Stress tests
Challenge decision logic through adversarial AI agents trained to surface risks and fragile assumptions.
A decision system, not a chatbot.
Rayify runs where your team already works - on your machines, against your sources, inside your tools. You bring a live question; what comes back is a decision your team can act on, with the reasoning and the evidence attached.
Runs on your stack
Your machines, your sources, your connectors. Deploy locally or in the cloud - your data does not have to leave to be useful.
Adversarial by design
Specialist agents argue the question from opposing institutional lenses - bull and bear, macro, regulatory, sector, supply-chain - and the disagreement between them is the output, not something averaged away.
Evidence attached
Every claim is tied to a verified source and every conclusion can be traced and reproduced, so the brief holds up in front of an investment committee.
What you get back
- The strongest case for and against the thesis
- The assumptions doing the most work, and which are most likely to break
- First- and second-order exposure effects
- Dissenting views worth your attention
- The evidence behind every claim
- Ongoing monitoring of those assumptions after the decision
Bringing rigorous stress-testing to high stakes decision makers
Preserves decision lineage
Track how reasoning evolves over time, revisit past decisions, see what changed, what succeeded or failed and why.
Scales depth
Expand your team's analytical capacity and explore new opportunities.
So that you can
Decide with conviction
Know the reasoning has been robustly challenged from many angles, not just the ones you'd have thought of.
Compound institutional judgment
Every decision leaves a record of the reasoning process, so your team learns from its own track record instead of repeating it.
Operate beyond your bandwidth
Expand coverage across positions, scenarios, and market shifts without adding headcount.
Questions Rayify is built to answer
- Stress-test our thesis: strongest case both ways, the assumptions doing the work, what would change our mind?
- Where does consensus on who wins agree vs diverge -- and what would reorder the tiers?
- The strongest case against our short -- what proves us wrong, and what flags it early?
- Which assumption is most exposed, where do we differ from the Street, and what would force a change?
- Our private-credit thesis assumes contained defaults. Where's it fragile, and what warns us first?
- Where could our multiple be wrong, and what signals the comp set no longer holds?
- Where's the target's competitive story most fragile, and what breaks before the numbers do?
Frequently asked questions
What data sources do you have access to?
By default we work from company filings, the open web, a live news feed, and our own prior research. You can also bring your own materials - diligence documents, models, and reports in PDF, Word, Excel, or CSV - to ground the work in your specific situation. Everything is attributed to its source, so you can check where any claim comes from. For ongoing engagements we can connect the third-party tools and data sources your team already uses, so the work runs inside your existing workflow rather than alongside it.
What does an engagement actually look like?
It starts with a defined question - a portfolio call, a market scenario, an investment thesis - and you can frame it around your own context. For a first engagement we run a short scoping session to turn that into a precise brief, do the work, and walk you through what we find. After that you can send questions straight in and get a faster turnaround. The deliverable is a brief you can act on: five sections, around a ten-minute read, covering the strongest case for and against, the assumptions most likely to break, the main knock-on effects, the dissenting views worth your attention, and the evidence behind every claim. A lot of clients run it alongside their own analyst to compare.
How long does a stress-test take?
Days, not weeks. The time goes into verification rather than generation: every claim traced to a source, and the reasoning adversarially tested before it reaches you. It is built to fit inside a live decision window, before conviction hardens into exposure.
Who sees our inputs?
Engagements run under an NDA by default. We keep nothing once the work is done, and we never use what you give us to train our models. Your documents are encrypted and private to your account, never pooled or shared, and anything you delete drops out of future work. Access is limited to the team on your engagement, for that work only.
How is this different from Claude, Gemini or ChatGPT?
General-purpose tools are built to give one confident answer. For a capital decision, that's exactly the wrong instinct. They average competing views into a single position, carry no track record on the question you're asking, and can land on a different answer next week with no way to see how they got there. Rayify takes the opposite approach. Each question runs through several expert lenses - bull and bear, macro, regulatory, thesis-specific - and the disagreement between them is what we show you, not something we average away. Every claim is tied to a verified source, and every conclusion can be traced and reproduced. You get an answer that holds up in front of an investment committee, not just a quicker one.