommendations. Not a signal bot. Not a promise machine. A disciplined decision layer for serious traders.
For education and research only. Not financial advice. AI-generated analysis may be wrong; verify data and manage risk.
Product wedge
The sticky feature is not “ask AI anything.” The sticky feature is a loop: market signal → thesis → checklist → risk → execution plan → journal → review → improvement.
What matters today, where risk is dangerous, and which assets deserve focus.
Does this setup match ATS, where is invalidation, and should I wait?
Why did this trade win/lose, what rule broke, and what should I study next?
Community questions, user feedback, and market research become roadmap intelligence.
Intelligence modules
Habit loop
A WIB-first morning desk that compresses macro, FX, gold, indices, and crypto into what matters today.
Users start the day with bias, danger windows, assets to watch, and no-trade zones.
Setup validator
A structured assistant for symbol, timeframe, thesis, invalidation, risk, and ATS alignment.
Users get a trade / wait / avoid verdict without fake certainty or signal-bot framing.
Retention moat
Private review over user journals and checklists with explicit consent, focused on repeated mistakes and next study step.
Users discover why they keep losing, what rules they break, and what to improve this week.
Alpha desk
Narrative, liquidity, on-chain, perps, and catalyst research converted into watch/trade/avoid cards.
Users see which crypto stories are tradeable and which are only noisy mindshare.
Company memory
Community questions, feedback, support issues, and high-quality analysis become lessons, FAQs, and product tickets.
The product learns from users every week instead of letting feedback disappear in chat.
Assistant workflows
“I want to long XAUUSD on M15. What needs to confirm before entry?”
Guardrail: Automated educational response. Strong calls or public posts require approval.
“Review my BTC trade and screenshot. I entered after breakout and got stopped.”
Guardrail: Requires user consent to inspect private journal/screenshot data.
“Give me today’s market map before London session.”
Guardrail: Can run automatically with citations and freshness labels.
“AI coins are trending again. Which ones are worth watching and what is the risk?”
Guardrail: Token recommendations and restricted data need compliance/human review before publishing.
Memory + source discipline
Explain concepts, recommend lessons, and connect market conditions to education.
Guardrail: Answers must cite lessons or clearly say when a concept is general market education.
Retrieve prior bias, scenario maps, levels, and post-market lessons.
Guardrail: Old research must be labeled archival or stale, never treated as current market context.
Warn users about CPI, NFP, FOMC, rate decisions, and dangerous trading windows in WIB.
Guardrail: Display event time, source, and whether actual data is supported by the feed.
Surface narratives, on-chain tracks, token cards, source stack, and source freshness discipline.
Guardrail: Public crypto research must transform restricted inputs and avoid raw proprietary labels.
Detect repeated mistakes, rule violations, bad timing, and personalized study priorities.
Guardrail: Requires explicit user consent per workflow. Never used in public answers or community output.
Find confusion clusters, content opportunities, support friction, and roadmap signals.
Guardrail: Aggregate and anonymize by default. Do not expose individual messages without consent.
Internal research signal for smart-flow clusters and token risk context.
Guardrail: No raw labels, rankings, wallet lists, or provider dashboard mimicry unless license allows it.
Premium path
Early members and curious traders
Rp 0
Serious retail traders
Rp149k–299k/mo
Committed / prop-style traders
Rp499k–999k/mo
Operating principles
Decision context beats raw data.
Every trading answer needs risk, invalidation, and uncertainty.
Freshness labels are mandatory for market-sensitive claims.
Private journals are user-owned and consent-gated.
Restricted third-party data is internal input until compliance says otherwise.
Metavulus.ai teaches discipline; it does not promise profits.