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Key Concepts

Before diving into Infactory, it’s helpful to understand a few key concepts that make up the platform. This guide introduces the core components and how they work together.

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The Infactory Platform Architecture

Infactory is built around a simple yet powerful workflow that transforms your structured data into intelligent API endpoints.

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Components of Infactory

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How Infactory Works

Understanding Infactory’s approach helps you get the most out of the platform.

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1. Data Connection & Analysis

When you connect a data source, Infactory:

• Takes a small sample of your data (about 50 rows)
• Analyzes the schema to understand data types and relationships
• Generates a data model that powers intelligent query generation
• Does not copy or store your actual data

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2. Intelligent Query Generation

Based on your data schema, Infactory:

• Automatically generates approximately 12 common query patterns
• Creates queries that can answer a wide range of questions
• Implements “slots” to make queries reusable for similar questions
• Allows you to create custom queries for specific needs

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3. Query Generalization Through Slots

Slots are what make Infactory queries so powerful:

def run(parameters, context):
    # This query answers "What is the average {metric} by {category}?"
    metric = parameters["metric"]  # A slot for any numeric field
    category = parameters["category"]  # A slot for any categorical field
    
    df = context.load_table("my_table")
    result = df.groupby(category).mean(metric)
    
    return result

A single query with slots can answer hundreds of related questions, without you having to create each variant manually.

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4. Direct Execution Without LLMs

Unlike many AI solutions, Infactory queries:

• Execute directly against your database
• Don’t use an LLM for each query execution
• Return consistent, reliable results
• Perform at database speed, not AI inference speed

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5. Unified API Gateway

When deployed, your queries become available through:

• Direct endpoints for specific queries with known parameters
• A unified endpoint that routes natural language questions to the right query

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The Infactory Difference

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Traditional AI-Powered Data Apps

Most approaches to building AI-powered data applications face these challenges:

1. Unpredictable Results: LLMs can hallucinate or provide inconsistent answers
2. Performance Issues: Every question requires a full LLM processing cycle
3. Complex Implementation: Requires extensive prompt engineering and fine-tuning
4. Governance Challenges: Difficult to control exactly what can and can’t be answered

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The Infactory Approach

Infactory takes a fundamentally different approach:

1. Deterministic Results: Queries execute directly against your data, ensuring consistent answers
2. High Performance: No LLM inference required for query execution
3. Simple Implementation: Auto-generated queries with minimal setup
4. Clear Governance: Precise control over what questions can be answered

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Key Benefits

• Simplified AI Development: Build AI-powered data applications without complex prompt engineering
• Consistency & Control: Get reliable, consistent answers to data questions
• Speed & Scalability: Process queries at database speed, not AI inference speed
• Security & Compliance: Keep your data secure in your database, with no external data storage

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Next Steps

Now that you understand the key concepts behind Infactory, you’re ready to start building. Continue to our Quickstart guide to create your first project, or explore our Core Features to learn more about each component in detail.