Knogin
AI Partners Platform

AI That Survives Cross-Examination

Ensemble reasoning. Cryptographic provenance. Court-grade output.

Five specialized AI partners working in concert, delivering investigative intelligence that holds up under legal scrutiny. Every conclusion backed by multi-model consensus and immutable audit trails.

CJIS-ReadySOC 2 Controls200+ Agencies
Ensemble Reasoning Active
QUERY
Reasoning
Fast
Vision
Specialist
Consensus Score87%
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The Accountability Crisis

When AI Becomes a Liability

AI-assisted investigation faces an accountability crisis. Documented failures have led to wrongful arrests, discontinued programs, and growing legal liability.

Robert Williams spent 30 hours in a Detroit jail because a facial recognition algorithm said he was the 9th-best match. Nijeer Parks was jailed for 10 days for a crime in a city he had never visited. Porcha Woodruff was arrested while eight months pregnant based on a computer saying 'possible match.'

These aren't edge cases. They represent a systematic failure in how AI-assisted investigation operates. Single-model outputs treated as definitive identifications. No mechanism to surface uncertainty. No requirement to generate alternative hypotheses. No way to prove what the AI actually saw versus what humans interpreted.

7+

Documented Wrongful Arrests

from facial recognition AI alone

40+

Programs Discontinued

predictive policing tools abandoned

15-30%

Hallucination Rate

in unsupervised LLM outputs

Key Insight

The failure wasn't the technology, it was the architecture. Single-model outputs with no validation, no provenance, and no mechanism to surface alternative explanations.

These incidents reveal systematic gaps that no existing platform addresses. Understanding these gaps is essential to understanding why a different approach is required.

Documented Failures

When AI Creates Liability

Each incident exposes a specific architectural gap that Argus Partners Platform addresses directly.

Timeline of AI failures in law enforcement

Wrongful Arrest
202030 hours detained

Robert Williams

Detroit, MI

Detained 30 hours after facial recognition matched his expired driver's license to surveillance footage. He was only the 9th-best match. June 2024 settlement requires Detroit PD to audit all cases since 2017.

Gap Exposed

Single algorithm output treated as identification. No mechanism to surface he was only the 9th-best match. Human investigators never saw the confidence ranking.

Argus Answer

Ensemble reasoning across multiple models would have shown weak consensus, automatically triggering additional investigation requirements before any arrest action.

Source: ACLU v. Detroit PD (2024)
Wrongful Arrest
201910 days jailed

Nijeer Parks

New Jersey

Jailed 10 days for a crime in a city he had never visited. The only evidence linking him to the crime was a facial recognition 'possible hit.'

Gap Exposed

No counter-hypothesis generation. 'Possible hit' presented as definitive without mechanism to explore alternatives.

Argus Answer

Adversarial validation generates alternative explanations that must be explicitly ruled out before action. Counter-hypotheses become required investigation steps.

Source: Parks v. Woodbridge PD
Wrongful Arrest
2023Arrested 8mo pregnant

Porcha Woodruff

Detroit, MI

Arrested at her home in front of her children while eight months pregnant, based on facial recognition matching her to a robbery suspect.

Gap Exposed

Pattern of repeated failures indicates systematic architectural issues, not isolated incidents. Same failure mode as Williams case years earlier.

Argus Answer

Cryptographic provenance creates immutable records of every AI output and human decision, enabling systematic audit and pattern identification before failures repeat.

Source: Woodruff v. Detroit PD (2023)
Program Discontinued
2018-202240+ cities abandoned

Predictive Policing Collapse

Los Angeles, CA

LAPD and 40+ other departments discontinued predictive policing programs after audits revealed biased outputs and inability to explain predictions.

Gap Exposed

Black-box models provide no explanation for predictions. No mechanism for investigators to understand or challenge AI conclusions.

Argus Answer

Complete reasoning transparency, every AI conclusion includes supporting evidence, alternative hypotheses considered, and explicit confidence bounds. Nothing is black-box.

Source: LAPD Inspector General Report
Evidence Challenged
2024$50M non-renewal

ShotSpotter Controversy

Chicago, IL

Chicago did not renew $50M ShotSpotter contract after studies showed 89% of alerts did not result in evidence of gunfire and concerns about evidence reliability in court.

Gap Exposed

High false positive rates and lack of explainability make AI evidence inadmissible or easily challenged in court proceedings.

Argus Answer

Court-grade output includes proper Bluebook citations, full reasoning chains, and uncertainty quantification suitable for legal proceedings.

Source: Chicago OIG Report (2021)
Evidence Challenged
2023Cases dismissed

Digital Forensics Scrutiny

Multiple jurisdictions

Multiple cases saw digital forensic evidence challenged or dismissed due to inability to explain extraction methodology and lack of chain-of-custody documentation.

Gap Exposed

Traditional digital forensics lacks the cryptographic verification necessary to prove evidence integrity throughout the investigative lifecycle.

Argus Answer

SHA-256 content hashing and Merkle tree verification creates tamper-evident provenance chain from evidence creation through court presentation.

Source: Various court proceedings
Product Paused
2023AI ethics concerns

Axon Draft One Pause

United States

Axon paused AI-generated police report tool after ethics board resignations and concerns about AI writing reports without sufficient human oversight.

Gap Exposed

Single-model AI generating official documents without validation creates both accuracy and accountability concerns.

Argus Answer

Multi-model ensemble ensures no single AI generates final output. Human-in-the-loop design maintains accountability while leveraging AI capabilities.

Source: NPR/Wired reporting
System Failure
2023Sanctions imposed

Legal AI Hallucinations

New York, NY

Attorney sanctioned for citing non-existent cases generated by ChatGPT, highlighting LLM hallucination risks in legal and investigative contexts.

Gap Exposed

Unsupervised LLM outputs hallucinate citations, facts, and conclusions with no mechanism for validation or verification.

Argus Answer

Ensemble reasoning with adversarial validation catches hallucinations through cross-model disagreement detection. No single model can introduce fabricated information unchallenged.

Source: Mata v. Avianca (SDNY 2023)
Market Analysis

What Every Platform Is Missing

Current AI investigation tools share fundamental architectural limitations that create the failures documented above.

No Multi-Model Consensus

Gap

Every major platform uses single-model architecture. One AI makes the call.

Real Consequence

Single point of failure. One algorithm's error becomes your liability. No mechanism to surface uncertainty or disagreement.

Related Incidents

Williams, Parks, Woodruff wrongful arrests all stemmed from single-model facial recognition.

Argus Solution

Ensemble Reasoning

Tasks execute across 5+ AI models simultaneously. Consensus scoring identifies agreement; conflicting points surface disagreement requiring human resolution.

No Cryptographic Provenance

Gap

Traditional audit logs can be modified. No immutable proof of what AI actually output.

Real Consequence

Evidence integrity challenges in court. Unable to prove chain of custody from AI output to final document.

Related Incidents

Cellebrite and digital forensic evidence challenged due to provenance gaps.

Argus Solution

Cryptographic Verification

SHA-256 content hashing with Merkle tree verification. Every operation cryptographically signed. Tamper-evident chain from creation to presentation.

No Adversarial Validation

Gap

AI confirms hypotheses rather than challenging them. Tunnel vision by design.

Real Consequence

Confirmation bias amplified by technology. Alternative explanations never surface.

Related Incidents

Nijeer Parks case: 'possible hit' became arrest without exploring alternatives.

Argus Solution

Counter-Hypothesis Generation

Every conclusion accompanied by automatically generated alternative explanations with evidence needed to validate or invalidate each.

Black-Box Operation

Gap

AI provides conclusions without explaining reasoning. Investigators cannot evaluate or challenge.

Real Consequence

Inability to explain AI conclusions leads to evidence challenges and program discontinuation.

Related Incidents

40+ predictive policing programs discontinued due to inexplainable outputs.

Argus Solution

Complete Transparency

Full reasoning chains, supporting evidence citations, confidence bounds, and alternative hypotheses for every conclusion.

Jurisdiction Compliance Gap

Gap

Generic AI tools lack jurisdiction-specific legal and procedural knowledge.

Real Consequence

Evidence gathered may not meet local evidentiary standards or procedural requirements.

Related Incidents

Cases dismissed due to procedural failures in evidence handling.

Argus Solution

Jurisdiction Intelligence

Configurable jurisdictional rule sets ensure all outputs meet local requirements for admissibility and procedural compliance.

The Argus Approach

Architecture Designed for Accountability

Three foundational layers ensure every AI conclusion can be defended, verified, and audited.

Select a layer to explore

Click any architecture layer to see detailed capabilities

How Intelligence Flows Through the Platform

Step 1

Investigation Query

Step 2

Multi-Model Processing

Step 3

Counter-Hypothesis Generation

Step 4

Cryptographic Signing

Step 5

Court-Grade Output

Five Specialized Partners

AI Built for Investigators

Each partner optimized for distinct investigative functions, working in concert through ensemble reasoning.

Interactive Demo

See Ensemble Reasoning in Action

Watch how multiple AI models process the same investigative query and generate consensus.

Sample Query

Analyze the financial connections between XYZ Corporation and entities registered at 123 Shell Company Lane. Identify potential indicators of money laundering.

Reasoning LLM

---

Thinking LLM

---

Fast LLM

---

Specialist LLM

---
Multi-Model Consensus---
Interactive Demo

Cryptographic Evidence Integrity

Every operation creates an immutable, verifiable record. See how tamper-detection works.

Evidence Provenance Chain

Merkle Root: f7a2...3e91

Evidence Created

a7f3...8c21

AI Analysis

b2e1...9d45

Document Export

c4d2...7e63

Digital Signature

e8f9...2a84

SHA-256 Hashing

Every piece of content receives a unique cryptographic fingerprint. Any modification, even a single character, produces a completely different hash.

Merkle Tree Verification

Operations are organized in a tree structure where each level verifies the levels below. Chain integrity can be verified without accessing all data.

Digital Signatures

Every operation is signed by the performing user or system. Non-repudiation ensures accountability for all evidence handling actions.

Deep Dive

Platform Capabilities

Explore the technical capabilities that make court-grade AI investigation possible.

Every investigative query executes across multiple AI models. Consensus scoring quantifies agreement levels and automatically flags conclusions where models disagree, ensuring human review of uncertain findings.

Key Features

  • Parallel execution across 4-6 AI models
  • Real-time consensus calculation with confidence intervals
  • Automatic disagreement detection and flagging
  • Configurable consensus thresholds per task type

Technical Implementation

Models process queries asynchronously via message queue. Results aggregated using weighted voting based on task-specific model performance metrics. Disagreement thresholds configurable per investigation type.

Use Case Example

Financial crime investigation where the Reasoning LLM identifies suspicious patterns but the Thinking LLM disagrees on intent automatically routes to senior investigator for resolution before conclusions are documented.

The platform doesn't just generate conclusions, it generates the arguments against them. For every hypothesis, alternative explanations are automatically created, ensuring investigators consider what a defense attorney would argue.

Key Features

  • Automatic counter-hypothesis generation
  • Evidence mapping for each alternative
  • Explicit documentation of ruled-out theories
  • Defense perspective integration

Technical Implementation

Dedicated adversarial model prompted to argue against primary conclusions. Counter-hypotheses require explicit evidence-based dismissal before conclusions can be finalized in investigation documentation.

Use Case Example

When the system concludes 'Subject A was present at location B,' it automatically generates 'Subject A's device was present but Subject A may not have been' with evidence requirements to distinguish between the two.

Every operation, AI output, human modification, export, access, creates a cryptographically signed record. Evidence integrity is mathematically provable, not just claimed.

Key Features

  • SHA-256 content hashing
  • Merkle tree chain verification
  • Digital signatures for all operations
  • Court-ready provenance documentation

Technical Implementation

Content addressed storage with SHA-256 hashing. Operations recorded in append-only log with Merkle tree structure. Verification can occur without accessing original content. Export includes full provenance chain.

Use Case Example

When evidence is presented in court, the prosecution can demonstrate cryptographically that the document has not been modified since AI analysis, with complete chain of custody documentation.

All outputs formatted for legal proceedings. Bluebook citations, evidence references, confidence bounds, and alternative hypotheses included in standard output format.

Key Features

  • Bluebook legal citation format
  • Evidence chain documentation
  • Confidence interval reporting
  • Alternative hypothesis documentation

Technical Implementation

Briefing Partner trained on legal document corpus with jurisdiction-specific formatting rules. Output templates validated by legal professionals. Confidence bounds calculated from ensemble disagreement metrics.

Use Case Example

Investigation summary automatically formatted with proper case citations, evidence exhibits referenced with chain-of-custody documentation, and explicit uncertainty bounds for each conclusion.

Different jurisdictions have different evidentiary standards, procedural requirements, and legal precedents. The platform adapts outputs to meet local requirements automatically.

Key Features

  • Jurisdiction-specific rule sets
  • Evidentiary standard compliance
  • Procedural requirement validation
  • Automatic format adaptation

Technical Implementation

Jurisdiction configuration includes evidentiary thresholds, required documentation, citation formats, and procedural checklists. System validates outputs against jurisdiction requirements before finalization.

Use Case Example

Evidence handling procedures automatically adjusted when case involves federal charges vs. state charges, ensuring documentation meets appropriate standards for each court system.

The platform automatically identifies conflicts between statements, evidence, and previously documented facts. Inconsistencies are flagged for investigation rather than overlooked.

Key Features

  • Cross-statement consistency analysis
  • Evidence conflict detection
  • Timeline inconsistency identification
  • Automatic investigator alerts

Technical Implementation

NLP extraction of factual claims from all sources. Graph-based consistency checking across extracted claims. Conflicts ranked by significance and routed to appropriate investigators.

Use Case Example

Witness statement claims subject was at Location A at 3pm while cell tower data shows device at Location B automatically flagged with specific inconsistency documented for follow-up.

Competitive Analysis

What Makes Argus Different

Feature comparison across platform categories. No single competitor addresses the full accountability requirement.

CapabilityTraditional PlatformsReport-Writing AILegal AI ToolsArgus Partners
Multi-Model Ensemble
Not supported
Not supported
Not supported
Full support
Adversarial Validation
Not supported
Not supported
Not supported
Full support
Cryptographic Provenance
Partial support
Not supported
Not supported
Full support
Court-Grade Citations
Not supported
Not supported
Full support
Full support
Jurisdiction Compliance
Not supported
Not supported
Partial support
Full support
Multi-Modal Analysis
Full support
Partial support
Not supported
Full support
Conflict Identification
Not supported
Not supported
Not supported
Full support
Counterfactual Analysis
Not supported
Not supported
Not supported
Full support
Real-Time Streaming
Full support
Partial support
Not supported
Full support
OSINT Integration
Partial support
Not supported
Not supported
Full support
Full Support
Partial Support
Not Supported

Comparison based on publicly available documentation as of 2024. Feature availability may vary by vendor and deployment configuration.

Real Scenarios

How Investigations Transform

Four detailed scenarios showing how the platform changes investigative workflows.

Financial Crimes

Financial Crime Investigation

Major bank files suspicious activity report on complex transaction network involving 15 shell companies across 4 jurisdictions. Traditional investigation would require months of manual document review and cross-referencing.

Current Approach Challenges

  • •Weeks of manual corporate registry searches across multiple jurisdictions
  • •Difficulty tracking beneficial ownership through layered structures
  • •Risk of missing connections buried in thousands of documents

Argus Workflow

  1. 1OSINT Partner maps complete corporate structure including UBOs in minutes
  2. 2Investigative Partner correlates financial flows with entity relationships
  3. 3Briefing Partner generates prosecution-ready summary with evidence citations

Measurable Outcomes

85%reduction in analysis time
3xmore connections identified
100%audit trail completeness
Partners Used:
OSINTInvestigativeBriefing
Security & Compliance

Enterprise-Grade Security

Built for the most demanding security and compliance requirements in law enforcement.

CJIS

Criminal Justice Information Services

Controls aligned with FBI CJIS Security Policy for criminal justice information handling and access controls. Each tenant is prepared for independent certification.

Controls Implemented
Regions:United States
Includes advanced authentication, encryption at rest and in transit, audit logging, and personnel security requirements.

SOC 2 Type II

Service Organization Control 2

Controls implemented for security, availability, processing integrity, confidentiality, and privacy. Each tenant is prepared for independent verification.

Controls Implemented
Regions:Global
Controls implemented covering all five trust service criteria, ready for independent audit.

FedRAMP

Federal Risk and Authorization Management Program

Federal government cloud security authorization for deployment in government environments.

In Progress
Regions:United States (Federal)
Currently pursuing Moderate baseline authorization with expected completion Q3 2025.

GDPR

General Data Protection Regulation

Full compliance with EU data protection requirements including data subject rights and processing controls.

Compliant
Regions:European Union
Data residency options available for EU-only data storage requirements.

ISO 27001

Information Security Management

International standard for information security management systems. Controls implemented, each tenant ready for independent certification.

Controls Implemented
Regions:Global
Covers all information security controls, ready for independent certification audit.

EU AI Act

European Artificial Intelligence Act

Designed for compliance with emerging EU AI regulation requirements for high-risk AI systems.

Preparing
Regions:European Union
Architecture designed with transparency, human oversight, and documentation requirements anticipated by the regulation.

Data Protection Built In

Every layer of the platform implements defense-in-depth security. Your investigation data never leaves your control.

AES-256 Encryption
TLS 1.3 Transit
MFA Required
Role-Based Access

Every Day Without Validated AI Is a Liability Accumulating

See how ensemble reasoning, adversarial validation, and cryptographic provenance support investigations. Schedule a demonstration with our team.