Spreadsheet Intelligence with Natural Language Queries

Build an AI-powered system that lets teams query spreadsheets using natural language—reducing data retrieval time from hours to seconds and democratizing access to business intelligence.

What You'll Build

An intelligent spreadsheet assistant that:

✅ Ingests Excel/CSV files and Google Sheets
✅ Answers questions in natural language
✅ Performs complex calculations automatically
✅ Provides cell-level citations for verification
✅ Identifies patterns and anomalies
✅ Generates self-service reports

Time to build: 2-3 hours Difficulty: Intermediate Best for: Operations teams, finance departments, business analysts, executive dashboards

Blocks & Tools You'll Use

Core Blocks

Agent Block - For natural language understanding and data analysis
Knowledge Base Tool - To index and query spreadsheet data
Google Sheets Tool - For live spreadsheet connections
Function Block - For custom calculations and data processing
Condition Block - To route complex queries

Supporting Tools

File Tool - For CSV/Excel uploads
Thinking Tool - For complex analytical reasoning
Response Block - To return formatted answers
Mail Tool or Slack Tool - For automated alerts

Use Case Scenarios

Scenario 1: Financial Query

Question: "What's our cash position by week for Q4?" Workflow: Parses question → Identifies relevant sheet → Aggregates weekly data → Returns table with cell citations

Scenario 2: Anomaly Detection

Question: "Show me all transactions over $50K that are 60+ days overdue" Workflow: Filters transactions → Checks payment dates → Calculates age → Returns flagged items

Scenario 3: Growth Analysis

Question: "Which accounts increased spending by more than 20% month-over-month?" Workflow: Calculates MoM changes → Identifies growth accounts → Returns ranked list with percentages

Implementation

Step 1: Set Up Data Ingestion

Create a new workflow named "Spreadsheet Intelligence"
Add a Starter Block with flexible inputs:

Trigger Type: Webhook, Chat Interface, or Slack Command

Input Options:
- Question (text, required): Natural language query
- Spreadsheet Source (optional): Specific sheet/file name
- Date Range (optional): Filter for time-bound queries

Examples:
- "What's our revenue by region for Q3?"
- "Show me overdue invoices"
- "Which products had negative margins last month?"

Add Google Sheets Tool or File Tool for data sources:
- Google Sheets: Connect with OAuth for live data
- CSV/Excel: Use File Tool for upload

Step 2: Create Spreadsheet Knowledge Base

Add Knowledge Base Tool
Configure for spreadsheet data:

Knowledge Base Name: company_spreadsheets

Data Processing:
- Ingest spreadsheet data with schema understanding
- Index by:
  * Column names (for semantic search)
  * Data types (numbers, dates, text)
  * Sheet names (for multi-sheet files)
- Create searchable metadata:
  * Date ranges
  * Unique values (categories, regions, products)
  * Summary statistics (min, max, avg)

Refresh Strategy:
- Real-time (for Google Sheets)
- On-demand (for uploaded files)
- Scheduled (daily sync)

Process spreadsheets to extract:
- Column headers and data types
- Relationships between sheets
- Common query patterns

Step 3: Build Query Understanding Agent

Add an Agent Block for natural language processing
Configure with Knowledge Base Tool and Thinking Tool:

Model: Claude 3.7 Sonnet (excellent at structured data)

System Prompt:
"You are a spreadsheet intelligence agent. Your job is to understand natural language questions about business data and translate them into structured queries.

AVAILABLE DATA SOURCES:
<knowledge_base: company_spreadsheets>

QUERY UNDERSTANDING STEPS:

1. **Parse Intent:**
   - What metric is being requested? (revenue, transactions, accounts, etc.)
   - What aggregation is needed? (sum, average, count, growth rate)
   - What filters apply? (date ranges, categories, thresholds)
   - What grouping is needed? (by region, by product, by time period)

2. **Identify Data Location:**
   - Which spreadsheet contains this data?
   - Which sheet(s) within that spreadsheet?
   - Which columns are relevant?

3. **Determine Calculation:**
   - Is this a simple lookup?
   - Does it require aggregation (SUM, AVG, COUNT)?
   - Does it need temporal analysis (MoM, YoY, trends)?
   - Are there conditional filters (>50K, overdue, growth >20%)?

4. **Plan Response Format:**
   - Should it return a single number?
   - A table/list?
   - A time series?
   - Comparisons?

USER QUESTION: <start.question>

OUTPUT FORMAT (JSON):
{
  'intent': 'revenue_by_region' | 'overdue_invoices' | 'growth_analysis',
  'data_source': {
    'spreadsheet': 'Q4_Financials.xlsx',
    'sheets': ['Revenue', 'Accounts'],
    'columns': ['Region', 'Amount', 'Date']
  },
  'filters': [
    {'column': 'Date', 'operator': '>=', 'value': '2024-10-01'},
    {'column': 'Amount', 'operator': '>', 'value': 1000}
  ],
  'aggregation': 'SUM' | 'AVG' | 'COUNT',
  'grouping': ['Region'] | ['Product', 'Month'],
  'calculation': 'simple' | 'growth_rate' | 'comparison',
  'response_format': 'number' | 'table' | 'list' | 'timeseries'
}"

User Prompt:
"Analyze this question: <start.question>"

Add Tools to the agent:
- Knowledge Base Search - Query spreadsheet index
- Thinking Tool - For complex query planning

Step 4: Execute Data Retrieval & Calculation

Add another Agent Block for data execution
Configure with Google Sheets Tool and Function Block:

Model: Claude 3.7 Sonnet

System Prompt:
"You are a data execution agent. Based on the query plan, retrieve and calculate the requested information.

QUERY PLAN: <query_agent.output>

EXECUTION STEPS:

1. **Data Retrieval:**
   - Access the identified spreadsheet/sheets
   - Apply filters to narrow down rows
   - Extract relevant columns

2. **Calculation:**
   - Perform required aggregations
   - Calculate growth rates if needed
   - Apply conditional logic
   - Sort/rank results if requested

3. **Citation:**
   - Track which cells contributed to the answer
   - Record cell references (Sheet!A1:B10)
   - Note formulas used in calculation

4. **Quality Check:**
   - Verify data completeness
   - Check for null values
   - Validate calculation logic

OUTPUT FORMAT (JSON):
{
  'answer': {
    'type': 'number' | 'table' | 'list',
    'value': <calculated result>,
    'unit': '$' | '%' | 'count',
    'formatted': '$125,450' | '23.5%' | '45 accounts'
  },
  'citations': [
    {
      'sheet': 'Revenue',
      'range': 'A2:C50',
      'description': 'Q3 revenue data by region'
    }
  ],
  'metadata': {
    'rows_analyzed': 150,
    'date_range': '2024-07-01 to 2024-09-30',
    'data_freshness': '2024-11-01 09:00 AM'
  },
  'confidence': 0.95
}"

User Prompt:
"Execute the query plan and return results with citations."

Add Tools:
- Google Sheets Tool - Read spreadsheet data
- Function Block - For complex calculations (Python/JS)

Step 5: Add Pattern Detection & Insights

Add an Agent Block for anomaly detection
Configure to run in parallel with main query:

Model: Claude 3.7 Sonnet

System Prompt:
"You are an insights agent. While answering the user's question, also identify interesting patterns, anomalies, or risks.

USER QUESTION: <start.question>
QUERY RESULTS: <execution_agent.output>
RAW DATA: <from Google Sheets Tool>

INSIGHT DETECTION:

1. **Anomalies:**
   - Values significantly outside normal range
   - Sudden spikes or drops in time series
   - Unusual patterns (e.g., account spending 10x normal)

2. **Risks:**
   - Cash flow concerns (declining balances)
   - Payment delays (overdue invoices increasing)
   - Concentration risk (top 3 customers = 80% revenue)

3. **Opportunities:**
   - Growth accounts (increasing spend trends)
   - Seasonal patterns (predictable demand cycles)
   - Efficiency improvements (process bottlenecks)

4. **Data Quality:**
   - Missing values
   - Inconsistent formatting
   - Outliers that might be errors

OUTPUT FORMAT (JSON):
{
  'insights': [
    {
      'type': 'anomaly' | 'risk' | 'opportunity',
      'severity': 'low' | 'medium' | 'high',
      'finding': 'Top customer spending decreased 35% in October',
      'details': 'Account ABC went from $50K/month to $32K/month',
      'action': 'Recommend account review meeting',
      'citation': 'Revenue!B15:B20'
    }
  ],
  'summary': '2 risks identified, 1 opportunity flagged'
}"

User Prompt:
"Analyze the data for patterns, anomalies, and insights."

Step 6: Format & Present Results

Add a Function Block to format the response
Use Python or JavaScript to create user-friendly output:

def format_response(query, answer, citations, insights):
    """Format the AI response with answer, citations, and insights"""

    # Build main answer
    response = {
        'question': query,
        'answer': answer['formatted'],
        'details': {},
        'sources': [],
        'insights': []
    }

    # Add detailed breakdown if table/list
    if answer['type'] == 'table':
        response['details'] = {
            'headers': answer['value']['headers'],
            'rows': answer['value']['rows'],
            'total': answer['value']['total']
        }

    # Add source citations
    for citation in citations:
        response['sources'].append({
            'sheet': citation['sheet'],
            'range': citation['range'],
            'description': citation['description']
        })

    # Add insights
    for insight in insights:
        if insight['severity'] in ['medium', 'high']:
            response['insights'].append({
                'type': insight['type'],
                'message': insight['finding'],
                'action': insight['action']
            })

    # Format for different output channels
    if output_channel == 'slack':
        return format_slack_message(response)
    elif output_channel == 'email':
        return format_email(response)
    else:
        return response  # JSON for API

def format_slack_message(response):
    """Create Slack-formatted message with blocks"""
    return {
        'blocks': [
            {
                'type': 'section',
                'text': {
                    'type': 'mrkdwn',
                    'text': f"*Question:* {response['question']}"
                }
            },
            {
                'type': 'section',
                'text': {
                    'type': 'mrkdwn',
                    'text': f"*Answer:* {response['answer']}"
                }
            },
            {
                'type': 'context',
                'elements': [
                    {
                        'type': 'mrkdwn',
                        'text': f"_Sources: {', '.join([s['sheet'] for s in response['sources']])}_"
                    }
                ]
            }
        ]
    }

Add Response Block to return formatted answer

Step 7: Distribution & Alerts

Add Condition Block to route based on insights:

// Check if high-priority insights were found
<insights_agent.insights>.some(i => i.severity === 'high')

If HIGH priority (needs immediate attention):

Add Slack Tool or Mail Tool

Send alert to relevant team:

To: finance-team@company.com
Subject: 🚨 Cash Flow Alert Detected

The spreadsheet intelligence system identified a high-priority issue:

Finding: <insights_agent.finding>
Details: <insights_agent.details>
Recommended Action: <insights_agent.action>

View full data: [Link to spreadsheet]

If NORMAL query:
- Return answer via Response Block
- Log query for analytics

Step 8: Learning & Optimization

Add Database Tool (Supabase/MongoDB) to log queries:

CREATE TABLE spreadsheet_queries (
    id SERIAL PRIMARY KEY,
    question TEXT,
    intent TEXT,
    execution_time FLOAT,
    confidence FLOAT,
    user_id TEXT,
    timestamp TIMESTAMP,
    feedback TEXT  -- user can rate answer quality
);

Track common patterns to improve:
- Which questions are asked most frequently?
- Which queries have low confidence scores?
- Where does data retrieval fail?
Use insights to:
- Precompute common queries
- Suggest questions to users
- Improve prompt templates

Workflow Diagram

[User Question] → [Starter Block]
         ↓
[Parse Intent] ← [Agent + Knowledge Base + Thinking]
         ↓
[Execute Query] ← [Agent + Google Sheets + Function]
         ↓
      ┌──┴──┐
      ↓     ↓
[Get Answer]  [Detect Insights] ← [Agent + Thinking]
      ↓         ↓
      └────┬────┘
           ↓
  [Format Response] ← [Function Block]
           ↓
   [High Priority?] ← [Condition Block]
        ↙     ↘
  [Send Alert]  [Return Answer]
    ↓             ↓
  [Mail/Slack]  [Response Block]

Advanced Enhancements

1. Multi-Sheet Analysis

Add Loop Block to query across multiple sheets:

Compare data across departments
Aggregate company-wide metrics
Identify cross-functional patterns

2. Time-Series Forecasting

Add Function Block with forecasting library:

from prophet import Prophet
import pandas as pd

def forecast_revenue(historical_data, periods=12):
    """Predict future revenue based on historical trends"""
    df = pd.DataFrame(historical_data)
    model = Prophet()
    model.fit(df)
    future = model.make_future_dataframe(periods=periods, freq='M')
    forecast = model.predict(future)
    return forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]

3. Interactive Dashboards

Add API Tool to send data to dashboard tools:

Metabase
Tableau
Looker
Custom React dashboards

4. Voice Queries

Add Speech-to-Text Tool:

Enable voice questions via phone or Slack
Return answers as audio responses
Perfect for mobile/field teams

5. Scheduled Reports

Add Scheduler for automated reporting:

Daily revenue summaries
Weekly pipeline updates
Monthly financial close reports
Quarterly board decks

6. Collaborative Queries

Add Slack Tool for team collaboration:

Share queries and answers in channels
Let multiple users refine questions
Build organizational query library

Testing Your Workflow

Test Case 1: Simple Aggregation

{
  "question": "What's our total revenue for Q3 2024?"
}

Expected: Single number with cell citations from Revenue sheet

Test Case 2: Filtered List

{
  "question": "Show me all invoices over $50K that are more than 60 days overdue"
}

Expected: Table with invoice numbers, amounts, ages, customer names

Test Case 3: Growth Analysis

{
  "question": "Which accounts increased their spending by more than 20% in October compared to September?"
}

Expected: Ranked list with account names, Sept amount, Oct amount, % change

Test Case 4: Anomaly Detection

{
  "question": "What's our cash position trend over the last 8 weeks?"
}

Expected: Time series data + insight if declining trend detected

Best Practices

Data Quality

Standardize formats - Ensure dates, currencies are consistent
Clean headers - Use clear, descriptive column names
Remove duplicates - Prevent skewed aggregations
Handle nulls - Define how to treat missing values

Query Accuracy

Test with known answers - Verify calculations manually first
Compare to existing reports - Cross-check automated results
User feedback loop - Let users flag incorrect answers
Confidence thresholds - Alert when confidence <0.8

Performance

Index large datasets - Use Knowledge Base for fast search
Cache common queries - Store frequently requested results
Limit row scanning - Add filters before aggregations
Paginate large results - Don't return 10K rows at once

Security & Compliance

Access control - Restrict who can query sensitive sheets
Audit logging - Track all queries and who made them
Data masking - Hide PII in certain queries
Rate limiting - Prevent system abuse

ROI Metrics

Metric	Before Automation	After Automation	Improvement
Query response time	2-4 hours	5-10 seconds	99% reduction
Analyst time on ad-hoc requests	20 hrs/week	2 hrs/week	90% reduction
Manager self-service rate	10%	85%	8.5x increase
Decision-making speed	Days	Real-time	Instant
Data errors (manual calc)	15%	<1%	95% reduction
Insights discovered	Low	High (auto-flagged)	Qualitative win

Deployment Checklist

Troubleshooting

Issue: Agent doesn't find the right data Solution: Improve Knowledge Base indexing, add more descriptive column names, use semantic search

Issue: Calculations are incorrect Solution: Add Function Block with explicit Python/JS logic, verify formulas manually

Issue: Slow query performance Solution: Index data properly, add filters earlier in workflow, cache common queries

Issue: Ambiguous questions confuse the agent Solution: Add few-shot examples to prompts, create question templates for users

Next Steps

Connect your spreadsheets - Start with 2-3 high-traffic sheets
Define common queries - List the top 20 questions your team asks
Test accuracy - Run queries and verify results manually
Train your team - Show them how to phrase questions
Monitor usage - Track which queries are most popular
Expand coverage - Add more spreadsheets as confidence grows

Need Help?

Blocks: See Agent Blocks, Function Blocks, Condition Blocks
Tools: See Google Sheets, Knowledge Base, File Tool
Templates: Browse more use case templates
Support: Contact our team for implementation assistance

Ready to transform your spreadsheets into intelligent assistants? Start building in Klyntos today.

What You'll Build​

Blocks & Tools You'll Use​

Core Blocks​

Supporting Tools​

Use Case Scenarios​

Scenario 1: Financial Query​

Scenario 2: Anomaly Detection​

Scenario 3: Growth Analysis​

Implementation​

Step 1: Set Up Data Ingestion​

Step 2: Create Spreadsheet Knowledge Base​

Step 3: Build Query Understanding Agent​

Step 4: Execute Data Retrieval & Calculation​

Step 5: Add Pattern Detection & Insights​

Step 6: Format & Present Results​

Step 7: Distribution & Alerts​

Step 8: Learning & Optimization​

Workflow Diagram​

Advanced Enhancements​

1. Multi-Sheet Analysis​

2. Time-Series Forecasting​

3. Interactive Dashboards​

4. Voice Queries​

5. Scheduled Reports​

6. Collaborative Queries​

Testing Your Workflow​

Test Case 1: Simple Aggregation​

Test Case 2: Filtered List​

Test Case 3: Growth Analysis​

Test Case 4: Anomaly Detection​

Best Practices​

Data Quality​

Query Accuracy​

Performance​

Security & Compliance​

ROI Metrics​

Deployment Checklist​

Troubleshooting​

Next Steps​

Need Help?​