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Using the Chatbot

Complete guide for interacting with the Poolula Platform AI chatbot to get answers about your business, properties, and documents.

Overview

The Poolula chatbot uses AI-powered search to answer questions by:

  1. Searching your database for properties, transactions, and financial data
  2. Searching your documents for formation docs, contracts, and policies
  3. Combining results to provide comprehensive answers with citations

Quick Start

Access the Chatbot

# 1. Start the API server
uv run uvicorn apps.api.main:app --reload --port 8082

# 2. Open browser
open http://localhost:8082

# 3. Ask a question in the chat interface

First Questions to Try

Property information:

  • "What is our property address?"
  • "What properties do we own?"

Financial data:

  • "What was my rental income last month?"
  • "Show me expenses for this year"

Documents:

  • "What documents do we have?"
  • "What's our business purpose in the operating agreement?"

How the Chatbot Works

graph TD
    A[Your Question] --> B{Question Analysis}
    B --> C{Choose Tools}
    C --> D[Search Database]
    C --> E[Search Documents]
    C --> F[List Documents]
    D --> G[Combine Results]
    E --> G
    F --> G
    G --> H[Generate Answer]
    H --> I[Show Response + Sources]

    style D fill:#e3f2fd
    style E fill:#fff3e0
    style F fill:#f3e5f5

Behind the Scenes

1. Your question is analyzed

  • AI determines what type of information you need
  • Decides which tools to use (database, documents, or both)

2. Tools search for relevant information

  • Database tool: Queries properties, transactions, obligations
  • Document search: Finds relevant text in your ingested documents
  • Document list: Shows available documents

3. AI generates an answer

  • Combines information from multiple sources
  • Provides citations showing where information came from
  • Formats answer in clear, natural language

Types of Questions

Property Questions

What you can ask:

  • Property addresses and details
  • Acquisition information
  • Depreciation basis
  • Placed-in-service dates

Examples:

"What is our property's address?"
→ Searches database for property record

"What is the land basis for our property?"
→ Queries financial data from database

"When was our property placed in service?"
→ Retrieves depreciation start date

Financial Questions

What you can ask:

  • Rental income and expenses
  • Transaction history
  • Category breakdowns
  • Date-range queries

Examples:

"What was my rental income in August 2024?"
→ Filters transactions by category and date

"Show me all utility expenses this year"
→ Queries expenses by category

"What were my total expenses in 2024?"
→ Aggregates transaction amounts

Document Questions

What you can ask:

  • What documents exist
  • Content within documents
  • Formation details
  • Compliance requirements

Examples:

"What documents do we have?"
→ Lists all ingested documents

"What's our LLC's business purpose?"
→ Searches formation documents

"Who are the members of our LLC?"
→ Searches operating agreement

Hybrid Questions

What you can ask:

  • Questions combining database + documents
  • Cross-referencing multiple sources

Examples:

"What properties do we own and what documents mention them?"
→ Queries database for properties
→ Searches documents for property references
→ Combines both results

"Show me our EIN and where it's mentioned in our documents"
→ Gets EIN from database
→ Finds document references

Asking Effective Questions

Be Specific

Good examples:

  • ✅ "What was my rental income in August 2024?"
  • ✅ "Show me utility expenses for this year"
  • ✅ "What is our property's land basis?"

Less effective:

  • ❌ "Tell me about money"
  • ❌ "What happened?"
  • ❌ "Show me stuff"

Include Key Details

Time periods:

  • "in August 2024"
  • "for this year"
  • "last month"

Categories:

  • "rental income"
  • "utility expenses"
  • "property management fees"

Specific values:

  • "our property's basis"
  • "land basis"
  • "building depreciation"

One Question at a Time

Good:

"What was my rental income in August 2024?"
... get answer ...
"What were my expenses in August 2024?"

Less effective:

"What was my rental income in August 2024 and what were my expenses and
what's my net income and what documents talk about rental properties?"

(AI can handle complex questions, but simpler is often better)

Understanding Responses

Response Format

Typical response structure:

[Answer text with relevant information]

Sources:
- [Source 1: Database query or document reference]
- [Source 2: Additional source if multiple tools used]

Source Citations

Database sources:

Source: Database Query (properties)
Type: query_database

Means: Information came from querying the SQLite database

Document sources:

Source: Poolula LLC Operating Agreement
Type: search_document_content
Relevance: 0.85

Means: Information found in specific document via semantic search

Interpreting Tool Usage

Look at the sources to understand how your question was answered:

Database query:

  • Fast, precise answers
  • For structured data (properties, transactions, dates, amounts)
  • Returns exact values

Document search:

  • Semantic search through text
  • For unstructured information (formation details, agreements, policies)
  • Returns relevant passages

Multiple sources:

  • Hybrid query used multiple tools
  • More comprehensive answer
  • Cross-referenced information

Common Question Patterns

"What" Questions

Property info:

  • "What is our property address?"
  • "What is our EIN number?"

Financial data:

  • "What was my rental income in [month]?"
  • "What were my expenses for [category]?"

"Show" / "List" Questions

Transactions:

  • "Show me all expenses in 2024"
  • "List rental income by month"

Documents:

  • "Show me what documents we have"
  • "List all formation documents"

"When" Questions

Dates and deadlines:

  • "When was our property placed in service?"
  • "When is our annual report due?"

"How much" / "How many" Questions

Amounts and counts:

  • "How much did we pay in utilities?"
  • "How many transactions do we have?"

Tips for Best Results

1. Use Natural Language

You don't need special syntax - just ask naturally:

✅ "What's our property worth?" ✅ "How much rental income did we get last month?"

2. Start Broad, Then Narrow

Strategy:

1. "What documents do we have?"
   → See what's available

2. "What's in our operating agreement?"
   → Search specific document

3. "Who are the members listed in the operating agreement?"
   → Get specific detail

3. Check Sources

Always review the sources shown with answers:

  • Verify information came from correct source
  • Note relevance scores for document searches
  • Cross-reference if needed

4. Rephrase if Needed

If an answer isn't helpful, try rephrasing:

First try: "Show me money stuff" → Too vague

Second try: "What was my rental income last month?" → Specific and clear

5. Use Context from Previous Questions

The chatbot maintains conversation context:

You: "What properties do we own?"
Bot: [Lists 900 S 9th St property]

You: "What's the land basis for that property?"
Bot: [Understands "that property" refers to 900 S 9th St]

Conversation Features

Session Context

The chatbot remembers previous questions in your conversation:

  • References to "that property", "this document", etc. work
  • Context helps with follow-up questions
  • Maintained per browser session

New Conversation

Start a new session if:

  • Changing topics completely
  • Getting confused responses
  • Want to clear context

(Refresh the page or use "New Conversation" if available)

Sample Questions by Persona

See Sample Questions for 133 example questions organized by:

1. New LLC Owner

  • Formation and structure
  • Annual compliance
  • Basic operations

2. Bookkeeper

  • Property basis & depreciation
  • Transaction analysis
  • Chart of accounts

3. Property Manager

  • Property operations
  • Vendor management
  • Maintenance tracking

4. Tax Preparer

  • Depreciation schedules
  • Deduction tracking
  • Basis calculations
  • Tax form preparation

Limitations & Known Issues

Current Limitations

Time-based queries:

  • "last month" works
  • Specific months work: "August 2024"
  • Relative dates might be less accurate

Calculations:

  • Basic aggregations work (sum, count)
  • Complex calculations may require follow-up
  • Tax calculations not automated (yet)

Document search:

  • Works on ingested documents only
  • Scanned PDFs need OCR (not yet implemented)
  • Images in documents not searchable

When Chatbot Can't Help

For:

  • Creating new records → Use API or manual entry
  • Updating data → Use API endpoints
  • Deleting information → Use API with caution
  • Complex multi-step workflows → Manual process

Remember: Chatbot is read-only - it searches and reports, but doesn't modify data.

Troubleshooting

No Answer or Empty Response

Possible causes:

  • No data exists for that query
  • Question too vague
  • Tool selection error

Try:

  • Rephrase question more specifically
  • Verify data exists (check database or documents)
  • Break complex question into simpler parts

Wrong Information

If answer seems incorrect:

  1. Check sources shown
  2. Verify data in source (database or document)
  3. Report issue if source is correct but answer is wrong

Slow Responses

Normal response time: 1-3 seconds

Slower responses happen when:

  • First query after startup (model loading)
  • Complex document searches
  • Large date ranges

Chatbot Not Available

Check:

# Is server running?
curl http://localhost:8082/health

# Start if needed
uv run uvicorn apps.api.main:app --reload --port 8082

Advanced Usage

Asking About Specific Documents

"What's in the Articles of Organization?"
"Search the operating agreement for member information"
"Find insurance policy details in our documents"

Filtering by Date Range

"Show transactions between January and March 2024"
"What was rental income from July to September?"

Aggregating Data

"Show total expenses by category for 2024"
"Break down rental income by month"
"What's my average monthly income?"

Next Steps

Learn more:

For developers: