01 · DepreciationLive
Depreciation
Matches asset-register rows to depreciation rules and flags anything the current ruleset cannot cover.
| IT hardware | 33% | matched |
|---|---|---|
| Machinery | - | review |
| Fitout | 10% | matched |
Financial intelligence built for control
Turn financial data into decisions you can defend.
Finora structures, validates and analyzes financial data so teams can find what deserves attention - with the rule, the figures and the exceptions behind every result.
- Deterministic pilot engines
- Flagged exceptions
- No silent guessing
Illustrative product view
Review center
Depreciation analysis
Rows processed2,418
Evaluated2,289
No applicable rule129
FindingNeeds review
Laptop - Dell Latitude₪6,200Book 15%Rule 33%
Why this was flagged
Book rate is below the rate in the applied rule.
The row is compared directly with an explicit rule. Where no rule applies, Finora does not infer a rate - the row is counted and flagged for professional review.
3 live enginesCurrent pilot capabilities
CSV + ExcelUse exports you already have
Explicit rulesSame input, same result
Review-readyFindings and exceptions
The real problem
Financial data is everywhere. Financial intelligence is not.
Most companies already have the data. The hard part is turning fragmented exports, policies and financial logic into a result that can be reviewed and trusted.
Disconnected spreadsheets and exports
Context is spread across systems, files and versions.
Manual review rebuilt every month
Teams repeat the same work instead of reviewing the result.
Rules buried in people and notes
Logic is difficult to reproduce consistently.
Answers without evidence
A number is not enough if no one can explain why it changed.
Finora turns the review process into a repeatable intelligence layer.
Structure the input. Apply explicit logic. Isolate uncertainty. Show the figures behind every finding.
Repeatable analysis
Same input + same rules + same code = same result.
Visible exceptions
Unknowns are flagged instead of guessed.
Explicit findings
Each finding shows the figures and the rule behind it.
Review-ready output
Spend time evaluating context, not rebuilding it.
Live pilot capabilities
Three deterministic engines. One standard of evidence.
Each engine evaluates row-level financial data against defined logic and shows what matched, what did not and what needs review.
02 · Payroll & ExpenseLive
Payroll & Expense
Compares payroll and expense rows with defined thresholds and surfaces exceptions outside policy or expected structure.
| Software | ₪420 | clear |
|---|---|---|
| Travel | ₪1,850 | review |
| Payroll | ₪7,900 | review |
03 · R&D Tax CreditsLive
R&D Tax Credits
Maps expense categories such as wages, contractors, supplies and cloud hosting to R&D rules and flags what does not fit.
| Cloud hosting | Eligible | mapped |
|---|---|---|
| Contractor | Review | check |
| Supplies | Eligible | mapped |
Illustrative sample rows and rates are for demonstration only. Results do not constitute tax or financial advice.
Product moment
Don’t just show a number. Show the evidence behind it.
Every finding should answer three questions: what was found, which rule produced it and what the engine could not evaluate.
From exception to explanation in one review flow.
Finora is designed to make financial analysis easier to inspect. The results view keeps each finding, the rule it was compared against and the rows that could not be evaluated together.
- 1
See what needs attention
Prioritize exceptions instead of scanning every row.
- 2
Understand the reason
See the rate or threshold each row was compared against.
- 3
Know what was not evaluated
Rows without an applicable rule are counted and flagged. Skipped rows come with a reason.
Finora · Review workspaceIllustrative interface
Rate below rule
Depreciation · Finding
AssetLaptop - Dell Latitude
Cost₪6,200
RiskHigh
Comparison
| Book rate | Rule rate | Gap | Direction |
|---|---|---|---|
| 15% | 33% | 18 pts | Under |
Applied logic
The asset matched an explicit depreciation rule. The book rate is compared directly with the rule rate. No rate is inferred for rows without an applicable rule.
129 rows had no applicable rule and are flagged for review.
Trust by design
Financial analysis should be explainable and reproducible.
The current pilot engines use explicit, deterministic rules. Run the same file again and you get the same result.
Complexity made clear.
The goal is not to hide uncertainty behind a confident-looking score. If the data is missing or the rule does not apply, the system should say so.
Design principle: deterministic where numbers require certainty; assistive intelligence only where it can be validated without compromising traceability.
No black-box tax conclusions. Professional judgment stays visible in the workflow.
01
Deterministic calculations
Explicit rules. No model guesses the numbers.
02
Explicit logic
Each finding shows the rate or threshold it was compared against.
03
Flagged exceptions
Rows without an applicable rule are counted and flagged for review.
04
Required fields checked
Analysis stops when a required column is missing.
Same input + same rules + same code = same result.
Where AI fits
AI is the direction. Trust is the constraint.
Finora is a financial intelligence platform built to turn complex financial data into clear, actionable insights.
Today, Finora’s pilot calculations are deterministic and reproducible. AI-assisted capabilities are being developed to enhance analysis, prioritization and decision support — without replacing the deterministic calculation layer.
What that means today
We do not label deterministic calculations as AI. AI-assisted capabilities are being developed to enhance analysis, prioritization and decision support — while keeping the deterministic calculation layer intact.
Deterministic calculations
Current pilot engines use explicit rules and reproducible execution.
Assistive intelligence over time
AI can help with interpretation, prioritization and workflow support where outputs can be reviewed.
Business value
Find what deserves attention before it becomes expensive.
The first value is not automation for its own sake. It is a clearer way to identify opportunities, exceptions, data-quality issues and the evidence behind a decision.
01 · Surface
Tax opportunities
Surface records that may deserve tax-treatment review instead of relying on manual sampling.
Depreciation / R&D02 · Detect
Expense exceptions
Identify rows that fall outside defined policy, thresholds or expected structures.
Payroll / Expense03 · Improve
Data quality
Catch missing fields and unmapped categories before they distort the result.
Mapping / Validation04 · Explain
Decision-ready evidence
Move from a headline result to the findings, rules and exceptions behind it.
Findings / ExceptionsFAQ
Questions serious teams should ask.
Financial software earns trust by being specific about what it does - and what it does not do.
- Are the current analysis engines AI?
- No. Pilot calculations are deterministic and reproducible. AI-assisted capabilities are being developed to support analysis and prioritization, without replacing the deterministic calculation layer.
- What happens when the data is incomplete?
- Required fields are validated before analysis. Rows without an applicable rule are counted and flagged for review instead of being silently inferred.
- Does Finora provide tax or financial advice?
- No. Finora organizes and analyzes data to support review. Illustrative outputs do not replace professional tax, accounting or financial judgment.
- How is pilot data handled?
- Access, storage, retention and deletion expectations are agreed before analysis begins. The pilot should start only after those handling rules are clear to both sides.
Pilot program
Prove the value on a focused workflow.
Start with one representative financial dataset. Run one workflow that matters. Review the evidence together before deciding whether to expand.
- 1 · SelectChoose a workflow worth testing.
- 2 · MapConfirm the required fields.
- 3 · AnalyzeRun the deterministic engine.
- 4 · ReviewEvaluate findings and exceptions.
Before any pilot analysis: data access, storage, retention and deletion expectations are agreed with the customer.