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Financial reviews where every figure is verified before you present it

Spend, margin, cash and runway, turned into a review deck for a finance audience. Derived metrics are calculated in code — the AI narrates them, it never invents them.

See a finance-style deck built from sample cash and margin data.

The problem

Sound familiar?

The audience checks the arithmetic

A finance audience will recompute your margin on the spot. A deck that can't survive that is worse than no deck.

Generative AI and money don't mix

A plausible-looking runway figure is not a runway figure. For anything cash-related, plausible is precisely the failure mode.

Month-end leaves no time for slides

The reporting window is the same days as the close. Deck building is the part that gets squeezed, and it shows.

How it works

Three steps, minutes of work

1

Upload the close output

Your P&L summary, spend by category, margin, cash balance and burn — CSV or Excel, multi-sheet workbooks included.

2

Confirm the read before anything generates

Review how each line was interpreted, with confidence scores, and correct the finance-specific ones. Nothing is generated until you approve.

3

Present, then hand over the workbook

Export the deck alongside a verification workbook tracing every figure to its source cell — so 'where did this come from?' has a one-click answer.

sample_saas_metrics.csv → Executive deck

Slide 2 of 8

Revenue momentum: MRR up 42% across H2

Monthly recurring revenueJul–Dec 2024
$126KJulAugSepOctNovDec

MRR · Dec 2024

$125,550

Verified · row 24

Churn · Dec 2024

1.8%

Verified · row 24

Every figure on this slide traces to a cell in the uploaded file — validated in code before export.

Runway, burn, margin and growth rates are computed deterministically from your data before the AI sees them, and every figure in the output is diffed against your source file in code. A number that isn't in your data cannot appear on a slide.

Real output

A financial reviews deck this produced

Generated from 24 months of SaaS metrics (MRR, ARR, churn, cash) under the Financial review preset — unedited, with every figure validated against the source file.

EXECUTIVE SUMMARY

Growth and retention are strong; cash runway is the constraint.

Recurring revenue up 597.5% — ARR reached $1.51M, compounding at 8.8% monthly

Unit economics outperformed — LTV:CAC improved to 12.7x as churn fell 66.7%

Liquidity risk rising — Cash runway now 4.3 mo at a $171.53K monthly burn

$1.51M
ARR
+597.5% vs. Jan 2023
1.8%
Churn Rate
down from 5.4%
$743.89K
Cash on Hand
-17.3% vs. Jan 2023
4.3 mo
Runway
at current burn
InsightNarrative • Confidential2 / 9
Revenue Growth

ARR compounded at 8.8% monthly, reaching $1.51M

Net ARR added: $1.29M over 24 months

Average monthly ARR growth: 8.8% compounded

No months of negative growth or contraction

ARR increased by $1.29M over 24 months, with no reversals.

InsightNarrative • Confidential3 / 9

FAQ

Financial reviews — common questions

Is the runway or burn figure calculated by the AI?+

No. Cash runway, burn, growth rates and margin are calculated in application code directly from your data, then supplied to the model as fixed inputs. The model's job is the narrative around them. Any number it returns is separately diffed against your file before the slide is built.

Can I show the finance team exactly where each number came from?+

Yes — that's what the verification workbook is for. It's an Excel export listing every figure on every slide with the source cell it traces to, and it honestly marks anything it cannot trace rather than passing it off as verified.

Does it handle multi-sheet financial workbooks?+

Yes. Each tab in an Excel workbook is ingested as its own data source, so a workbook with separate P&L, cash and headcount tabs can drive a single deck with lineage preserved per source.

Put verified AI reporting to the test

Upload your data and check the output against your own spreadsheet. Every number in the deck is validated in code — that's the point.