Tell it your goal. It does the investing work.
Bring an idea, or just your preferences. AI finds candidates, compares shares, options and waiting, and acts in a paper account within your limits. Then it keeps checking—even the paths it didn’t take.
Concept preview · Fictional scenario · No real trades
Find beyond your shortlist.
3 candidates. 2 set aside, with reasons.
Use expertise you don’t have yet.
Shares, an option strategy, or waiting.
See how every choice holds up.
Chosen and unchosen, from the same start.
Meet Your AI Investment Team.
Illustrative concept · Paper trading only
The option we didn’t take
ran out of time.
The evidence still hadn’t arrived.
The investment idea and the time available to test it were different bets.
The expiry was recorded at the start. Later observations keep those original terms.
The deadline was fixed on Day 0. The option was never traded. Expiry alone does not tell us its return.
Four questions.
One connected record.
Research in a chat. Notes in a file. A position in an account. Bring the reasons, the actions and what happened next together.
Why did I believe this?
The reasoning is scattered across chats and notes.
The view, the evidence, the date.
Keep the original investment thesis alongside what was known then.
What would change my mind?
The strongest objection needs a place of its own.
A countercase you can come back to.
Record the other side and the evidence that would change the decision.
Who decided—and what actually happened?
A suggestion, your permission and a fill are different things.
Each action keeps its own record.
Separate the AI’s recommendation, your boundaries and the paper execution.
Did the reasoning hold up later?
The selected and unselected approaches need follow-through.
New checks, connected to the original.
Revisit the evidence and every path from the same starting point.
The decision, before the outcome.
Infrastructure demand could become Company A’s orders.
Industry growth does not establish company orders—or their timing.
New order evidence, or a business that no longer fits the thesis.
What you believed. What would change your mind. What happened next.
That’s the print in DecisionPrint.The share price rose.
Shares participated in the scenario’s price move.
The thesis is still unproven.
The original company order evidence remains missing.
Compare the observations, then question the judgment. Contract and pricing data are incomplete, so this example does not rank net returns.
A better question
for the next decision.
Does the strategy give the thesis enough time?
The Day 60 observation suggests a lesson to investigate. It has to survive more than one example before it shapes another decision.
- 01
Observe the mismatch.
Start with the timing mismatch in this comparison. Treat it as a question to test across other decisions.
- 02
Look for the cases that disagree.
Test other decisions: options that worked, shares that failed, and times when waiting was better.
Further testing · not completed - 03
Use it where it holds up.
If validated, check strategy deadlines when the catalyst has no firm date. Keep the reason for using—or rejecting—the lesson.
Future use · conditional
Investment Intelligence
Start with what you want to know.
Your goal, the alternatives, and what happens after the first decision.
What is Vibe Investing?
Vibe Investing takes its inspiration from Vibe Coding: describe what you want to build, and AI handles the coding. It opens up software creation to people who have never written a line of code.
We believe investing can become just as accessible. Our ambition is to give people without a professional investment background access to the capabilities a hedge fund portfolio manager draws on—from finding opportunities and researching companies to developing strategies and evaluating decisions.
Bring an idea, or simply tell DecisionPrint your preferences. It takes on the professional work: researching opportunities, comparing strategies you may never have used, and putting them into practice in a paper trading account within your limits. As markets change, it keeps reassessing—including tracking the approaches you didn’t choose—so you can see what worked, why, and what deserves a rethink.
Can AI find investment opportunities based on my preferences?
Start with what interests you, how much risk you’re comfortable with, and how long you want to invest. DecisionPrint uses those preferences to look for opportunities—including companies and approaches you might not have considered. It explains why each candidate could fit, challenges the investment case, and shows which ones deserve a closer look—and why others were set aside.
I don’t understand options. Can AI compare strategies for me?
With Vibe Coding, you can build software with a sophisticated architecture without first becoming a software engineer. We want to bring that same access to investment strategies: you shouldn’t have to become a derivatives specialist before you can explore what they could do for you.
Describe what you want to achieve. DecisionPrint explores different ways to get there—from building or reducing a position to hedging, using leverage, or waiting for better conditions. It compares instruments and strategy combinations, explains how they work, and makes the trade-offs clear: potential gains and losses, capital requirements, and timing. It then tracks the alternatives in a simulated environment, so you can see how different choices hold up as conditions change.
Can AI track my investment thesis over time?
DecisionPrint connects an investment thesis and its counterargument to a dated decision journal, subsequent checks and the alternatives you did not choose. The example keeps the original record and adds later observations, so hindsight does not replace what was believed at the time.
How does this differ from using an AI research assistant?
DecisionPrint is built to take on an ongoing investment assignment. Bring an idea—or just your preferences—and delegate the work of finding opportunities, challenging the investment case, developing strategies and testing them in a simulated portfolio.
The work continues as markets change. It revisits the reasoning, evaluates whether to hold, reduce, hedge or wait, and acts within the permissions you’ve given it. It also follows the alternatives you didn’t choose, so you can compare how different decisions would have played out.
Each decision adds to a record you can learn from: what was known, why a choice was made, and whether the reasoning held up—not just whether the position made money.
Why does DecisionPrint use paper trading?
Investment decisions take time to reveal their strengths and flaws. A profitable trade doesn’t necessarily mean the reasoning was right, and a loss doesn’t tell you everything that went wrong. Learning requires a record of what was known, why a decision was made, and what happened next.
Paper trading provides a sandbox for that learning. DecisionPrint is designed to put strategies into practice without risking real capital, preserve the reasoning behind each decision, and track both the chosen approach and the alternatives over time. This creates feedback that can be examined, compared and used to test improvements to future decisions.
Our long-term ambition is continually improving investment intelligence: AI that learns from a growing history of decisions and tested lessons. The sandbox is where those improvements can be evaluated before they are trusted.
Can I choose the AI models I use?
Yes. DecisionPrint is designed to offer two ways to use AI:
A monthly plan with credits. No API keys to manage. DecisionPrint automatically selects suitable models for each task, balancing capability, cost and speed.
Bring your own key (BYOK). Connect your own provider accounts and choose the models you want to use. You pay your model providers directly, with a separate DecisionPrint platform fee per million metered tokens.
Choose the convenience of automatic model selection, or bring your own keys for more control.