AI can summarise a financial document.The question is whether your committee can defend a decision based on that summary.
Deterministic, not stochastic.
Ask an LLM the same question twice. You may get different answers. Run the same document through IACalc twice. Identical verdict. Reproducibility is the minimum for institutional governance.
Computation, not summary.
A summary tells you what the document says. A computation tells you what the numbers mean. The Beneish M-Score is eight indices with documented thresholds — not an opinion. IACalc runs 53 of these. An LLM runs zero.
Intent-aware, not generic.
An LLM doesn’t know you’re screening for yield. It doesn’t know your committee treats 35% customer concentration as a hard stop. IACalc calibrates every signal to your thesis. Change the intent, change the verdict.
Gaps, not hallucinations.
When data is missing, an LLM fills the hole with plausible fiction. IACalc reports NOT ASSESSABLE and tells you exactly what’s absent. A missing number is more useful than an invented one.
Provenance, not confidence.
Every value traces to a source page, table, and cell. Every threshold cites an academic paper or regulatory framework. The audit trail isn’t bolted on — it’s the architecture.
IACalc uses LLMs — for extraction, narrative generation, and counter-narrative analysis. What it doesn’t do is let them make the analytical judgment. The computation is deterministic. The thresholds are documented. The verdict is reproducible. That’s the difference between a tool your committee can use and one it can’t.
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.