A stronger basis for a decision.
AI can produce polished work quickly. Decision readiness comes from checking what the work actually supports, for the use at hand.
A polished document is not necessarily ready to carry a decision.
It may be accurate in parts, well written, and covered in citations. The important question is still open: does this document support this decision?
That question is harder than fact-checking. It asks whether the evidence supports the claims, whether the numbers use the right base, whether the assumptions are visible, whether the conclusion follows, and what remains unresolved.
Decision grade is narrow on purpose. It is earned for one declared use through a defined review. It is not a permanent property of a document, a general label for good analysis, or a promise that the result is universally correct.
For work beyond the reviewed material—including synthesis, scenarios, backcasting, implications, and recommendations—the better term is decision-ready. The aim is a stronger basis for judgment, with the distance from source evidence made visible.
The practical reframe
Think of the difference between an article with citations and the same article without them.
The citations do not make the article correct. They give the reader a stronger basis for checking what the author says. A good integrity process does something similar for AI-assisted professional work: it makes the claims, evidence, assumptions, arithmetic, reasoning, limits, and unresolved questions easier to inspect before anyone relies on them.
A disciplined process should produce work that is more decision-ready than an ordinary prompt-and-answer workflow. How much better is an empirical question that must be measured against strong model and human comparators.
Where to start
Understand the problem
Read The problem for the distinction between polished output, supported claims, and decision-ready work.
Inspect the evidence
Read Current evidence for the active 2026 research, its exact task, and the limit that travels with each result.
Use it now
Read Questions to ask before relying and bring them to the next review, procurement, or approval meeting.
What has to be checked
What entered the process
Establish the source, the version, and any part that could not be read faithfully. A perfect analysis of a damaged representation is still damaged.
What the document actually supports
Test claims, assumptions, in-document evidence, arithmetic, contradictions, and reasoning dependencies. Keep outside truth separate from what the submitted material says.
What the intended use requires
Name the decision, action, audience, or reliance the document is meant to support. The same document can be adequate for exploration and inadequate for a board or capital decision.
What remains unresolved
Preserve material uncertainty, disagreement, missing research, and conditions. Completion is not the same as clearance.
The distance problem
Specificity and reliability should be expected to weaken as work moves farther from source evidence.
Direct claims sit close to a source. Synthesis adds interpretation. Scenarios add conditional branches. Backcasting adds a desired future state and a proposed path. Recommendations add judgment about what someone should do.
Each step may be useful. Each step also creates more room for assumptions, omitted alternatives, false precision, and confident prose to outrun what is known. The answer is not to ban future-facing work. It is to label its conditions and inspect the path.
Closer to evidence
Source passages, direct claims, reconciled arithmetic, named limits, and explicit support states.
Farther from evidence
Synthesis, implications, scenarios, backcasting, forecasts, and recommendations. Useful as conditional decision aids, not verified futures.
What this site claims
- AI is already part of ordinary professional work.
- AI can create polished work faster than people can check it.
- A correct final answer can hide weak earlier reasoning.
- A yes-or-no check can miss the difference between supported and partly supported.
- Important work needs a declared use, a controlled check, preserved limits, and a recorded decision.
What this site does not claim
- That citations make a document true.
- That one research result supplies a universal AI error rate.
- That multiple models are automatically independent.
- That a completed check guarantees a safe outcome.
- That current external research proves any particular system outperforms a strong model or a qualified human reviewer.
Published openly. The source is on GitHub. The framework is also available through llms.txt, the full-text bundle, and the public MCP server. Content is licensed under CC BY 4.0.
The publisher and commercial-interest disclosure is stated separately from the doctrine.