Meaning, paragraph and number changes

Semantic Document Compare

Compare two documents locally, align similar paragraphs, isolate additions/removals and list changed numeric tokens.

✓ Deterministic local baseline✓ Evidence per finding✓ JSON + CSV report✓ Versioned method
Interactive tool

Analyse your own file

Local by default

Choose the original document

Drop it here or browse this device

No file selected

Choose the revised document

Drop it here or browse this device

No second file selected

Your original file is analysed in this browser and is not uploaded. An optional AI second-opinion button appears only after the local report; if you choose it, extracted text—not the original file—is sent to our server.

Processing manifest

What happens to the file

Original stays on this device
Default processing
Browser-only JavaScript, PDF.js, pdf-lib and Tesseract where the task needs them
Accepted input
PDF and text-based documents
Report contract
File Intelligence JSON schema 1.0 plus a flattened CSV view
Optional AI
Explicit second opinion; extracted text only; original file is not uploaded
Example analysis report

Synthetic service terms change notice period from 30 to 45 days, price from EUR 120 to EUR 135 and add a backup clause.

Download compare-before.txtDownload compare-after.txtRun it with “Load published sample” above to reproduce the report.

How the result is calculated

Text is extracted, split into paragraphs, represented as term-frequency vectors and aligned greedily above a cosine-similarity threshold. Exact numeric tokens are compared separately so a small price/date change is not hidden by high overall similarity.

Published formula and threshold

Overall similarity is cosine(term-frequency vectors) × 100. Paragraphs align at ≥42%; >98.5% is unchanged. These thresholds are published and versioned, not learned from user files.

Supported and unsupported cases

Input or situationWhat version 1.0.0 checksWhat it does not claim
Text/selectable PDFParagraph additions, removals, similarity and numeric-token changesPixel/layout differences
Reordered similar clausesBest unmatched paragraph alignmentReliable legal equivalence
Tables represented as textSome row/number changesMerged cells or spatial relationships
AI summaryOptional narrative of deterministic evidenceSubstitution for legal review
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What the confidence score means

Confidence describes how much of the file the published parser could inspect and how specific the evidence was. It is deliberately separate from the result score. A 95/100 confidence value does not mean “95% likely to be true,” and a high health/readiness score does not certify safety, compliance or factual correctness.

Comparison with other approaches

ApproachUseful strengthImportant limitation
Local semantic compareLocal paragraph alignment plus numeric changesNot a legal redline
Line diffExact and deterministicNoisy when paragraphs wrap or move
Visual PDF compareFinds layout/pixel changesCan miss meaning or treat reflow as change
Word processor track changesBest when edit history existsRequires compatible editable sources
Anonymous synthetic release dataset

5/5 fixtures passed (100%)

Dataset: FH-INT-SYNTH-2026-07. Scope: Aligned change, addition, removal, numeric-token change and identical-control fixtures. Fixtures contain generated data only; no visitor file or personal record is collected.

Open method and raw CSV

When this result can be wrong

Boilerplate can inflate similarity, word order is weakly represented, a negation may look deceptively similar, and extracted PDF reading order can be wrong. Review every changed paragraph and number.

Keep the original file, test the output in the receiving application and escalate high-stakes legal, financial, medical, accessibility or security decisions to a qualified reviewer.

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Version and accuracy change log

ReleaseDateCalculation/coverage changeRe-test requirement
1.0.02026-07-29Initial public engine, report schema, thresholds, synthetic fixtures and sample files.All published fixtures and one browser sample per tool.

Downloadable evidence, not a black-box verdict

After analysis, use Download JSON report for structured evidence or Download CSV report for review in a spreadsheet. Both include engine version, timestamp, score, confidence, metrics, findings, recommendations and limitations. Sensitive-data reports mask candidate values and no report includes the complete extracted document text.

Related tools and independent checks

Method or accuracy correction? Read the testing methodology and contact info@brothers.net.tr. Material scoring changes are published in the change log.