For finance, risk and operations teams
Cut review time. Never lose control.
Zarene AI takes the slow, repetitive parts of financial operations off your team’s desk — reconciliations, statement reviews, credit checks — and hands back the hours. Every figure is checked before anyone sees it, and nothing goes out until someone on your team signs it off.
- No credentials that can move money or alter records
- Every figure traceable to its source
- Tamper-evident audit ledger
- Deploys into your own cloud
What lands on a reviewer’s desk
The product, shown with synthetic data
Statement analysis · FY2024 · Draft 3
| Line item | Value |
|---|---|
| Cash and equivalents | |
| Trade receivables | |
| Investment securities | |
| Other assets | |
| Total assets | |
| Total liabilities | |
| Total equity | |
| Total liabilities and equity |
Provenance
Cash and equivalents
- Source
- Page 41
- Extraction confidence
- 99%
- Checked by
- Footing check
1 validation failure must be resolved before release.
An analysis ready for sign-off. All figures are synthetic. Select any figure to see exactly where it came from.
Zarene AI by the numbers
- 0
- credentials that can move money or alter records
- 1 click
- from any output to its source
- 6
- workstreams across financial operations
- 100%
- of released decisions reconstructable from the ledger
How the work moves
The same checkpoints your team already trusts. Just faster.
Every case moves through the same stages a careful reviewer would already insist on — read, calculate, check, approve. We remove the manual toil from each one without changing who is accountable for the result.
- 01AI
Extract
A vision-language model reads the document and returns labelled fields, each with a page reference. It transcribes. It does not calculate.
- 02Deterministic code
Validate
Does the arithmetic hold. Do the derived figures agree with the underlying records they claim to summarise. Are the labels consistent across every table. Failures are raised here, before a human reads a draft.
- 03Deterministic code
Compute
Every total, ratio and variance is computed in typed code from validated inputs. Same inputs, same outputs, every time, with or without a model provider.
- 04AI
Narrate
The model writes commentary from figures that already exist. Its output is checked against the computed set; any figure that does not match a computed value is rejected before the draft is shown.
- 05Human
Approve
A named user approves. The approval records who, when, which draft and which engine version. Until then, nothing leaves the platform.
The kind of error that gets missed
In a live customer’s working papers, a table carried two correct figures in the wrong columns.
Both numbers were right. Both appeared, word for word, in the source document. Most review tools only check that a figure appears somewhere in the source — every one of them would have passed that page. Catching an error like this means recomputing the figure from where it actually belongs, not just confirming it exists.
| Prior period | Current period | |
|---|---|---|
| As printed | 1.42 | 1.18 |
| As computed | 1.18 | 1.42 |
This is the kind of gap that should concern anyone signing off on an automation programme. Nothing here was invented — the document itself was internally inconsistent, and a surface-level check has no way to see that.
Two further errors surfaced in the same set: a statement that did not foot, and a line reported as zero against non-zero adjustments elsewhere in the same document. All three were caught before a reviewer ever saw the draft.
Built for financial operations
Six workstreams. One platform.
Every workstream below is available today, built the same way and held to the same standard: read by AI, checked and computed in code, released only under a human signature.
A short call, not a sales cycle.
Bring one process you would like to see automated and one you would not. Thirty minutes is usually enough to tell whether this is a fit.