> ## Documentation Index
> Fetch the complete documentation index at: https://docs.flowx.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Agent Builder use cases

> Real-world examples of what you can build with FlowX.AI Agent Builder.

<Warning>
  **Preview**

  Agent Builder is currently in preview and may change before general availability.
</Warning>

Agent Builder supports a wide range of AI-powered automation scenarios. Below are the primary categories with examples drawn from production implementations.

## Document processing and extraction

Extract structured data from unstructured documents — invoices, contracts, shipping documents, financial statements — using TEXT\_EXTRACTION nodes with tailored prompts per document type.

<AccordionGroup>
  <Accordion title="Logistics document processing" icon="truck">
    An email-triggered pipeline processes carrier emails and their attachments:

    * **Classify** documents by type (BOL, invoice, rate confirmation, lumper receipt, fuel receipt, and more)
    * **Extract** structured fields from each document type using specialized TEXT\_EXTRACTION nodes
    * **Reconcile** extracted data against a Transportation Management System (TMS)
    * **Surface exceptions** for human review via task management

    **AI nodes used:** TEXT\_EXTRACTION (12 document types), TEXT\_UNDERSTANDING (12 comparison workflows), TEXT\_GENERATION (email summary)

    **Patterns involved:** [Fan-out extraction](../patterns/fan-out-extraction), [AI comparison](../patterns/ai-comparison-reconciliation)
  </Accordion>

  <Accordion title="Mortgage document validation" icon="building-columns">
    Process mortgage application packages by extracting product data from bank documentation:

    * **Extract** structured product details (interest rates, fees, insurance requirements) from bank product sheets
    * **Weight** income and debt values per bank-specific rules using AI extraction
    * **Validate** legal/regulatory eligibility using AI understanding

    **AI nodes used:** TEXT\_EXTRACTION (income/debt/product extraction), TEXT\_UNDERSTANDING (eligibility checks)

    **Patterns involved:** [Hybrid AI + business rules](../patterns/hybrid-ai-rules), [Fan-out extraction](../patterns/fan-out-extraction)
  </Accordion>
</AccordionGroup>

***

## Conversational AI

Build chat-based assistants that understand user intent, maintain conversation context, and provide personalized responses grounded in your data.

<AccordionGroup>
  <Accordion title="Mortgage advisor chatbot" icon="comments">
    A conversational assistant that guides users through mortgage product selection:

    * **Detect intent** — classify user messages as greetings, product inquiries, data input, or other
    * **Route** to specialized handlers (small talk, personalized offers, knowledge base Q\&A)
    * **Generate** personalized mortgage consultant reports with comparative cost tables
    * **Maintain** conversation history across sessions

    **AI nodes used:** TEXT\_UNDERSTANDING (intent detection), TEXT\_GENERATION (responses, reports), CUSTOM\_AGENT (knowledge base RAG)

    **Patterns involved:** [Intent classification](../patterns/intent-classification-routing), [Knowledge base RAG](../patterns/knowledge-base-rag), [Session state](../patterns/session-state-management)
  </Accordion>
</AccordionGroup>

***

## AI-augmented decision making

Combine AI capabilities with deterministic business logic for auditable, explainable decision-making.

<AccordionGroup>
  <Accordion title="Product eligibility and scoring" icon="scale-balanced">
    Evaluate financial product eligibility using a pipeline that mixes AI and business rules:

    * **AI extracts** income weights and debt factors from bank-specific rule documents
    * **Business rules** compute PMT, DTI ratios, maximum loan amounts, and currency conversions
    * **AI filters** products by qualitative criteria (loan type, sustainability features) with fuzzy matching
    * **Business rules** normalize scores and rank the top products
    * **AI generates** a professional consultant report with the final recommendations

    **AI nodes used:** TEXT\_EXTRACTION, TEXT\_UNDERSTANDING, TEXT\_TRANSFORMATION, TEXT\_GENERATION

    **Patterns involved:** [Hybrid AI + business rules](../patterns/hybrid-ai-rules)
  </Accordion>

  <Accordion title="Document reconciliation" icon="code-compare">
    Compare AI-extracted document data against system-of-record values:

    * **Compare** field-by-field with structured exception reporting
    * **Generate** match rates and confidence scores per document
    * **Flag** mismatches, missing fields, and derived value discrepancies
    * **Route** exceptions to human reviewers based on severity

    **AI nodes used:** TEXT\_UNDERSTANDING (comparison agents per document type)

    **Patterns involved:** [AI comparison and reconciliation](../patterns/ai-comparison-reconciliation)
  </Accordion>
</AccordionGroup>

***

## Email automation

Process incoming emails and their attachments automatically using email triggers and AI workflows.

<AccordionGroup>
  <Accordion title="Carrier email processing" icon="envelope">
    Monitor an email inbox for incoming carrier communications:

    * **Trigger** workflows automatically when emails arrive (IMAP integration)
    * **Summarize** email content and classify intent (invoice submission, payment inquiry, dispute)
    * **Extract** and process all attachments through the document pipeline
    * **Look up** related orders in external systems (TMS) by BOL number
    * **Notify** relevant staff of exceptions via email templates

    **AI nodes used:** TEXT\_GENERATION (email summary), TEXT\_EXTRACTION (attachments), TEXT\_UNDERSTANDING (comparison)

    **Data sources:** Email Trigger, REST API (TMS), FlowX Database
  </Accordion>
</AccordionGroup>

***

## Building your own use case

<Steps>
  <Step title="Identify the AI tasks">
    Map which steps in your process need AI (extraction, classification, generation, comparison) versus deterministic logic (calculations, routing, validation).
  </Step>

  <Step title="Choose your patterns">
    Select from the [AI patterns](../patterns/overview) that match your needs. Most apps combine 2-4 patterns.
  </Step>

  <Step title="Design the data flow">
    Define your data sources, data model, and how data moves between processes, workflows, and AI nodes.
  </Step>

  <Step title="Build incrementally">
    Start with a single AI node (e.g., one document type extraction), validate it works, then expand to the full pipeline.
  </Step>

  <Step title="Add human review">
    Use task management views for exception handling and quality assurance, especially for high-stakes decisions.
  </Step>
</Steps>

## Related resources

<CardGroup cols={2}>
  <Card title="Tutorials" icon="graduation-cap" href="../tutorials/overview">
    End-to-end build guides based on production apps
  </Card>

  <Card title="AI patterns" icon="puzzle-piece" href="../patterns/overview">
    Reusable architectural patterns
  </Card>

  <Card title="Node types" icon="diagram-project" href="./node-types">
    Detailed node reference
  </Card>

  <Card title="Using agents" icon="play" href="../using-agents/overview">
    Deploy agents in your apps
  </Card>
</CardGroup>