Market context.
Reasoning in focus.
We’re developing Maven to connect market context, testable hypotheses, and human review in one research workflow.
An early-stage research project. Explore the concept below.
Meet Maven.
A research workflow starts with a question. Maven is our project to keep the observation, expectation, and evidence together, so the reasoning can be reviewed.
The interactive explorer shows written examples of the sequence we’re building toward.
Explore MavenA research process you can inspect.
About the projectFrom observation
to a reviewable thesis.
Explore the research sequence we’re building toward. Select a stage and compare two written examples of how follow-through changes an interpretation.
Hypothetical observations, written to explain the process.
Location before direction.
A move means more when its location is explicit. Start by separating the market setting from the interpretation of a single observation.
Recorded observation
Price is testing the upper boundary of a prior trading range.
Still unresolved
The observation alone does not establish whether price will sustain outside that range.
Question to carry forward: what would distinguish a sustained move from a return into the range?
Make the expectation testable.
An upward move outside the range creates a continuation hypothesis. Write down what should follow and what would weaken that explanation.
Expected follow-through
Subsequent observations remain outside the range and sustain the upward move.
Weakening condition
Price returns into the prior range instead of sustaining outside it.
Question to carry forward: does the next evidence support the original expectation?
Compare the expectation with the evidence.
In this written example, subsequent observations remain outside the range. The original hypothesis has support, while uncertainty remains.
Observed in this example
Price remains beyond the prior upper boundary and sustains the initial upward move.
Interpretation to review
This is consistent with the continuation hypothesis. It does not establish a future outcome.
In this written example, price returns into the range. The evidence weakens the initial continuation hypothesis.
Observed in this example
Price moves back inside the prior range rather than sustaining outside it.
Interpretation to review
The expected follow-through did not occur. An opposite hypothesis would need its own evidence.
Question to carry forward: what changed in the evidence, and what changed in the thesis?
Keep the conclusion traceable.
Preserve the setting, the original expectation, and the observation that supported it. A human reviewer still evaluates the interpretation.
Research note
Continuation was supported by the observations in this illustrative case; uncertainty and weakening conditions remain explicit.
Human review
Check whether the recorded evidence supports that wording and whether relevant context is missing.
Retain the initial thesis alongside the evidence that weakened it. Revising an interpretation should leave a visible reasoning trail.
Research note
The continuation thesis weakened when price returned into the range. No opposite conclusion is established by that fact alone.
Human review
Check whether the revision follows from the evidence and whether a new hypothesis needs separate validation.
Question to retain: could someone else reconstruct this reasoning from the note?
This explorer contains manually written examples. It is a concept illustration, not a live Maven product, AI response, market feed, or trading recommendation.
What we’re developing
An AI-assisted workflow for organizing market context, documenting hypotheses, evaluating follow-through, and supporting review.
Where the project stands
Maven is in early development and is not available as a public service. Intended AI integrations remain subject to development and evaluation.
Evidence before
conviction.
These questions guide Maven’s development. Browse a topic and open a note to see the reasoning behind it.
Can an observation become a testable thesis?Connect the market setting to an expectation that can be checked.
A useful thesis connects context, a directional move, and an expected follow-through. It also states what would weaken or invalidate the interpretation.
We want the research process to preserve that sequence rather than reduce it to a single label. The workflow explorer illustrates how the same initial expectation can meet supporting or weakening evidence.
The question we’re exploring
Can a research note make the expectation and its failure condition clear enough for another person to review?
Explore the thesis stageCan higher-timeframe context stay intact?Keep the original setting visible when inspecting more detailed observations.
Lower-timeframe observations should support or challenge the original thesis without silently replacing it. A local move can look different when separated from the market structure around it.
Our research explores how to keep context and more detailed observations distinct and traceable. Both should remain available when the interpretation is reviewed.
The question we’re exploring
Can more detailed evidence be examined without losing the location and structure that gave it meaning?
Explore the context stageCan AI help review without hiding uncertainty?Ground explanations in supplied evidence and keep human review explicit.
Planned AI use includes assisting with research notes, structured explanations, and post-analysis review. A fluent explanation still needs to be checked against its underlying evidence.
Intended integrations, including Claude, remain subject to development and evaluation. We are not presenting a production AI integration or validated trading performance.
The question we’re exploring
Can AI organize an explanation while retaining its assumptions, missing evidence, and unresolved questions?
Explore the review stageThese are development questions and illustrative explanations. No performance results or completed studies are being presented.
Small beginnings.
Deliberate development.
A focus on the research process.
Caftra Labs is an independent, early-stage project focused on AI-assisted trading research. Maven is our current system under development.
Our aim is to organize the reasoning around a market observation so it can be examined, challenged, and reviewed. We’re working toward a useful research workflow and evaluating its limits carefully.
A few useful answers.
Can I use Maven today?
Maven is not available as a public service. The website explorer shows manually written examples of the research sequence we’re developing toward.
Does the explorer generate AI analysis?
No. It lets you inspect illustrative research stages and compare predefined examples. Planned AI use for notes, explanations, and review still requires development and evaluation.
Are there published trading results?
We are not presenting public performance results, backtests, or promises of returns. This website describes the project and its intended research approach.