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AQREVI
product / ai

One model explains your numbers. A second checks every claim.

The AI reviews a day, a trade, a week or a period and answers questions about your statistics. The engine computes the numbers first, and a second, fast decision model picks the trades to review and checks the claims against the data.

A day review with links to the computed metrics

Trading AI is usually confident. You have nothing to check it with.

It gives you a sentence with no arithmetic behind it: the numbers were generated along with the text. You can neither confirm nor refute it, and it sounds more convincing than the truth.

code computes, one model decides, another explains

How it works here

The model gets already computed numbers, and each of its claims is checked against the data.

Code computes

Metrics, limits, tilt and streaks come from an engine under tests. The model receives finished numbers and is kept out of the maths.

The decision model answers briefly

It answers narrow questions with a probability: does a trade break your rules, does a claim hold on the data, which agent should get the question.

The review model reasons

The review model writes the reviews, the chat answers and the agents' work: the platform model or your own by key. It sees the computed numbers, your dossier and your rules, and every call goes into the trace.

A review on your numbers

Reviews of a day, a trade, a week, a period, a playbook or a prop account. The model sees the engine's metrics, your dossier and your rules, so it judges a trade by your style, not a textbook.

  • Every number in the answer links to the metric it came from
  • A chat with tools: the model fetches the cut it needs
  • Memory of your past findings and decisions
  • There's no setting that lets the model compute
A coach answer with metric links under each number

A model picks the trades to review

Five hundred trades won't go into a model whole. The engine sets flags: no stop, a loss beyond −1.5R, the best by R. The decision model scores each trade against the rules in your dossier, and 10–40 of the right ones go to the review.

  • A rule-break probability for each trade
  • The reason for the pick shows next to the trade
  • The review costs less and hits closer than one over the whole history
  • Picking is off by default; you switch it on with a button
The pick: trades, engine flags and break probability

Every claim checked

The finished review is split into claims about the data, the ones with a number or a comparison. The decision model checks each against the facts and marks it. Advice like 'reduce your risk' isn't checked, because it isn't a fact.

  • Supported: from 80% confidence
  • Hypothesis: from 50%, worth checking
  • Recheck: the claim doesn't hold
  • The mark sits right under the sentence
A review with a mark under each claim

Entry risk before the trade

In the journal before entry you enter the instrument, direction and risk. Code checks the remaining daily limit, trade count and tilt, and the decision model rates the entry against your rules. A hard limit in code outranks the model: risk above the remaining limit means stop.

  • Three outcomes: clear, careful, stop
  • The reason shows: limit, tilt or a rule
  • The model can tighten the verdict but never loosen it
  • The model never judges the trade's direction
The risk gate in the journal: limit left, tilt and verdict

Your choice of model and key

On paid plans the AQREVI model answers by default, and it's included. You can connect your own key from Anthropic, OpenAI, Google, xAI, DeepSeek, OpenRouter and others, and then no operations are spent. On Free the model runs on your own key.

  • Every chat window shows which model is answering
  • The provider only receives the answer's context
  • We forbid the platform model from keeping your data
  • Without a model everything works except the reviews
Model settings: the AQREVI model, your own key, what leaves

A trace of every call

The call log is append-only: the run, the step, what went out, what came back, what it cost. The trace screen is built from that log, so there's no second version of events.

  • A model error shows in the log, not buried in the answer
  • The cost of a run in operations
  • Both models' answers sit in one trace
  • The log lives in your database and exports with it
A run trace: the steps, inputs and outputs of one review

Where it differs

Trading AI usually looks at a chart and calls a direction. Ours doesn't read charts and never says long or short. It explains numbers already computed from your trades, and a second model checks whether each claim holds on the data.

Check it yourself

Open the trace of any answer: the request, the tool calls, the returned text and the cost in operations. Under a review sit the decision model's marks: supported, hypothesis or recheck.

The order

From an empty screen to the first review

Pick a model

On a paid plan the AQREVI model is already connected. On Free, add your own key in settings.

Ask about what's computed

About a metric, a day, a streak or a trade. The model gets the numbers, never access to computing them.

Check the marks

The decision model's marks sit under the review, and every number links to its metric. If you disagree, open the maths.

What it does not do

What this module does not do

  • It doesn't read charts and won't say 'long' or 'short'. The product is designed that way.
  • It doesn't predict the market. It can break down what happened, not what comes next.
  • It doesn't watch you: no screen recording, no click history, no behavioural profile.
  • If the engine didn't compute a number, the model has nothing to say about it.

Questions

The things that stand in the way

What is the second model?

A model for short decisions. It answers a narrow question with a probability: yes or no, which option. It's cheap and fast, so it checks what would be costly for the review model: every trade and every claim. The mechanism doesn't depend on which model writes the review.

Do my trades go to OpenAI or to you?

The journal lives with us, in your account's own database. The model provider only gets the answer's context: with the AQREVI model, the provider we use; with your own key, the one whose key you entered.

Will it predict where the market goes?

No. It reads your statistics. Other people have already sold you price forecasts.

What if the model gets it wrong?

It can't get a number wrong, because it doesn't produce numbers. It can misread them, which is why the decision model flags claims that don't hold and every number links to its metric.

What does it cost?

In operations, not tokens. A chat question costs one, a day review three, a period review five, an agent answer ten. Free has 30 operations a month on your own key, Core 300, Pro 2,000, Apex 7,500.

A reading you can check

The numbers under it are computed separately and open, and a second model has marked the claims. A confident sentence without arithmetic leaves you nothing but trust.

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