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AI Tools Suite - Product Manual

A comprehensive collection of AI/ML operational tools for monitoring, security, compliance, and cost management.

Overview
Architecture
Tool Catalog
Product Roadmap
Installation
Quick Start
User Guide
Detailed Tool Documentation
Directory Structure
Version History

Overview

This suite provides 14 essential tools for managing AI/ML systems in production environments. Each tool addresses a specific operational need, from cost tracking to security testing.

Target Users

ML Engineers
Data Scientists
DevOps/MLOps Teams
Product Managers
Compliance Officers

Architecture

System Overview

The AI Tools Suite uses a modern web architecture with a unified SvelteKit frontend and FastAPI backend.

┌─────────────────────────────────────────────────────────────────────────┐
│                         SVELTEKIT FRONTEND                              │
│  ┌────────────────────────────────────────────────────────────────────┐ │
│  │                        UNIFIED DASHBOARD                           │ │
│  │  ┌──────────────────┐  ┌─────────────────────────────────────────┐ │ │
│  │  │  Sidebar Nav     │  │  Main Content Area                      │ │ │
│  │  │  ────────────    │  │  ────────────────                       │ │ │
│  │  │  Dashboard       │  │                                         │ │ │
│  │  │  Drift Monitor   │  │    [Selected Tool View]                 │ │ │
│  │  │  Cost Tracker    │  │                                         │ │ │
│  │  │  Security Test   │  │    - Interactive Charts                 │ │ │
│  │  │  Data History    │  │    - Data Tables                        │ │ │
│  │  │  Model Compare   │  │    - Configuration Forms                │ │ │
│  │  │  Privacy Scan    │  │    - Real-time Updates                  │ │ │
│  │  │  Label Quality   │  │    - Export Options                     │ │ │
│  │  │  Cost Estimate   │  │                                         │ │ │
│  │  │  Data Audit      │  │                                         │ │ │
│  │  │  Content Perf    │  │                                         │ │ │
│  │  │  Bias Checks     │  │                                         │ │ │
│  │  │  Profitability   │  │                                         │ │ │
│  │  │  Emergency Ctrl  │  │                                         │ │ │
│  │  │  Reports         │  │                                         │ │ │
│  │  └──────────────────┘  └─────────────────────────────────────────┘ │ │
│  └────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘
                                    │
                                    │ REST API / WebSocket
                                    ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                          FASTAPI BACKEND                                │
│  ┌────────────────────────────────────────────────────────────────────┐ │
│  │                         API ROUTERS                                │ │
│  │  /api/drift  /api/costs  /api/security  /api/privacy  /api/...    │ │
│  └────────────────────────────────────────────────────────────────────┘ │
│  ┌────────────────────────────────────────────────────────────────────┐ │
│  │                      SERVICE LAYER                                 │ │
│  │  DriftDetector │ CostAggregator │ PIIScanner │ BiasAnalyzer │ ... │ │
│  └────────────────────────────────────────────────────────────────────┘ │
│  ┌────────────────────────────────────────────────────────────────────┐ │
│  │                    SHARED SERVICES                                 │ │
│  │  Authentication │ Database ORM │ File Storage │ Background Jobs   │ │
│  └────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘
                                    │
                                    ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                          DATA LAYER                                     │
│  ┌──────────────────┐  ┌──────────────────┐  ┌──────────────────────┐  │
│  │  PostgreSQL/     │  │  Redis           │  │  File Storage        │  │
│  │  SQLite          │  │  (Cache/Queue)   │  │  (Uploads/Reports)   │  │
│  │  - Users         │  │  - Session cache │  │  - CSV/JSON uploads  │  │
│  │  - Audit logs    │  │  - Job queue     │  │  - Generated reports │  │
│  │  - Metrics       │  │  - Real-time     │  │  - Model artifacts   │  │
│  └──────────────────┘  └──────────────────┘  └──────────────────────┘  │
└─────────────────────────────────────────────────────────────────────────┘

Tech Stack

Layer	Technology	Purpose
Frontend	SvelteKit + TypeScript	Unified single-page application
UI Components	Tailwind CSS + shadcn-svelte	Modern, accessible components
Charts	Apache ECharts	Interactive data visualizations
Backend	FastAPI (Python 3.11+)	REST API + WebSocket support
ORM	SQLAlchemy 2.0	Database abstraction
Database	SQLite (dev) / PostgreSQL (prod)	Persistent storage
Cache	Redis (optional)	Session cache, job queue
Deployment	Docker Compose	Container orchestration

API Design

All tools are accessible via a RESTful API:

Base URL: http://localhost:8000/api/v1

Endpoints:
├── /drift/           # Model Drift Monitor
├── /costs/           # Vendor Cost Tracker
├── /security/        # Security Tester
├── /history/         # Data History Log
├── /compare/         # Model Comparator
├── /privacy/         # Privacy Scanner
├── /labels/          # Label Quality Scorer
├── /estimate/        # Inference Estimator
├── /audit/           # Data Integrity Audit
├── /content/         # Content Performance
├── /bias/            # Safety/Bias Checks
├── /profitability/   # Profitability Analysis
├── /emergency/       # Emergency Control
└── /reports/         # Result Interpretation

Tool Catalog

#	Tool Name	Deliverable	Description	Status
1	Model Drift Monitor	Dashboard	Tracks prediction confidence over time to detect when AI accuracy begins to decline.	Pending
2	Vendor Cost Tracker	API spend aggregator	Provides a single view of all API expenses across providers like OpenAI, Anthropic, and AWS.	Pending
3	Security Tester	Input fuzzer	Tests AI endpoints for exploits and prompt injections to prevent unauthorized access.	Pending
4	Data History Log	Audit trail logger	Maintains a record of which data versions were used to train specific models for legal compliance.	Pending
5	Model Comparator	Response evaluator	Compares outputs from different models side-by-side to determine the best fit for specific tasks.	Pending
6	Privacy Scanner	PII detector	Automatically finds and removes personal information (names, emails) from training datasets.	Pending
7	Label Quality Scorer	Agreement calculator	Measures the consistency of data labeling teams to ensure high-quality training inputs.	Pending
8	Inference Estimator	Token/Price calculator	Predicts monthly operational costs based on expected usage before a project is deployed.	Pending
9	Data Integrity Audit	Data cleaning app	Identifies and fixes errors in databases to prevent data loss and improve model performance.	Pending
10	Content Performance	Retention model	Visualizes audience drop-off points to identify which content segments drive engagement.	Pending
11	Safety/Bias Checks	Bias scanner checklist	Audits recommendation engines to ensure they follow privacy laws and treat users fairly.	Pending
12	Profitability Analysis	Cost-vs-revenue view	Correlates AI costs with business revenue to identify specific areas for monthly savings.	Pending
13	Emergency Control	Manual override template	Provides a reliable mechanism to immediately suspend automated processes if they fail.	Pending
14	Result Interpretation	Automated report generator	Converts technical metrics into a standardized list of actions for business decision-makers.	Pending

Product Roadmap

Phase 1: Foundation (MVP)

Goal: Core infrastructure and 3 essential tools

Milestone	Deliverables	Dependencies
1.1 Project Setup	SvelteKit frontend scaffold, FastAPI backend scaffold, Docker Compose config, CI/CD pipeline	None
1.2 Shared Infrastructure	Authentication system, Database models, API client library, Shared UI components (sidebar, charts, tables)	1.1
1.3 Inference Estimator	Token counting, Multi-provider pricing, Cost projection UI, Export to CSV	1.2
1.4 Data Integrity Audit	File upload, Missing value detection, Duplicate finder, Interactive cleaning UI	1.2
1.5 Privacy Scanner	PII detection engine, Redaction modes, Scan results UI, Batch processing	1.2

Phase 2: Monitoring & Costs

Goal: Production monitoring and cost management tools

Milestone	Deliverables	Dependencies
2.1 Model Drift Monitor	Baseline upload, KS/PSI tests, Drift visualization, Alert configuration	Phase 1
2.2 Vendor Cost Tracker	API key integration (OpenAI, Anthropic, AWS), Cost aggregation, Budget alerts, Usage forecasting	Phase 1
2.3 Profitability Analysis	Revenue data import, Cost-revenue correlation, ROI calculator, Savings recommendations	2.2

Phase 3: Security & Compliance

Goal: Security testing and compliance tools

Milestone	Deliverables	Dependencies
3.1 Security Tester	Prompt injection test suite, Jailbreak detection, Vulnerability report generation	Phase 1
3.2 Data History Log	Data versioning (SHA-256), Model-dataset linking, Audit trail UI, GDPR/CCPA reports	Phase 1
3.3 Safety/Bias Checks	Fairness metrics (demographic parity, equalized odds), Bias detection, Compliance checklist	Phase 1

Phase 4: Quality & Comparison

Goal: Data quality and model evaluation tools

Milestone	Deliverables	Dependencies
4.1 Label Quality Scorer	Multi-rater agreement (Kappa, Alpha), Inconsistency flagging, Quality reports	Phase 1
4.2 Model Comparator	Side-by-side comparison UI, Quality scoring, Latency benchmarks, Cost-per-query analysis	Phase 1

Phase 5: Analytics & Control

Goal: Advanced analytics and operational control

Milestone	Deliverables	Dependencies
5.1 Content Performance	Engagement tracking, Drop-off visualization, Retention curves, A/B analysis	Phase 1
5.2 Emergency Control	Kill switch API, Graceful degradation, Rollback triggers, Incident logging	Phase 1
5.3 Result Interpretation	Metric-to-insight engine, Executive summary generator, PDF/Markdown export	Phase 1

Roadmap Visualization

Phase 1: Foundation          Phase 2: Monitoring       Phase 3: Security
─────────────────────        ─────────────────────     ─────────────────────
┌─────────────────────┐      ┌─────────────────────┐   ┌─────────────────────┐
│ Project Setup       │      │ Model Drift Monitor │   │ Security Tester     │
│ Shared Infra        │ ───► │ Vendor Cost Tracker │   │ Data History Log    │
│ Inference Estimator │      │ Profitability       │   │ Safety/Bias Checks  │
│ Data Integrity      │      └─────────────────────┘   └─────────────────────┘
│ Privacy Scanner     │                │                         │
└─────────────────────┘                │                         │
                                       ▼                         ▼
                              Phase 4: Quality          Phase 5: Analytics
                              ─────────────────────     ─────────────────────
                              ┌─────────────────────┐   ┌─────────────────────┐
                              │ Label Quality       │   │ Content Performance │
                              │ Model Comparator    │   │ Emergency Control   │
                              └─────────────────────┘   │ Result Interpret    │
                                                        └─────────────────────┘

Success Metrics

Phase	Key Metrics
Phase 1	Frontend/backend running, 3 tools functional, <2s page load
Phase 2	Real-time drift alerts, Cost tracking across 3+ providers
Phase 3	90%+ PII detection rate, Compliance reports generated
Phase 4	Inter-rater agreement calculated, Model comparison functional
Phase 5	Emergency shutoff <1s response, Automated reports generated

Installation

Prerequisites

# Required
Node.js 18+
Python 3.11+
Docker & Docker Compose (recommended)

# Optional
PostgreSQL 15+ (for production)
Redis 7+ (for caching/queues)

Quick Setup with Docker

# Clone and navigate
cd ai_tools_suite

# Start all services
docker-compose up -d

# Access the application
# Frontend: http://localhost:3000
# Backend API: http://localhost:8000
# API Docs: http://localhost:8000/docs

Manual Setup

Backend (FastAPI)

cd backend

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run development server
uvicorn main:app --reload --port 8000

Frontend (SvelteKit)

cd frontend

# Install dependencies
npm install

# Run development server
npm run dev

# Build for production
npm run build

Environment Variables

Create .env files in both frontend/ and backend/ directories:

backend/.env

DATABASE_URL=sqlite:///./ai_tools.db
SECRET_KEY=your-secret-key-here
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...

frontend/.env

PUBLIC_API_URL=http://localhost:8000

Quick Start

Accessing the Dashboard

Start the application (Docker or manual setup)
Open http://localhost:3000 in your browser
Use the sidebar to navigate between tools

API Usage

All tools are accessible via REST API:

# Check API health
curl http://localhost:8000/api/v1/health

# Estimate inference costs
curl -X POST http://localhost:8000/api/v1/estimate/calculate \
  -H "Content-Type: application/json" \
  -d '{"model": "gpt-4", "tokens": 1000000, "requests_per_day": 1000}'

# Scan for PII
curl -X POST http://localhost:8000/api/v1/privacy/scan \
  -F "file=@data.csv"

# Check model drift
curl -X POST http://localhost:8000/api/v1/drift/analyze \
  -F "baseline=@baseline.csv" \
  -F "production=@production.csv"

User Guide

This section provides step-by-step instructions for using the tools available in Phase 1.

Inference Estimator

Purpose: Calculate AI API costs before deploying your application.

How to Use

Navigate to the Tool
- Click "Inference Estimator" in the sidebar (or go to /inference-estimator)
Configure Your Model
- Select your AI provider (OpenAI, Anthropic, Google, or Custom)
- Choose the specific model (e.g., GPT-4, Claude 3, Gemini Pro)
- For custom models, enter your own input/output prices per 1M tokens
Enter Usage Parameters
- Input Tokens per Request: Average tokens you send to the model
- Output Tokens per Request: Average tokens the model returns
- Requests per Day: Expected daily API calls
- Peak Multiplier: Account for traffic spikes (1x = normal, 2x = double traffic)
View Cost Breakdown
- Daily Cost: Input cost + Output cost per day
- Monthly Cost: 30-day projection
- Annual Cost: 365-day projection
Override Pricing (Optional)
- Click the edit icon next to any model to set custom pricing
- Useful for negotiated enterprise rates or new models

Example Calculation

Model: GPT-4 Turbo
Input: 500 tokens/request × $10.00/1M = $0.005/request
Output: 200 tokens/request × $30.00/1M = $0.006/request
Requests: 10,000/day

Daily Cost: (0.005 + 0.006) × 10,000 = $110.00
Monthly Cost: $110 × 30 = $3,300.00

Expected Outcome

After entering your parameters, you will see:

┌─────────────────────────────────────────────────────────────┐
│  COST BREAKDOWN                                             │
├─────────────────────────────────────────────────────────────┤
│  Provider: OpenAI                                           │
│  Model: GPT-4 Turbo                                         │
│                                                             │
│  ┌─────────────┬────────────┬────────────┬───────────────┐  │
│  │   Period    │ Input Cost │ Output Cost│    Total      │  │
│  ├─────────────┼────────────┼────────────┼───────────────┤  │
│  │   Daily     │   $50.00   │   $60.00   │    $110.00    │  │
│  │   Monthly   │ $1,500.00  │ $1,800.00  │  $3,300.00    │  │
│  │   Yearly    │$18,250.00  │$21,900.00  │ $40,150.00    │  │
│  └─────────────┴────────────┴────────────┴───────────────┘  │
│                                                             │
│  Tokens per Day: 7,000,000 (5M input + 2M output)           │
│  Cost per Request: $0.011                                   │
│  Cost per 1K Requests: $11.00                               │
└─────────────────────────────────────────────────────────────┘

Data Integrity Audit

Purpose: Analyze datasets for quality issues, missing values, duplicates, and outliers.

How to Use

Navigate to the Tool
- Click "Data Integrity Audit" in the sidebar (or go to /data-audit)
Upload Your Dataset
- Drag and drop a file onto the upload area, OR
- Click to browse and select a file
- Supported formats: CSV, Excel (.xlsx, .xls), JSON
Click "Analyze Data"
- The tool will process your file and display results
Review the Results

Quick Stats Panel:
- Rows: Total number of records
- Columns: Number of fields
- Duplicates: Count of duplicate rows
- Issues: Number of problems detected
Overview Tab:
- Missing values summary with counts and percentages
- Duplicate row detection results
Columns Tab:
- Detailed statistics for each column:
  - Data type (int64, float64, object, etc.)
  - Missing value count and percentage
  - Unique value count
  - Sample values
Issues & Recommendations Tab:
- List of detected problems with icons:
  - ! = Missing values
  - 2x = Duplicates
  - ~ = Outliers
  - # = High cardinality
  - = = Constant column
  - OK = No issues
- Actionable recommendations for fixing each issue

Understanding the Results

Issue Type	What It Means	Recommended Action
Missing Values	Empty cells in the data	Fill with mean/median or remove rows
Duplicate Rows	Identical records	Remove duplicates to avoid bias
Outliers	Extreme values	Investigate if valid or remove
High Cardinality	Too many unique values	Check if column is an ID field
Constant Column	Only one value	Consider removing from analysis

Expected Outcome

After uploading a dataset (e.g., customers.csv), you will see:

┌─────────────────────────────────────────────────────────────────────┐
│  QUICK STATS                                                        │
│  ┌──────────┬──────────┬──────────────┬─────────────┐               │
│  │  Rows    │ Columns  │  Duplicates  │   Issues    │               │
│  │  10,542  │    12    │      47      │      5      │               │
│  └──────────┴──────────┴──────────────┴─────────────┘               │
├─────────────────────────────────────────────────────────────────────┤
│  OVERVIEW TAB                                                       │
│  ────────────                                                       │
│  Missing Values:                                                    │
│  ┌─────────────────┬─────────┬─────────┐                            │
│  │ Column          │ Count   │ Percent │                            │
│  ├─────────────────┼─────────┼─────────┤                            │
│  │ email           │   23    │  0.22%  │                            │
│  │ phone           │  156    │  1.48%  │                            │
│  │ address         │   89    │  0.84%  │                            │
│  └─────────────────┴─────────┴─────────┘                            │
│                                                                     │
│  ⚠ 47 duplicate rows found (0.45%)                                  │
├─────────────────────────────────────────────────────────────────────┤
│  ISSUES & RECOMMENDATIONS TAB                                       │
│  ────────────────────────────                                       │
│  Issues Found:                                                      │
│  [!] Dataset has 268 missing values across 3 columns                │
│  [2x] Found 47 duplicate rows (0.45%)                               │
│  [~] Column 'age' has 12 potential outliers                         │
│  [#] Column 'user_id' has very high cardinality (10,495 unique)     │
│  [=] Column 'status' has only one unique value                      │
│                                                                     │
│  Recommendations:                                                   │
│  💡 Fill missing values with mean/median for numeric columns        │
│  💡 Consider removing duplicate rows to improve data quality        │
│  💡 Review if 'user_id' should be used as an identifier             │
│  💡 Consider removing constant column 'status'                      │
└─────────────────────────────────────────────────────────────────────┘

API Endpoints

# Analyze a dataset
curl -X POST http://localhost:8000/api/v1/audit/analyze \
  -F "file=@your_data.csv"

# Clean a dataset (remove duplicates and missing rows)
curl -X POST http://localhost:8000/api/v1/audit/clean \
  -F "file=@your_data.csv"

# Validate schema
curl -X POST http://localhost:8000/api/v1/audit/validate-schema \
  -F "file=@your_data.csv"

# Detect outliers
curl -X POST http://localhost:8000/api/v1/audit/detect-outliers \
  -F "file=@your_data.csv"

Sample API Response

{
  "total_rows": 10542,
  "total_columns": 12,
  "missing_values": {
    "email": {"count": 23, "percent": 0.22},
    "phone": {"count": 156, "percent": 1.48},
    "address": {"count": 89, "percent": 0.84}
  },
  "duplicate_rows": 47,
  "duplicate_percent": 0.45,
  "column_stats": [
    {
      "name": "customer_id",
      "dtype": "int64",
      "missing_count": 0,
      "missing_percent": 0.0,
      "unique_count": 10542,
      "sample_values": [1001, 1002, 1003, 1004, 1005]
    }
  ],
  "issues": [
    "Dataset has 268 missing values across 3 columns",
    "Found 47 duplicate rows (0.45%)",
    "Column 'age' has 12 potential outliers"
  ],
  "recommendations": [
    "Consider filling missing values with mean/median",
    "Consider removing duplicate rows to improve data quality"
  ]
}

Privacy Scanner

Purpose: Detect and redact personally identifiable information (PII) from text and files.

How to Use

Navigate to the Tool
- Click "Privacy Scanner" in the sidebar (or go to /privacy-scanner)
Choose Input Mode
- Text Mode: Paste or type text directly
- File Mode: Upload CSV, TXT, or JSON files
Configure Detection Options
- Toggle which PII types to detect:
  - Emails
  - Phone numbers
  - SSN (Social Security Numbers)
  - Credit Cards
  - IP Addresses
  - Dates of Birth
Enter or Upload Content
- For text: Paste content into the text area
- For files: Drag and drop or click to upload
- Tip: Click "Load Sample" to see example PII data
Click "Scan for PII"
- The tool will analyze your content and display results
Review the Results

Risk Summary:
- PII Found: Total number of PII entities detected
- Types: Number of different PII categories
- Risk Score: Calculated severity (0-100)
- Risk Level: CRITICAL, HIGH, MEDIUM, or LOW
Overview Tab:
- PII counts by type with color-coded severity
- Risk assessment with explanation
Entities Tab:
- Detailed list of each detected PII item:
  - Type (EMAIL, PHONE, SSN, etc.)
  - Original value
  - Masked value
  - Confidence score (percentage)
Redacted Preview Tab:
- Shows your text with all PII masked
- Safe to share after verification

PII Detection Patterns

Type	Example	Masked As
EMAIL	john.doe@example.com	jo***@example.com
PHONE	(555) 123-4567	--4567
SSN	123-45-6789	*--6789
CREDIT_CARD	4532015112830366	--****-0366
IP_ADDRESS	192.168.1.100	192...*
DATE_OF_BIRTH	03/15/1985	//1985

Risk Levels Explained

Level	Score	Description
CRITICAL	70-100	Highly sensitive PII (SSN, Credit Cards). Immediate action required.
HIGH	50-69	Multiple sensitive PII elements. Consider redaction before sharing.
MEDIUM	30-49	Some PII detected that may require attention.
LOW	0-29	Minimal or no PII detected.

API Endpoints

# Scan text for PII
curl -X POST http://localhost:8000/api/v1/privacy/scan-text \
  -F "text=Contact john@example.com or call 555-123-4567" \
  -F "detect_emails=true" \
  -F "detect_phones=true"

# Scan a file for PII
curl -X POST http://localhost:8000/api/v1/privacy/scan-file \
  -F "file=@customer_data.csv"

# Scan CSV/Excel with column-by-column analysis
curl -X POST http://localhost:8000/api/v1/privacy/scan-dataframe \
  -F "file=@customer_data.csv"

# Redact PII from text
curl -X POST http://localhost:8000/api/v1/privacy/redact \
  -F "text=Call 555-123-4567 for support" \
  -F "mode=mask"

# List supported PII types
curl http://localhost:8000/api/v1/privacy/entity-types

Redaction Modes

Mode	Description	Example Output
`mask`	Shows partial value	jo***@example.com
`remove`	Replaces with [REDACTED]	[REDACTED]
`type`	Shows PII type	[EMAIL]

Expected Outcome

After scanning text or a file, you will see results like:

RISK SUMMARY
┌────────────┬──────────┬────────────┬─────────────────┐
│ PII Found  │  Types   │ Risk Score │   Risk Level    │
│     7      │    5     │     72     │    CRITICAL     │
└────────────┴──────────┴────────────┴─────────────────┘

ENTITIES TAB
┌────────────────┬───────────────────────┬────────────────┬───────┐
│ Type           │ Original              │ Masked         │ Conf  │
├────────────────┼───────────────────────┼────────────────┼───────┤
│ EMAIL          │ john.smith@example.com│ jo***@example..│  95%  │
│ PHONE          │ (555) 123-4567        │ ***-***-4567   │  85%  │
│ SSN            │ 123-45-6789           │ ***-**-6789    │  95%  │
│ CREDIT_CARD    │ 4532015112830366      │ ****-****-0366 │  95%  │
└────────────────┴───────────────────────┴────────────────┴───────┘

REDACTED PREVIEW
Customer Record:
Email: jo***@example.com
Phone: ***-***-4567
SSN: ***-**-6789
Credit Card: ****-****-****-0366

Sample API Response

{
  "total_entities": 7,
  "entities_by_type": {
    "EMAIL": 2, "PHONE": 2, "SSN": 1, "CREDIT_CARD": 1, "IP_ADDRESS": 1
  },
  "risk_level": "CRITICAL",
  "risk_score": 72,
  "entities": [
    {
      "type": "SSN",
      "value": "123-45-6789",
      "confidence": 0.95,
      "masked_value": "***-**-6789"
    }
  ],
  "redacted_preview": "Email: jo***@example.com\nSSN: ***-**-6789..."
}

Detailed Tool Documentation

1. Model Drift Monitor

Purpose: Detect when model performance degrades over time.

Features:

Real-time confidence score tracking
Statistical drift detection (KS test, PSI)
Alert thresholds configuration
Historical trend visualization

API Endpoints:

POST /api/v1/drift/baseline     # Upload baseline distribution
POST /api/v1/drift/analyze      # Analyze production data for drift
GET  /api/v1/drift/history      # Get drift score history
PUT  /api/v1/drift/thresholds   # Configure alert thresholds

2. Vendor Cost Tracker

Purpose: Aggregate and visualize API spending across providers.

Supported Providers:

OpenAI
Anthropic
AWS Bedrock
Google Vertex AI
Azure OpenAI

Features:

Daily/weekly/monthly cost breakdowns
Per-project cost allocation
Budget alerts
Usage forecasting

3. Security Tester

Purpose: Identify vulnerabilities in AI endpoints.

Test Categories:

Prompt injection attacks
Jailbreak attempts
Data exfiltration probes
Rate limit testing
Input validation bypass

Output: Security report with severity ratings and remediation steps.

4. Data History Log

Purpose: Maintain audit trail for ML training data.

Features:

Data version hashing (SHA-256)
Model-to-dataset mapping
Timestamp logging
Compliance report generation (GDPR, CCPA)

5. Model Comparator

Purpose: Evaluate and compare model outputs.

Features:

Side-by-side response comparison
Quality scoring (coherence, accuracy, relevance)
Latency benchmarking
Cost-per-query analysis

6. Privacy Scanner

Purpose: Detect and remove PII from datasets.

Detected Entities:

Names
Email addresses
Phone numbers
SSN/National IDs
Credit card numbers
Addresses
IP addresses

Modes:

Detection only
Automatic redaction
Pseudonymization

7. Label Quality Scorer

Purpose: Measure inter-annotator agreement.

Metrics:

Cohen's Kappa
Fleiss' Kappa (multi-rater)
Krippendorff's Alpha
Percent agreement

Output: Quality report with flagged inconsistent samples.

8. Inference Estimator

Purpose: Predict operational costs before deployment.

Inputs:

Expected request volume
Average tokens per request
Model selection
Peak usage patterns

Output: Monthly cost projection with confidence intervals.

9. Data Integrity Audit

Purpose: Clean and validate datasets.

Checks:

Missing values
Duplicate records
Data type mismatches
Outlier detection
Schema validation
Referential integrity

Interface: Interactive data cleaning with preview and undo.

10. Content Performance

Purpose: Analyze user engagement patterns.

Features:

Drop-off point visualization
Engagement heatmaps
A/B test analysis
Retention curve modeling

11. Safety/Bias Checks

Purpose: Audit AI systems for fairness.

Metrics:

Demographic parity
Equalized odds
Calibration across groups
Disparate impact ratio

Output: Compliance checklist with recommendations.

12. Profitability Analysis

Purpose: Connect AI costs to business outcomes.

Features:

Cost attribution by feature/product
Revenue correlation analysis
ROI calculation
Optimization recommendations

13. Emergency Control

Purpose: Safely halt AI systems when needed.

Features:

One-click system suspension
Graceful degradation modes
Rollback capabilities
Incident logging

Implementation: API endpoints + admin dashboard.

14. Result Interpretation

Purpose: Translate metrics into business actions.

Features:

Automated insight generation
Executive summary creation
Action item extraction
Trend interpretation

Output: Markdown/PDF reports for stakeholders.

Directory Structure

ai_tools_suite/
├── PRODUCT_MANUAL.md
├── docker-compose.yml
├── .env.example
│
├── frontend/                      # SvelteKit Application
│   ├── src/
│   │   ├── routes/
│   │   │   ├── +layout.svelte     # Shared layout with sidebar
│   │   │   ├── +page.svelte       # Dashboard home
│   │   │   ├── drift-monitor/
│   │   │   │   └── +page.svelte
│   │   │   ├── cost-tracker/
│   │   │   │   └── +page.svelte
│   │   │   ├── security-tester/
│   │   │   ├── data-history/
│   │   │   ├── model-comparator/
│   │   │   ├── privacy-scanner/
│   │   │   ├── label-quality/
│   │   │   ├── inference-estimator/
│   │   │   ├── data-audit/
│   │   │   ├── content-performance/
│   │   │   ├── bias-checks/
│   │   │   ├── profitability/
│   │   │   ├── emergency-control/
│   │   │   └── reports/
│   │   ├── lib/
│   │   │   ├── components/        # Shared UI components
│   │   │   │   ├── Sidebar.svelte
│   │   │   │   ├── Chart.svelte
│   │   │   │   ├── DataTable.svelte
│   │   │   │   └── FileUpload.svelte
│   │   │   ├── stores/            # Svelte stores
│   │   │   └── api/               # API client
│   │   └── app.html
│   ├── static/
│   ├── package.json
│   ├── svelte.config.js
│   ├── tailwind.config.js
│   └── tsconfig.json
│
├── backend/                       # FastAPI Application
│   ├── main.py                    # Application entry point
│   ├── requirements.txt
│   ├── routers/
│   │   ├── drift.py
│   │   ├── costs.py
│   │   ├── security.py
│   │   ├── history.py
│   │   ├── compare.py
│   │   ├── privacy.py
│   │   ├── labels.py
│   │   ├── estimate.py
│   │   ├── audit.py
│   │   ├── content.py
│   │   ├── bias.py
│   │   ├── profitability.py
│   │   ├── emergency.py
│   │   └── reports.py
│   ├── services/                  # Business logic
│   │   ├── drift_detector.py
│   │   ├── cost_aggregator.py
│   │   ├── pii_scanner.py
│   │   ├── bias_analyzer.py
│   │   └── ...
│   ├── models/                    # Pydantic schemas
│   │   ├── drift.py
│   │   ├── costs.py
│   │   └── ...
│   ├── database/
│   │   ├── connection.py
│   │   └── models.py              # SQLAlchemy models
│   └── tests/
│
├── shared/                        # Shared utilities (deprecated)
├── tests/                         # Integration tests
└── examples/                      # Example data and usage
    ├── sample_baseline.csv
    ├── sample_production.csv
    └── sample_pii_data.csv

Version History

Version	Date	Changes
0.1.0	TBD	Phase 1 - Foundation (3 tools)
0.2.0	TBD	Phase 2 - Monitoring & Costs
0.3.0	TBD	Phase 3 - Security & Compliance
0.4.0	TBD	Phase 4 - Quality & Comparison
1.0.0	TBD	Phase 5 - Full Release (14 tools)

Support

For issues or feature requests, refer to the project documentation or contact the development team.

Last Updated: December 2024

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AI Tools Suite - Product Manual

Table of Contents

Overview

Target Users

Architecture

System Overview

Tech Stack

API Design

Tool Catalog

Product Roadmap

Phase 1: Foundation (MVP)

Phase 2: Monitoring & Costs

Phase 3: Security & Compliance

Phase 4: Quality & Comparison

Phase 5: Analytics & Control

Roadmap Visualization

Success Metrics

Installation

Prerequisites

Quick Setup with Docker

Manual Setup

Backend (FastAPI)

Frontend (SvelteKit)

Environment Variables

Quick Start

Accessing the Dashboard

API Usage

User Guide

Inference Estimator

How to Use

Example Calculation

Expected Outcome

Data Integrity Audit

How to Use

Understanding the Results

Expected Outcome

API Endpoints

Sample API Response

Privacy Scanner

How to Use

PII Detection Patterns

Risk Levels Explained

API Endpoints

Redaction Modes

Expected Outcome

Sample API Response

Detailed Tool Documentation

1. Model Drift Monitor

2. Vendor Cost Tracker

3. Security Tester

4. Data History Log

5. Model Comparator

6. Privacy Scanner

7. Label Quality Scorer

8. Inference Estimator

9. Data Integrity Audit

10. Content Performance

11. Safety/Bias Checks

12. Profitability Analysis

13. Emergency Control

14. Result Interpretation

Directory Structure

Version History

Support

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