“Can’t I just paste this into ChatGPT?”
You can get prose from an LLM. You cannot get intent-aware threshold logic, evidentiary sufficiency, and deterministic screening from one.
Not just citation. Intent, fear, signal, verdict.
Sounds polished. Still has no threshold logic.
Based on the company’s financial profile, leverage appears manageable and the business seems strategically attractive. However, investors should monitor concentration risk and cash flow trends before proceeding.
Structured, sourced, and sufficient for a verdict.
What changes once the output has to survive real committee scrutiny.
Not just “where did this number come from?”
It asks whether the evidence is already sufficient to conclude the deal breaches the stated thresholds.
It can re-evaluate the same extraction under different buyer intents without rerunning the whole pipeline.
Evidentiary sufficiency
An LLM can describe a red flag, but it cannot tell you whether the accumulated evidence is already sufficient to reject the deal.
Evidentiary sufficiency
IACalc applies rejection sufficiency. One confirmed fatal flaw can justify a critical threshold breach; proving yes requires broader absence checks.
Intent -> Fear -> Signal -> Verdict
Generic output ignores buyer context. Yield, strategic IP, and distressed buyers all get the same soft answer.
Intent -> Fear -> Signal -> Verdict
IACalc activates a fear profile from the thesis, evaluates the relevant signals, and produces a verdict calibrated to that specific buyer.
Progressive evidence accumulation
Reads the current prompt in isolation. It does not know what evidence is still missing or what has already become decisive.
Progressive evidence accumulation
IACalc accumulates evidence from documents, web, and image sources, then shows what is sufficient, inconclusive, and still needed.
What the system is actually doing under the hood.
Negative selection, not generic summary
The core question is not “is this deal good?” It is “does this deal breach the thresholds for this mandate?” The system is designed to find disqualifying evidence early, before sunk cost and deal momentum distort judgment.
The IFKS chain is the product
Intent activates fears. Fears activate signals. Signals roll into verdicts. That Intent -> Fear -> Signal -> Verdict chain is what makes the analysis explainable and thesis-aware.
Three-layer architecture
Knowledge graph, metric sets, and evaluation engine are separated. The computational core stays stable while the analytical coverage keeps expanding.
More than source citation
Every number is citable, but citation is only the trust layer. The deeper value is knowing what that number means for this buyer, under this metric set, at this sufficiency state.
Industrial analytical coverage
The engine runs 53 calculators, 15 qualitative modules, and external enrichment collectors. It is a due diligence system, not a clever prompt.
One extraction, many buyer views
The same extracted data can be evaluated against multiple buyer intents with near-zero marginal cost, because thesis application happens in evaluation, not extraction.
Look at a report your team could actually defend.
The point is not to get an answer faster. The point is to get an answer that survives internal review, committee scrutiny, and follow-up diligence.