01Live
Depreciation
Matches asset-register rows to depreciation rules and flags anything the current ruleset cannot cover.
- Office equipment25%matched
- Special machinery-review
- IT hardware33%matched
AI financial intelligence
Turn financial data into intelligence you can act on.
Finora turns finance exports and spreadsheets into structured, reproducible insights - starting with depreciation, payroll & expense, and R&D tax credits.
Built for finance teams and accounting firms that need numbers they can trace, review and defend.
Latest analysis
Ready to review
Reviewed value₪128.4k
Exceptions23
Rules applied2,418
Coverage94.7%
Findings by category
Priority findings
Unmatched assets require review.
Reproducible
Input + ruleset preserved.
Built around one principle: financial intelligence should be explainable.
Deterministic where it matters
Same input + same rules = same result.
Exceptions are visible
Unknowns are flagged, never hidden.
Designed for review
Trace findings back to rows and rules.
The problem
Financial data is everywhere. Financial intelligence is not.
Most companies already have the data. What they lack is a reliable layer that organizes it, tests it against rules and turns it into decisions.
Disconnected spreadsheets and exports
Critical financial context lives across files, systems and versions.
Manual review repeated every month
Teams spend time rebuilding the same logic instead of reviewing the result.
Rules buried in people, emails and notes
Business logic is difficult to reproduce consistently across the organization.
Answers without an audit trail
When a number changes, it is hard to see exactly why.
With Finoraone intelligence layer
From raw financial data to a picture you can review, explain and act on.
Finora structures the input, applies explicit rules, isolates what does not fit and preserves the context behind every result.
Repeatable analysis
Explicit rules, not black-box guessing.
Visible exceptions
Rows that cannot be evaluated are surfaced.
Traceable results
Know which inputs and rules produced a finding.
Review-ready output
Move from hunting for context to evaluating it.
Live pilot capabilities
One intelligence layer. Three deterministic engines.
Each engine evaluates row-level financial data against defined logic and shows what matched, what did not and what needs review.
02Live
Payroll & Expense
Compares payroll and expense rows with defined thresholds and surfaces exceptions that fall outside policy or logic.
- Travel expense₪1,850review
- Software₪420clear
- Payroll item₪7,900review
03Live
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 hostingeligiblemapped
- Contractorreviewcheck
- Supplieseligiblemapped
Sample rows and rates are shown for illustration only. They are not tax advice.
One standard across every engine
Same file + same ruleset + same code = same result.
How it works
From spreadsheet to a number you can explain.
Finora is designed to make the analysis process visible, not magical.
Upload
Use the CSV or Excel export you already have.
Map
Confirm required columns. If data is missing, Finora stops instead of guessing.
Analyze
Run deterministic engines against the selected ruleset.
Review
Inspect findings, exceptions and the trail behind every result.
Column mapping
- Asset ID
- asset_id
- Purchase date
- purchase_date
- Cost
- cost
- Asset type
- asset_type
4 / 4 required fields mapped
Depreciation analysis
Run analysis
Rows2,418
Matched2,289
Review129
| Asset | Cost | Rule | Status |
|---|---|---|---|
| IT equipment | ₪14,000 | 33% | Matched |
| Machinery | ₪82,500 | - | Review |
| Office fitout | ₪31,200 | 10% | Matched |
Trust by design
AI for complexity. Deterministic rules for numbers.
Finora preserves the context behind each analysis so a professional can understand how a result was produced and where the engine could not evaluate a row.
“The goal is not to replace judgment. It is to make the evidence behind judgment easier to see.”
Input fingerprint
Know which file produced the run.
Ruleset version
Know which logic was applied.
Code version
Know which build produced the result.
Flagged exceptions
Know where professional review is required.
- 01
Input file
Source export and file fingerprint.
- 02
Ruleset
Versioned financial logic.
- 03
Analysis
Deterministic engine execution.
- 04
Findings
Reviewable output and exceptions.
- Exceptions are flagged, not hidden
- Required data validated before analysis
- Results can be re-run against the same input
Business value
Find what deserves attention before it becomes expensive.
The first value comes from making financial data easier to examine - opportunities, anomalies, quality issues and the evidence behind a decision.
01Surface
Tax opportunities
Surface records that may deserve tax-treatment review instead of relying on manual sampling.
Depreciation / R&D02Detect
Expense exceptions
Identify rows that fall outside defined policy, thresholds or expected structures.
Payroll / Expense03Improve
Data quality
Catch missing fields and unmapped categories before they distort the result.
Mapping / Validation04Explain
Decision-ready evidence
Move from a headline number to the rows, rules and exceptions that sit behind it.
Findings / Audit trailThe Finora layer
Not another dashboard. An intelligence layer for financial data.
Finora sits between raw finance exports and the decisions people need to make from them. The platform organizes, validates, analyzes and explains - while keeping the underlying logic visible.
Intelligence Builds Wealth. Better financial decisions start with clearer, more trustworthy information.
Finora
Intelligence
Layer Tax Data quality Expenses R&D
Intelligence
Layer Tax Data quality Expenses R&D
AI financial intelligence
Turn financial data into clear, actionable intelligence.
A structured intelligence layer for financial data - designed to surface what matters, explain why, and support better business decisions.
Built for clarity. Designed for review.
Rows2,418
Matched2,289
Review129
Illustrative interface. Figures are examples, not real client data.
One principle: financial intelligence should be explainable.
Deterministic where it matters
Same input + same rules = same result.
Exceptions stay visible
Unknowns are flagged instead of hidden.
Designed for review
Trace findings back to rows and rules.
The problem
Financial data is everywhere. Financial intelligence is not.
Most companies already have the data. What they lack is a reliable layer that organizes it, tests it against rules and turns it into decisions.
Disconnected spreadsheets and exports
Critical context is scattered across files, systems and versions.
Manual review repeated every month
Teams rebuild the same logic instead of reviewing the result.
Rules buried in people and notes
Financial logic becomes hard to reproduce consistently.
Answers without an audit trail
When a number changes, it is hard to see exactly why.
With Finora
One intelligence layer across the financial picture.
Structure the input, apply explicit rules, isolate what does not fit, and preserve the context behind every result.
Repeatable
Explicit logic, not black-box guessing.
Visible
Exceptions surfaced clearly.
Traceable
Inputs and rules stay connected.
Review-ready
Move from hunting to evaluating.
Live pilot capabilities
One intelligence layer. Three deterministic engines.
Each engine evaluates row-level financial data against defined logic and shows what matched, what did not and what needs review.
01Live
Depreciation
Matches asset-register rows to depreciation rules and flags anything outside the active ruleset.
- Office equipment25%matched
- Machinery-review
- IT hardware33%matched
02Live
Payroll & Expense
Compares payroll and expense rows with defined thresholds and surfaces exceptions.
- Travel expense₪1,850review
- Software₪420clear
- Payroll item₪7,900review
03Live
R&D Tax Credits
Maps expense categories such as wages, contractors, supplies and cloud hosting to R&D rules.
- Cloud hostingeligiblemapped
- Contractorreviewcheck
- Supplieseligiblemapped
Sample rows and rates are shown for illustration only. They are not tax advice.
One standard across every engine
Same file + same ruleset + same code = same result.
How it works
From spreadsheet to a number you can explain.
The analysis process stays visible from input to finding.
Upload
Use the CSV or Excel export you already have.
Map
Confirm required columns. Missing data stops the run instead of being guessed.
Analyze
Run deterministic engines against the selected ruleset.
Review
Inspect findings, exceptions and the trail behind every result.
Column mapping
- Asset ID
- asset_id
- Purchase date
- purchase_date
- Cost
- cost
- Asset type
- asset_type
4 / 4 required fields mapped
Depreciation analysis
RunRows2,418
Matched2,289
Review129
Trust by design
AI for complexity. Deterministic rules for numbers.
Finora preserves the context behind each analysis so every result can be understood, reviewed and reproduced.
“The goal is not to replace judgment. It is to make the evidence behind judgment easier to see.”
Input fingerprint
Know which file produced the run.
Ruleset version
Know which logic was applied.
Code version
Know which build produced the result.
Flagged exceptions
Know where professional review is required.
- 01
Input
Source export and fingerprint.
- 02
Ruleset
Versioned financial logic.
- 03
Analysis
Deterministic execution.
- 04
Findings
Reviewable output and exceptions.
The bigger picture
A financial intelligence layer across the business.
Finora is designed to grow beyond individual analyses into a consistent layer that organizes, tests and explains financial data across the company.
Pilot program
Bring your real financial data. See what Finora can reveal.
Start with one focused analysis, validate the output with your team, and measure the value before expanding.
Request pilot accessFocused scope. Real data. Reviewable output.
Pilot program
Prove the value on your own data.
Finora is in an early pilot with a small number of finance teams and accounting firms. Bring one real file. Run one workflow. Review the evidence together.
- Select a workflow that matters.
- Map the data and run the engine.
- Review findings and exceptions.
- Decide whether the value is worth scaling.