AI-directed forensic analysis built on high-performance C++ computational engines, incorporating Burrows' Delta stylometric processing, Benford's Law statistical pipelines, and cross-document verification matrices capable of processing 124,750+ claim pairs per 500 pages. Machine learning pattern recognition integrated with Daubert-compliant forensic methodologies delivers the computational power to examine everything, miss nothing, and produce court-ready investigative packages that manual review teams simply cannot match.
Every AI-generated finding is verified by trained analysts, with complete methodology transparency, known error rates, and peer-reviewed foundations - the analytical and investigative firepower of artificial intelligence, constrained by the evidentiary standards courts demand.
Why we find what barristers miss: detection finds issues, investigation proves they cannot be explained, cross-examination weaponises them, and ranking separates supporting evidence from binary proof.
Exhaustive scan of all case materials identifying every inconsistency, variance, and gap. One sentence per issue.
Deep analysis proving each detection cannot be innocently explained. Compound arguments connecting multiple sources.
Attack your own client's case using the same methodology. Find vulnerabilities before the adversary does.
Analyse each vulnerability, develop prepared responses, and strengthen weak positions with supporting evidence.
Generate questions using adversary's own documents. Each question designed to expose irreconcilable positions.
Classify findings on 1-4 scale from supporting evidence to binary proof. Focus counsel on what matters.
Distil 400,000+ word analysis into 50+ pages containing only binary proof that cannot be refuted.
Traditional document review identifies "areas of concern" without proving they matter. Our multi-stage system finds issues, proves they cannot be explained away, anticipates counterarguments, weaponises findings for cross-examination, and ranks everything by litigation impact.
Barristers have 3-4 hours to review a 500-page case file before chambers conference. We spend 40-50 hours with computational tools examining every claim pair, every timeline conflict, every figure variance. Then we rank findings so counsel knows immediately which three points win the case.
Not all findings have equal impact. We classify evidence from supporting material to binary proof.
| Level | Classification | Criteria | Example |
|---|---|---|---|
| 4 | Binary | No innocent explanation possible. Direct contradiction with documentary proof. Material to case outcome. | Claimant states under oath event occurred 10 June. Email from claimant dated 8 June references event as "last week." |
| 3 | Material | Extremely weak explanation available. Significantly damages credibility. High litigation impact. | Same item claimed at materially different amounts across multiple documents with no explanation for variance. |
| 2 | Strong | Plausible explanation exists but problematic. Supports pattern of issues. Useful in compound arguments. | Invoice dated after claimed payment date. Possible explanation: invoice date error. Supports larger timeline analysis. |
| 1 | Supporting | Contributes to overall pattern. May have innocent explanation. Adds weight to stronger findings. | Minor inconsistency in peripheral detail. Not independently significant but reinforces credibility concerns. |
A typical 500-page case generates 2,000-4,000+ detections. Exhaustive analysis produces volume - ranking produces focus. Our system separates:
The Case Summary contains only Level 4 and Level 3 findings - the 50+ pages of binary proof distilled from the 1,400+ page full report.
Systematic approaches to forensic document analysis, each producing documented, citable findings.
Systematic comparison of claims, figures, and assertions across pleadings, correspondence, contracts, and witness statements. Each statement indexed and cross-referenced to identify inconsistencies, contradictions, and claim evolution patterns.
Chronological mapping of documented events, communications, and claimed occurrences. Analysis identifies sequence inconsistencies, temporal impossibilities, and gaps in the documentary record.
Court-accepted authorship attribution using Burrows' Delta analysis. Writing style fingerprinting, vocabulary distribution comparison, quantitative similarity scoring. Daubert-compliant methodology with 90%+ accuracy in controlled conditions.
Statistical analysis of financial data using Benford's Law, anomaly detection, and fraud pattern recognition. Every number examined. Every calculation verified. Every irregularity documented.
Cross-statement comparison identifying contradictions, claim evolution, and narrative inconsistencies. Factual documentation of differences with source citations - ultimate determination reserved for finder of fact.
Systematic cataloguing and organisation of findings into structured formats suitable for legal proceedings, including indexed citations, cross-reference tables, and exhibit preparation.
Structured approach from document intake to final deliverables.
Secure receipt and cataloguing of pleadings, correspondence, financial records, and witness statements.
Comprehensive indexing of claims, figures, dates, and assertions with source citations.
Systematic application of forensic methods to indexed data with documented findings.
Quality review of all findings, citation verification, and methodology documentation.
Final evidence package with executive summary, detailed findings, and supporting exhibits.
Court-admissible forensic mathematics. Every test produces quantified confidence levels with academic citations.
Comprehensive digit frequency analysis applying established forensic accounting methodology. Naturally occurring financial data follows predictable mathematical distributions first documented by Newcomb (1881) and formalised by Benford (1938). Fabricated or manipulated data consistently deviates from these patterns.
Multiple independent tests quantify the probability that observed patterns occurred by chance. Results expressed as confidence levels admissible in court proceedings with full methodology documentation.
Results classified per Dr. Mark Nigrini's established forensic accounting standards, widely accepted in fraud litigation. Each dataset receives a conformity rating with supporting statistical evidence.
Multi-method anomaly identification using complementary statistical approaches. Flagged values cross-referenced against case documentation to assess legitimacy.
Targeted analysis for common manipulation techniques documented in forensic accounting literature. Each pattern tested independently with statistical significance assessment.
Every statistical analysis produces court-ready documentation with full methodology transparency. Results designed for expert witness presentation and cross-examination resilience.
Established forensic linguistics methods accepted under Daubert. Every statement examined. Every pattern documented.
Authorship attribution using quantitative computational linguistics. Admissible under Daubert with documented methodology and known error rates. Key precedents: Unabomber case (FBI), David Hodgson case (UK, 2008).
Objective comparison of multiple statements identifying contradictions and inconsistencies. Framed as factual comparison - determination of significance reserved for finder of fact.
Analysis of checkable vs uncheckable detail ratios. Research indicates accounts of experienced events contain higher proportions of verifiable details. Used to identify areas warranting further investigation.
We deliver Daubert-compliant evidence packages designed to survive cross-examination. Documented methodology. Quantified confidence levels. Precise source citations. Everything a litigation team needs to dominate discovery and depositions.
Exhaustive analysis at scales no manual review team can approach.
Every claim compared against every other claim. A 500-page case file contains 124,750 potential claim pairs. We cross-reference every one. Manual review teams pick samples - we examine everything.
Human accuracy drops after 2-3 hours. Our systems maintain consistent analytical precision across 10,000+ pages. We never tire. We never lose focus. We never miss a pattern because we're on page 847 and it's late.
Statistical patterns - Benford's Law violations, rounding bias, threshold clustering, behavioral markers - require analysis of entire datasets. Human spot-checking cannot detect patterns distributed across thousands of data points. We see the patterns invisible to sequential reading.
Every indexed claim retrievable instantly. Precise citation to document, page, and paragraph. When a witness says something that contradicts page 412 of Exhibit C, we find it. Every time.
AI-powered systems generate findings. Trained analysts verify, contextualise, and apply judgment. Every analysis undergoes quality review before delivery. We identify what exists in the documentary record - trained analysts determine what it means for the case.
Evidentiary standards applied to all analysis and deliverables.
Every finding includes complete source citations: document name, page reference, paragraph number, and date. All assertions traceable to original documentation.
Analysis methods documented in detail sufficient for independent verification. Each conclusion includes the analytical process by which it was reached.
Findings categorised by confidence level based on corroboration, source reliability, and analytical certainty. Clear distinction between established facts and analytical conclusions.
Analysis scope and limitations documented. Gaps in the documentary record identified. Conclusions qualified where evidence is incomplete or ambiguous.
Court-ready evidence packages structured for litigation use.
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