Revenue ledger · rows 12–13
Q4 2024 | revenue_usd = 1,000,000 Q4 2025 | revenue_usd = 1,250,000
Back to logic overviewFacts become
01 / Fact derivation
Facts become
new conclusions.
Turn separate facts into connected, checkable reasoning. See calculations, conditions and multi-step dependencies produce new conclusions.
Start with this example1Revenue grows2Profit falls3Follow the derivation
Your AI can show how it knows.
EXAMPLE 01 / AI ANALYTICS
Growth can hide a different story.
Revenue rises 25%. Combine the revenue and cost records to discover what happened to profit and margin.
Change the current cost. Watch the same evidence structure support a different conclusion.
What the records tell us
Same company · Q4 · USD
| Period | Revenue | Cost |
|---|---|---|
| Q4 2024 | $1,000,000 | $600,000 |
| Q4 2025 | $1,250,000 | $875,000 |
Same company, quarter and currency. Source records are available below.
Current cost scenario
Current profit: $375,000 · margin: 30%.
What-if scenario: the current cost is changed. Original source records are preserved below.
01
Profit
Subtract cost from revenue for each period.
$1,250,000 − $875,000 = $375,000
Previous period$400,000
→Current period$375,000
02
Profit margin
Divide profit by revenue to compare how much each dollar earns.
$375,000 ÷ $1,250,000 = 30%
Previous period40%
→Current period30%
Derived conclusion
Revenue rose. Profit fell.
Revenue grew by $250,000, while cost grew by $275,000. Profit fell $25,000 and margin fell 10 percentage points.
Profit change−6.25%
Inspect the evidence+
Cost ledger · rows 08–09
Q4 2024 | cost_usd = 600,000 Q4 2025 | cost_usd = 875,000
Reporting scope · paragraph 2
Both periods cover the same company and the same quarter. Revenue and cost are measured in USD.
Read the rule+
profit(period) = revenue(period) − cost(period) margin(period) = profit(period) ÷ revenue(period) profit_fell ← current_profit < previous_profit
A useful answer connects the underlying records, derives the intermediate facts, and keeps the entire path available for inspection.
LOGIC ACROSS AI WORKFLOWS
Different questions. Explicit logic.
The same idea applies to an agent action or a chain of dependent services.
EXAMPLE 02 / AGENT ACTION
Can the agent share this report?
All three conditions must be satisfied before sharing.
✓Owner permission
○Destination approvalNot approved
✓No restricted content
One condition is not satisfied.
Let an agent explain the conditions behind a proposed action.
Read the rule+
share_allowed ← owner_permission ∧ approved_destination ∧ no_restricted_content
EXAMPLE 03 / MULTI-STEP REASONING
Which services are affected?
Key store unavailable
- Identity serviceNeeds the key storeAffected
- CheckoutNeeds the identity serviceAffected
- Public status pageUses a separate static cacheNot affected
Follow a dependency through several steps to surface an impact that no single record states.
Read the rule+
affected(service) ← depends_on(service, dependency) ∧ affected(dependency)
A conclusion with its workings.
Evidence → Reasoning → Reusable knowledge
Source-backed facts
Preserve the passage, value and scope behind each premise.
Explicit derivation
Use conditions, calculations and exceptions to produce intermediate facts. ASP-based reasoning makes these dependencies inspectable.
Independent checking
Revisit the evidence and replay the relevant derivation with an independent checker.
CITPROOF
Build reasoning into your AI.
Bring us a workflow where answers, actions or discoveries need a stronger basis. Explore how evidence chains and logic verification can fit your product.
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