Welcome! Use Cases and AI Architecture

Use Cases and AI Architecture

This comprehensive guide provides detailed use case examples and implementation strategies for AI solutions across various industries. Each example follows a structured approach: defining specific goals, starting with broad categories, and progressively refining your solution through iterative implementation and testing.

Planning Your AI Solution

Once you've identified a use case, break it down into incremental steps:

  • Define specific goals - What exact problem are you solving?

  • Start broad, then refine - Begin with general categories before moving to specific details

  • Implement, test, and iterate - Build your solution progressively

Choosing the Right AI Architecture

Based on your use case and available data, select the appropriate AI architecture:

Large Language Model (LLM) Solutions

  • Choose when: Your data is primarily text-based (PDFs, Word documents, text files)
  • What it does: Creates an AI subject matter expert you can chat with through text prompts
  • Key benefit: Provides conversational access to knowledge in your documents
  • Best for: Document Q&A, Customer Support, Knowledge Management

Computer Vision Solutions

Computer vision solutions require image data (still images or video) and come in two main types:

Image Classifiers

  • Choose when: You need to categorize entire images
  • What it does: Labels a complete image based on its content
  • Example: A classifier trained to identify "Hot Dog" vs. "Not Hot Dog" will label the entire image based on whether it contains a hot dog
  • Best for: Quality Control, Medical Imaging, Product Classification

Object Detectors

  • Choose when: You need to locate specific objects within images
  • What it does: Finds objects within images, draws bounding boxes around them, and applies appropriate labels
  • Example: An object detector trained on hot dogs will draw a bounding box around any hot dog in an image and label it specifically, rather than labeling the entire image
  • Best for: Security Monitoring, Inventory Management, Medical Analysis

Example 1: Document Intelligence System

Creating an AI assistant for enterprise document management:

Define document categories:

  • Document Type: Contracts, Invoices, Reports, Policies, Procedures...
  • Department: HR, Finance, Legal, Operations, IT...
  • Priority Level: Urgent, High, Medium, Low...

Implementation Steps:

  1. Start with document classification:

    • Use LLM Dataset Generator to process your document collection

    • Train initial model on broad document categories

    • Test classification accuracy with sample documents

  2. Add intelligent search capabilities:

    • Implement RAG system for document Q&A functionality

    • Create vector database from processed documents

    • Test search accuracy with common queries

    • Add department-specific filtering and routing

Example 2: Manufacturing Quality Control

Implementing automated defect detection in a production line:

Define classification categories:

  • Defect Type: Crack, Scratch, Discoloration, Missing Component...
  • Severity Level: Critical, Major, Minor, Acceptable...
  • Product Category: Electronics, Automotive Parts, Consumer Goods...

Implementation Steps:

  1. Start with basic pass/fail detection:

    • Train a binary classifier to distinguish "Good" vs "Defective" products

    • Validate with production line test data

    • Fine-tune confidence thresholds to minimize false positives

  2. Add defect type classification:

    • Train additional models to identify specific defect types

    • Integrate with existing pass/fail model

    • Test on diverse production scenarios

    • Add severity classification for quality grading

Example 3: Retail Inventory Management

Automating product recognition and inventory tracking:

Define product categories:

  • Product Category: Clothing, Electronics, Home Goods, Food, Books...
  • Brand: Nike, Apple, Samsung, IKEA, Generic...
  • Condition: New, Used, Damaged, Returned...

Implementation Steps:

  1. Start with basic product detection:

    • Train object detector to locate products in images

    • Test detection accuracy across different product types

    • Optimize bounding box accuracy and confidence thresholds

  2. Add product classification:

    • Train image classifier for product category identification

    • Combine detection and classification in sequence

    • Add brand recognition for high-value items

    • Implement condition assessment for used goods

Example 4: Medical Image Analysis

Developing diagnostic assistance for medical imaging:

Define analysis categories:

  • Image Type: X-Ray, MRI, CT Scan, Ultrasound, Pathology...
  • Anatomical Region: Chest, Abdomen, Brain, Extremities...
  • Condition Indicators: Normal, Abnormal, Requires Review...

Implementation Steps:

  1. Start with image type classification:

    • Train classifier to identify different medical imaging modalities

    • Validate with diverse medical image datasets

    • Ensure high accuracy for proper routing to specialized models

  2. Add anatomical region detection:

    • Train object detector to locate anatomical structures

    • Create region-specific analysis pipelines

    • Add condition screening for common abnormalities

    • Implement confidence scoring for diagnostic recommendations

Example 5: Security and Surveillance

Building intelligent security monitoring systems:

Define security categories:

  • Object Detection: Person, Vehicle, Package, Weapon...
  • Activity Classification: Normal, Suspicious, Unauthorized Access...
  • Time-based Analysis: Day, Night, Business Hours, After Hours...

Implementation Steps:

  1. Start with basic object detection:

    • Train object detector to identify people and vehicles

    • Test across different lighting conditions and camera angles

    • Optimize for real-time processing requirements

  2. Add behavioral analysis:

    • Implement activity classification for suspicious behaviors

    • Add time-based context for access control

    • Create alert system for unauthorized activities

    • Integrate with existing security infrastructure

Example 6: Customer Support Automation

Creating intelligent customer service solutions:

Define support categories:

  • Query Type: Technical Support, Billing, Product Information, Complaints...
  • Priority Level: Critical, High, Medium, Low...
  • Resolution Path: Self-Service, Agent Escalation, Specialist Transfer...

Implementation Steps:

  1. Start with query classification:

    • Use LLM Dataset Generator with support documentation

    • Train model to categorize incoming customer queries

    • Test accuracy with historical support tickets

  2. Add intelligent response generation:

    • Implement RAG system for automated responses

    • Add priority assessment for routing decisions

    • Create escalation triggers for complex issues

    • Integrate with CRM and ticketing systems

Next Steps

After identifying your use case and selecting the appropriate architecture:

  • For more information on the webAI application where these flows are built - Navigator, see our Navigator Overview

  • For more information on building some of these flows inside of Navigator, see our Quick Start guides