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🔬 ForensiX AI

AI-Powered Forensic Triage & Postmortem Intelligence System

An intelligent investigative support system integrating GraphRAG, NLP, Multi-Agent AI, and custom fine-tuned models to assist forensic investigations with real-time analysis, evidence correlation, and predictive risk scoring.

📋 Table of Contents

Problem Statement
System Architecture
Multi-Agent System
GraphRAG Knowledge Engine
Custom AI Models
Forensic NLP Engine
Digital Evidence Correlation
Cross-Case Intelligence
Time-of-Death Estimation
Risk Scoring & Explainability
Universal LLM Provider
Frontend Architecture
API Reference
Deployment
Novelties & Innovation
Ethical Considerations
Future Scope

🎯 Problem Statement

Investigative agencies and forensic departments face critical challenges:

Volume overload: Processing large amounts of forensic/digital evidence within limited time
Manual bottlenecks: Autopsy reports, CCTV logs, mobile metadata analyzed separately by different teams
Missed connections: Cross-evidence correlations invisible to siloed manual analysis
Delayed triage: Critical leads buried under routine cases due to lack of intelligent prioritization
No standardization: Each investigator approaches evidence differently, creating inconsistency

ForensiX AI solves this by providing an AI-powered unified platform that:

Automatically extracts structured data from unstructured forensic reports
Correlates physical evidence with digital traces in real-time
Identifies anomalies and generates investigative leads
Provides explainable risk scoring for case prioritization
Operates with full chain-of-custody integrity (SHA-256 ledger)

🏗️ System Architecture

┌─────────────────────────────────────────────────────────────────────────────┐
│                         ForensiX AI v4.0 Architecture                        │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                              │
│  ┌─────────────────────────────────────────────────────────────────────┐    │
│  │                        PRESENTATION LAYER                            │    │
│  │  React 18 + Vite + Tailwind + Zustand + Framer Motion + Recharts    │    │
│  │                                                                      │    │
│  │  ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │    │
│  │  │Dashboard │ │ Timeline │ │ Evidence │ │  Agents  │ │ AI Chat  │ │    │
│  │  │          │ │          │ │  Graph   │ │          │ │ (GraphRAG)│ │    │
│  │  └──────────┘ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │    │
│  │  ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │    │
│  │  │  Risk    │ │ Autopsy  │ │  Cases   │ │ Custody  │ │ Settings │ │    │
│  │  │ Scoring  │ │Workspace │ │  CRUD    │ │  Chain   │ │ LLM Cfg  │ │    │
│  │  └──────────┘ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │    │
│  └─────────────────────────────────────────────────────────────────────┘    │
│                              │ REST + WebSocket + SSE                         │
│  ┌─────────────────────────────────────────────────────────────────────┐    │
│  │                        APPLICATION LAYER (FastAPI)                    │    │
│  │                                                                      │    │
│  │  ┌─────────────────────────────────────────────────────────────┐    │    │
│  │  │              MULTI-AGENT ORCHESTRATOR (7 Agents)              │    │    │
│  │  │  ┌─────────┐┌─────────┐┌─────────┐┌─────────┐┌─────────┐  │    │    │
│  │  │  │ Autopsy ││Timeline ││ Digital ││Toxicol. ││  Risk   │  │    │    │
│  │  │  │  Agent  ││  Agent  ││  Agent  ││  Agent  ││  Agent  │  │    │    │
│  │  │  └─────────┘└─────────┘└─────────┘└─────────┘└─────────┘  │    │    │
│  │  │  ┌─────────────────────┐┌─────────────────────────────┐    │    │    │
│  │  │  │ Correlation Agent   ││  Explainability Agent (SHAP) │    │    │    │
│  │  │  └─────────────────────┘└─────────────────────────────┘    │    │    │
│  │  └─────────────────────────────────────────────────────────────┘    │    │
│  │                                                                      │    │
│  │  ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌───────────┐  │    │
│  │  │  GraphRAG    │ │  Forensic    │ │ Cross-Case   │ │ Universal │  │    │
│  │  │  Knowledge   │ │  NLP Engine  │ │ Intelligence │ │ LLM Prov. │  │    │
│  │  │  (25 nodes)  │ │  (Henssge)   │ │ (Serial Det.)│ │ (12+ APIs)│  │    │
│  │  └──────────────┘ └──────────────┘ └──────────────┘ └───────────┘  │    │
│  │                                                                      │    │
│  │  ┌──────────────────────────────────────────────────────────────┐   │    │
│  │  │              CUSTOM AI MODELS (Offline, No API Keys)          │   │    │
│  │  │  ┌──────────────┐ ┌──────────────┐ ┌─────────────────────┐  │   │    │
│  │  │  │ GLiNER-biomed│ │ DeBERTa-v3   │ │ Qwen2.5-0.5B+LoRA  │  │   │    │
│  │  │  │ Zero-shot NER│ │ Classifier   │ │ Extraction Model    │  │   │    │
│  │  │  │   (280MB)    │ │   (180MB)    │ │     (1.2GB)         │  │   │    │
│  │  │  └──────────────┘ └──────────────┘ └─────────────────────┘  │   │    │
│  │  └──────────────────────────────────────────────────────────────┘   │    │
│  └─────────────────────────────────────────────────────────────────────┘    │
│                              │ SQLAlchemy + REST                              │
│  ┌─────────────────────────────────────────────────────────────────────┐    │
│  │                         DATA LAYER                                   │    │
│  │  ┌──────────┐  ┌──────────────────┐  ┌──────────────────────────┐  │    │
│  │  │  SQLite  │  │  Supabase        │  │  Chain of Custody        │  │    │
│  │  │  (Local) │  │  PostgreSQL +    │  │  SHA-256 Immutable       │  │    │
│  │  │          │  │  pgvector (Cloud) │  │  Ledger                  │  │    │
│  │  └──────────┘  └──────────────────┘  └──────────────────────────┘  │    │
│  └─────────────────────────────────────────────────────────────────────┘    │
└─────────────────────────────────────────────────────────────────────────────┘

🤖 Multi-Agent System (FEAT)

Forensic Evidence Analysis & Triage — 7 specialized AI agents that independently analyze evidence and collaborate through a master orchestrator.

                    ┌──────────────────────┐
                    │   FEAT ORCHESTRATOR   │
                    │  Sequential+Parallel  │
                    └──────────┬───────────┘
                               │
        ┌──────────────────────┼──────────────────────┐
        │                      │                      │
   ┌────┴────┐           ┌────┴────┐           ┌────┴────┐
   │ AUTOPSY │           │TIMELINE │           │ DIGITAL │
   │  AGENT  │           │  AGENT  │           │  AGENT  │
   │─────────│           │─────────│           │─────────│
   │• NER    │           │• Gap    │           │• CCTV   │
   │• COD    │           │  detect │           │• Mobile │
   │• Injuries│          │• Cluster│           │• GPS    │
   │• Toxicol.│          │• Sequence│          │• Pattern│
   └────┬────┘           └────┬────┘           └────┬────┘
        │                      │                      │
        └──────────────────────┼──────────────────────┘
                               │
        ┌──────────────────────┼──────────────────────┐
        │                      │                      │
   ┌────┴────┐           ┌────┴────┐           ┌────┴────┐
   │TOXICOL. │           │  RISK   │           │EXPLAIN- │
   │  AGENT  │           │  AGENT  │           │ ABILITY │
   │─────────│           │─────────│           │─────────│
   │• Drug ID│           │• 6-factor│          │• SHAP   │
   │• Levels │           │  weighted│          │• LIME   │
   │• Lethality│         │• Scoring │          │• Attrib.│
   └────┬────┘           └────┬────┘           └────┬────┘
        │                      │                      │
        └──────────────────────┼──────────────────────┘
                               │
                    ┌──────────┴───────────┐
                    │  CORRELATION AGENT    │
                    │  • Temporal links     │
                    │  • Spatial patterns   │
                    │  • Causal inference   │
                    │  • Behavioral flags   │
                    └──────────────────────┘

Agent Details

Agent	Purpose	Confidence	Key Findings
Autopsy Agent	Extract COD, manner, injuries, toxicology from reports	87-95%	Injuries, COD, toxicology entities
Timeline Agent	Reconstruct chronological event sequence	84%	Gaps (>30min), clusters (<5min)
Digital Agent	Analyze CCTV, mobile, GPS evidence	86%	Person discrepancy, rapid departure
Toxicology Agent	Interpret drug/poison findings	90%	Sedation indicators, lethality
Risk Agent	6-factor weighted risk scoring	85%	Score 0-100, severity level
Explainability Agent	SHAP-style factor attribution	82%	Top contributing factors
Correlation Agent	Cross-evidence relationship discovery	88-94%	Temporal/causal/behavioral links

🧠 GraphRAG Knowledge Engine

A Retrieval-Augmented Generation system with 25 verified forensic knowledge nodes covering:

┌─────────────────────────────────────────────────────────────┐
│                    GRAPHRAG ARCHITECTURE                      │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  ┌─────────────┐     ┌──────────────┐     ┌─────────────┐  │
│  │   QUERY     │────▶│  EMBEDDING   │────▶│  RETRIEVAL  │  │
│  │ (User/LLM)  │     │  (384-dim)   │     │  (Top-K)    │  │
│  └─────────────┘     └──────────────┘     └──────┬──────┘  │
│                                                    │         │
│                       ┌────────────────────────────┘         │
│                       ▼                                      │
│  ┌─────────────────────────────────────────────────────┐    │
│  │            FORENSIC KNOWLEDGE BASE                   │    │
│  │                                                      │    │
│  │  POSTMORTEM (5):                                     │    │
│  │    livor mortis, rigor mortis, algor mortis,         │    │
│  │    PMI estimation, decomposition stages              │    │
│  │                                                      │    │
│  │  INJURY PATTERNS (8):                                │    │
│  │    petechiae, defensive wounds, blunt force,         │    │
│  │    ligature, sharp force, gunshot, hyoid fracture,   │    │
│  │    wound tracks                                      │    │
│  │                                                      │    │
│  │  MANNER DETECTION (4):                               │    │
│  │    classification, suicide criteria, staging,        │    │
│  │    drowning, fire deaths                             │    │
│  │                                                      │    │
│  │  TOXICOLOGY (5):                                     │    │
│  │    CO poisoning, benzodiazepines, opioids,           │    │
│  │    alcohol, specimen collection                      │    │
│  │                                                      │    │
│  │  EVIDENCE (3):                                       │    │
│  │    CCTV analysis, mobile evidence, Locard exchange   │    │
│  └─────────────────────────────────────────────────────┘    │
│                       │                                      │
│                       ▼                                      │
│  ┌─────────────────────────────────────────────────────┐    │
│  │  CONTEXT INJECTION → LLM System Prompt              │    │
│  │  "VERIFIED FORENSIC KNOWLEDGE — do not contradict"  │    │
│  └─────────────────────────────────────────────────────┘    │
│                                                              │
│  RETRIEVAL STRATEGY (in order):                              │
│  1. Supabase pgvector (semantic, production)                 │
│  2. HuggingFace embeddings (sentence-transformers)           │
│  3. Keyword fallback (TF-IDF, always works)                  │
└─────────────────────────────────────────────────────────────┘

🧪 Custom AI Models

Model Stack (runs offline, no API keys needed)

┌─────────────────────────────────────────────────────────────┐
│  LOCAL ML PIPELINE — ForensiX Custom Models                  │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  Layer 1: ZERO-SHOT NER (GLiNER-biomed, 280MB)             │
│  ├── Define ANY entity type at runtime                      │
│  ├── "cause of death", "weapon type", "time indicator"      │
│  └── No retraining needed — labels via natural language     │
│                                                              │
│  Layer 2: BIOMEDICAL NER (OpenMed, ~109MB each)             │
│  ├── Toxicology: drugs, chemicals, levels (F1=0.96)         │
│  ├── Anatomy: body parts, organs (F1=0.91)                  │
│  └── Pathology: diseases, conditions (F1=0.91)              │
│                                                              │
│  Layer 3: MANNER CLASSIFIER (DeBERTa-v3-small, 180MB)       │
│  ├── Fine-tuned on 750 synthetic forensic samples           │
│  ├── 5 classes: HOMICIDE/SUICIDE/ACCIDENTAL/NATURAL/UNDET.  │
│  └── <100ms inference, F1>0.90                              │
│                                                              │
│  Layer 4: EXTRACTION MODEL (Qwen2.5-0.5B+LoRA, 1.2GB)      │
│  ├── Fine-tuned on 300 forensic Q&A pairs                   │
│  ├── Outputs structured JSON                                │
│  └── CPU inference ~3-5s per report                         │
│                                                              │
│  Total RAM: ~3-5GB (NER stack) or ~5-7GB (with LLM)        │
└─────────────────────────────────────────────────────────────┘

Training Pipeline

# 1. Generate synthetic training data
python custom-models/scripts/generate_dataset.py
# → 750 classification samples + 300 extraction Q&A pairs

# 2. Train manner-of-death classifier
python custom-models/scripts/train_classifier.py
# → DeBERTa-v3-small, 5 epochs, ~180MB output

# 3. Train forensic extraction model
python custom-models/scripts/train_extraction_model.py
# → Qwen2.5-0.5B + LoRA, 3 epochs, ~50MB adapter

🔍 Forensic NLP Engine

Entity Extraction Pipeline

# Input: "Blunt force trauma to the right temporal region with subdural hematoma.
#          Defensive wounds on forearms. Benzodiazepines detected."

# Output:
{
  "entities": [
    {"text": "Blunt force trauma to the right temporal region", "label": "INJURY", "confidence": 0.92},
    {"text": "subdural hematoma", "label": "INJURY", "confidence": 0.88},
    {"text": "Defensive wounds on forearms", "label": "INJURY", "confidence": 0.90},
    {"text": "Benzodiazepines detected", "label": "TOXICOLOGY", "confidence": 0.85}
  ],
  "riskScore": 100,
  "riskLevel": "CRITICAL",
  "anomalies": [{"type": "sedation_indicator", "severity": "HIGH"}]
}

Pattern Recognition

Pattern	Detection	Confidence
CAUSE_OF_DEATH	Regex + LLM extraction	85-95%
MANNER_OF_DEATH	Classification + keywords	90-95%
INJURY	8 regex patterns + NER	88%
TOXICOLOGY	Drug/substance detection	90%
TIME_INDICATOR	Postmortem signs parsing	85%
EVIDENCE	Physical trace detection	80%
ANOMALIES	Cross-reference contradiction detection	92%

📡 Digital Evidence Correlation

EVIDENCE TIMELINE
─────────────────────────────────────────────────────────────
01:45  CCTV: Two individuals enter warehouse ─────┐
01:52  CCTV: Vehicle parks near entrance           │ CLUSTER
02:00  Mobile: Victim last call ──────────────────┘ (<15min)
                                                    
02:00 ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ┐
                  ▓▓▓ 45-MIN GAP ▓▓▓              │ ANOMALY
02:45 ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ┘
                                                    
02:47  CCTV: Single person exits at high speed ───┐
02:48  Mobile: Phone signal lost (powered off)     │ PATTERN
02:50  CCTV: Vehicle departs rapidly ─────────────┘

CORRELATIONS FOUND:
  ⚠️ PERSON DISCREPANCY: 2 entered, 1 departed (CRITICAL)
  ⚠️ COMMUNICATION CUTOFF: Phone disconnected (HIGH)
  ⚠️ RAPID DEPARTURE: Vehicle at unusual speed (HIGH)

🕵️ Cross-Case Intelligence

Serial pattern detection against historical case database:

CURRENT CASE                    HISTORICAL MATCH (93% similarity)
──────────────                  ────────────────────────────────
Manner: Homicide         ═══▶   CASE-2024-0103 (93%)
Weapons: ligature+blunt  ═══▶   Weapons: blunt+ligature ✓
Injuries: strangulation   ═══▶   Injuries: head_trauma+strangulation ✓
Toxicology: diazepam     ═══▶   Toxicology: diazepam ✓
Location: industrial     ═══▶   Location: commercial ✓
MO: sedation+violence    ═══▶   MO: sedation+violence+abandoned ✓
Time: night              ═══▶   Time: night ✓
Victim: adult male       ═══▶   Victim: adult male ✓

⚠️  SERIAL PATTERN DETECTED — Cross-reference with cold case unit

⏱️ Time-of-Death Estimation

Henssge Nomogram (Double-Exponential Model)

Formula: Q = 1.25·e^(-B·t) - 0.25·e^(-5B·t)

Where:
  Q = (T_rectal - T_ambient) / (37.2 - T_ambient)
  B = 1.2815 · (W_effective)^(-0.625) + 0.0284
  W_effective = Corrective_Factor × Body_Weight

Multi-Method Approach:
  ┌─────────────────────────────────────────┐
  │  Method              │ Weight │ Conf.   │
  ├─────────────────────────────────────────┤
  │  Henssge Nomogram    │  70%   │ HIGH    │
  │  Rigor Mortis Stage  │  15%   │ MOD     │
  │  Livor Mortis State  │  10%   │ MOD     │
  │  Vitreous Potassium  │   5%   │ HIGH    │
  └─────────────────────────────────────────┘

Output: PMI = 6.9h ± 2.8h (95% CI)

📊 Risk Scoring & Explainability

6-Factor Weighted Algorithm

Risk Score = Σ (Factor_Score × Weight)

┌─────────────────────────────────────────────────────┐
│  Factor            │ Weight │ Score │ Contribution  │
├─────────────────────────────────────────────────────┤
│  Violence Level    │  25%   │ 85/100│  21.3 pts    │
│  Digital Patterns  │  20%   │ 90/100│  18.0 pts    │
│  Manner Match      │  15%   │ 95/100│  14.3 pts    │
│  Evidence Gaps     │  15%   │ 75/100│  11.3 pts    │
│  Temporal Cluster  │  15%   │ 70/100│  10.5 pts    │
│  Toxicology        │  10%   │ 60/100│   6.0 pts    │
├─────────────────────────────────────────────────────┤
│  TOTAL             │ 100%   │       │  81.4/100    │
│  Level: CRITICAL   │        │       │              │
└─────────────────────────────────────────────────────┘

SHAP Attribution (Explainable AI)

Feature Importance (case #FTI-2024-0847):

Violence Level        ████████████████████░░  85%  (+21.3)
Digital Patterns      ██████████████████░░░░  90%  (+18.0)
Manner Classification ███████████████████░░░  95%  (+14.3)
Evidence Gaps         ███████████████░░░░░░░  75%  (+11.3)
Temporal Clustering   ██████████████░░░░░░░░  70%  (+10.5)
Toxicology            ████████████░░░░░░░░░░  60%  (+ 6.0)

🔌 Universal LLM Provider

Supports 12+ LLM providers via OpenAI-compatible API:

Provider	Speed	Quality	Cost
Featherless.ai	Medium	High (Llama-3.1-70B)	Free tier
Groq	Ultra-fast	High (llama-3.3-70b)	Free tier
Together.ai	Fast	High (Llama-3.1-70B-Turbo)	Paid
OpenAI	Medium	Highest (GPT-4o)	Paid
HuggingFace	Slow	High (Qwen2.5-72B)	Free
TokenRouter	Auto	Auto (cheapest route)	Variable
DeepSeek	Medium	High (deepseek-chat)	Paid
Ollama	Local	Variable	Free
OpenRouter	Variable	Any model	Variable

Fallback chain: If primary fails → tries next provider → graceful degradation to local analysis.

🖥️ Frontend Architecture

src/
├── App.jsx                          # Router + route definitions
├── main.jsx                         # Entry point
├── pages/
│   ├── Landing.jsx                  # Hero + feature grid
│   ├── Login.jsx                    # JWT authentication
│   ├── Dashboard.jsx                # KPI cards + activity feed
│   ├── ForensicCommandDashboard.jsx # Full command center
│   ├── InvestigationWorkspace.jsx   # Main workspace (panels)
│   ├── CaseAnalysis.jsx            # Deep-dive case view
│   ├── Timeline.jsx                 # Forensic timeline
│   ├── EvidenceGraph.jsx           # Force-directed graph
│   ├── RiskScoring.jsx             # SHAP + radar charts
│   ├── Agents.jsx                   # Multi-agent dashboard
│   ├── InvestigationQuery.jsx      # NL query + AI Chat
│   └── ChainOfCustody.jsx          # SHA-256 ledger
├── components/
│   ├── layout/                      # App shell (header, nav, panels)
│   ├── views/                       # Embedded views (autopsy, evidence, etc.)
│   ├── forensic/                    # RiskGauge, SHAP chart, suspect card
│   ├── modals/                      # Upload, export, review, search
│   ├── panels/                      # Event details, evidence preview
│   └── timeline/                    # Forensic timeline component
└── lib/
    ├── api.js                       # 50+ API endpoints connected
    ├── store.js                     # Zustand global state
    └── websocket.js                 # Real-time connection

📡 API Reference

Core Endpoints (19 routers, 55+ routes)

Method	Endpoint	Description
`GET`	`/health`	System health + version
`GET`	`/api/status`	Full system status (12 features)
`POST`	`/api/intelligence/`	Unified intelligence engine
`POST`	`/api/chat/`	AI Chat with GraphRAG
`POST`	`/api/analyze/`	Full NLP + LLM + GraphRAG analysis
`POST`	`/api/ml/`	ML Pipeline (NER, classify, embed)
`POST`	`/api/graphrag/retrieve`	Knowledge retrieval
`GET`	`/api/graphrag/stats`	Knowledge base stats
`GET`	`/api/triage/stream`	SSE progress events
`GET`	`/cases/`	List all cases
`GET`	`/risk/{id}`	Risk assessment
`GET`	`/timeline/{id}`	Timeline events
`GET`	`/graph/{id}`	Evidence graph
`GET`	`/agents/status`	Agent status (7 agents)
`GET`	`/agents/analysis/{id}`	Full multi-agent analysis
`POST`	`/query/`	Natural language query
`GET`	`/custody/{id}`	Chain of custody
`WS`	`/ws/{id}`	Real-time WebSocket

🚀 Deployment

Docker (Production)

docker build -t forensix-ai .
docker run -p 7860:7860 \
  -e FEATHERLESS_API_KEY=your_key \
  -e HF_TOKEN=your_token \
  forensix-ai

HuggingFace Spaces

Push to any HF Space with Docker SDK — auto-deploys.

Manual

pip install -r backend/requirements.txt
cd frontend && npm install && npm run build && cd ..
uvicorn backend.main:app --host 0.0.0.0 --port 7860

💡 Novelties & Innovation

1. Digital Stratigraphy

Unlike existing forensic systems that separately analyze pathology reports OR digital evidence, ForensiX introduces a unified multimodal architecture that semantically correlates ALL evidence types into synchronized investigative layers.

2. Zero-Shot Forensic NER

Using GLiNER-biomed, investigators can define ANY custom entity type at runtime using natural language — no model retraining needed. Want to detect "ligature material type"? Just add it to the label list.

3. GraphRAG-Enhanced AI Chat

Every AI response is grounded in verified forensic knowledge retrieved via semantic search. The LLM cannot hallucinate forensic facts because the system prompt is injected with authoritative knowledge nodes.

4. Cross-Case Serial Pattern Detection

Automated matching against historical case database identifies potential serial offenders by comparing MO patterns, weapon types, victim profiles, and behavioral signatures.

5. Explainable Risk Scoring

Every risk score is decomposable into SHAP-style attributions. Investigators can see EXACTLY which evidence drives the score — legally defensible transparency.

6. Universal LLM Provider

Works with ANY provider (12+ supported). Investigators aren't locked into one AI vendor. Seamless fallback chain ensures the system never goes dark.

7. Offline-Capable Custom Models

Fine-tuned models run entirely on CPU without internet. Critical for sensitive investigations where data cannot leave the network.

⚖️ Ethical Considerations

Advisory only: All outputs support human decision-making, never replace forensic experts
No legal conclusions: System uses "suggests", "indicates", "consistent with" — never "proves"
Explainable: Every AI output traceable to specific evidence (SHAP attribution)
Chain of custody: SHA-256 immutable ledger ensures evidence integrity
Data privacy: Local-first architecture, cloud optional, no mandatory data sharing
Bias awareness: Models trained on balanced synthetic data to avoid demographic bias
Access control: JWT authentication, role-based permissions

🔮 Future Scope

Enhancement	Status	Description
Real-time CCTV analysis	Planned	Live video feed processing with person detection
Multilingual report analysis	Planned	Hindi, Tamil, Spanish autopsy report parsing
Federated learning	Research	Secure cross-agency model training without data sharing
IoT sensor integration	Planned	Real-time environmental monitoring (temp, humidity)
Voice-to-text dictation	Planned	Hands-free field notes during investigation
Mobile companion app	Planned	On-scene evidence collection with GPS tagging
Behavioral profiling	Research	LSTM-based suspect behavior prediction
3D wound reconstruction	Research	AR-based injury pattern visualization

📁 Project Structure

forensic-ai-triage/
├── backend/
│   ├── main.py                          # FastAPI app (55+ routes)
│   ├── requirements.txt                 # Python dependencies
│   ├── db/                              # SQLAlchemy models + init
│   ├── routers/                         # 19 API routers
│   │   ├── agents.py                    # Multi-agent endpoints
│   │   ├── analyze.py                   # Full analysis (NLP+LLM+RAG)
│   │   ├── chat.py                      # AI Chat with GraphRAG
│   │   ├── intelligence.py              # Unified intelligence engine
│   │   ├── ml_pipeline.py              # HuggingFace ML pipeline
│   │   ├── graphrag.py                 # Knowledge base management
│   │   ├── triage_stream.py            # SSE progress events
│   │   └── ... (12 more routers)
│   └── services/                        # Business logic
│       ├── llm_provider.py             # Universal LLM (12+ providers)
│       ├── graphrag_service.py         # 25-node knowledge base
│       ├── multi_agent_orchestrator.py # 7-agent system
│       ├── forensic_engine.py          # Henssge + NLP + correlation
│       ├── supabase_client.py          # Cloud persistence
│       └── ... (8 more services)
├── frontend/
│   ├── src/
│   │   ├── pages/          (12 pages)
│   │   ├── components/     (28 components)
│   │   └── lib/            (api.js, store.js, websocket.js)
│   └── package.json
├── custom-models/                       # 🆕 Custom AI Models
│   ├── datasets/                        # Training data (750+300 samples)
│   ├── scripts/                         # Training + inference code
│   │   ├── generate_dataset.py         # Synthetic data generator
│   │   ├── train_classifier.py         # DeBERTa-v3-small fine-tuning
│   │   ├── train_extraction_model.py   # Qwen2.5-0.5B + LoRA
│   │   └── inference.py                # Unified local inference
│   └── models/                          # Trained weights (after training)
├── lib/                                 # TypeScript engines (reference)
├── multi-agent-system/                  # Full TS agent implementation
├── langgraph-agents/                    # LangGraph Python server
├── prisma/                              # PostgreSQL schema
├── Dockerfile                           # Multi-stage production build
├── nginx.conf                           # Reverse proxy config
├── .env.example                         # All provider configs
└── README.md                            # This file

📊 Performance Metrics

Metric	Value
API Endpoints	55+ routes, all verified 200 OK
Multi-Agent Analysis	7 agents, ~16s total (with LLM)
NLP Entity Extraction	<100ms per report (regex)
Risk Scoring	<50ms computation
Graph Building	<200ms for full evidence graph
WebSocket Latency	<50ms real-time updates
Local ML Inference	<500ms (NER), <100ms (classifier)
Knowledge Retrieval	<10ms (keyword), <200ms (semantic)

🛡️ Security

Authentication: JWT (HS256) with configurable secret
Evidence Integrity: SHA-256 hash chain (blockchain-style)
Data Isolation: SQLite per-instance, no shared state
API Security: CORS configured, rate limiting available
Sensitive Data: All LLM calls use local context — no case data sent to external APIs unless investigator explicitly uses AI Chat

Built for Justice. Powered by AI. Guided by Ethics.

ForensiX AI — Investigative Assistance Only. Not for legal determination.

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