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πŸ“ŠAI Extractβ€’In Google Sheets

Extract Structured Data with AI

Transform messy, unstructured text into clean, structured data. Parse addresses, extract entities, and organize information β€” all with natural language prompts.

data-extraction.xlsx
ABC
1Raw TextFormulaExtracted
2Contact John at john@acme.comβ€”
3Total: $1,249.99 USD=AI_EXTRACT(A3, "price")1249.99
4123 Main St, New York, NY 10001=AI_EXTRACT(A4, "city, state")New York, NY
Why AI Extract?

Turn chaos into clean data

AI understands context and extracts exactly what you need from any text, no matter how messy.

🎯

Natural Language Extraction

Describe what you want to extract in plain English. AI understands context and finds it.

✨

Entity Recognition

Automatically identify and extract names, companies, emails, phones, dates, and more.

πŸ“Š

Address Parsing

Parse messy addresses into structured components: street, city, state, zip, country.

🧠

Data Classification

Categorize and tag data automatically based on content, sentiment, or custom criteria.

⚑

Multi-Field Extraction

Extract multiple pieces of information at once and return them as structured columns.

πŸ”§

Cleaning & Normalization

Standardize formats, fix typos, and clean up messy data automatically.

Examples

AI extraction in action

See how AI transforms messy text into clean, structured data.

Entity Extractionβ€’Extract Company Information
Formula
=AI_EXTRACT(A1, "Extract name, email, phone, company")
Input Text
Hi, this is John Smith from Acme Corp. You can reach me at john.smith@acmecorp.com or call me at (555) 123-4567. Looking forward to discussing partnership opportunities!
Extracted Data
AI
2D Array2 rows Γ— 4 cols
[0]NameEmailPhoneCompany
[1]John Smithjohn.smith@acmecorp.com(555) 123-4567Acme Corp
Use case: extract entities from text google sheets
Data Classificationβ€’Extract Contact Details
Formula
=AI_EXTRACT(A1, "Extract job title, company, location, industry")
Input Text
Sarah Johnson, VP of Marketing at TechStart Inc, based in Austin, Texas. We're a B2B SaaS company in the project management space.
Extracted Data
AI
2D Array2 rows Γ— 4 cols
[0]Job TitleCompanyLocationIndustry
[1]VP of MarketingTechStart IncAustin, TexasB2B SaaS
Use case: extract contact info google sheets
Entity Extractionβ€’Classify Products
Formula
=AI_EXTRACT(A1, "Extract product name, price, color, size")
Input Text
Premium Wireless Headphones - Black, Over-Ear, Noise Cancelling. Price: $299.99. Available in Black, Silver, and Rose Gold. Weight: 250g.
Extracted Data
AI
2D Array2 rows Γ— 4 cols
[0]ProductPriceColorSize
[1]Premium Wireless Headphones$299.99Black, Silver, Rose GoldOver-Ear
Use case: categorize data google sheets
Entity Extractionβ€’Parse Invoice Details
Formula
=AI_EXTRACT(A1, "Extract invoice number, date, total amount, vendor name")
Input Text
INVOICE #INV-2024-0156 | Date: January 15, 2024 | Bill To: Acme Corp | Total: $1,245.00 | Vendor: Office Supplies Plus
Extracted Data
AI
2D Array2 rows Γ— 4 cols
[0]Invoice #DateTotalVendor
[1]INV-2024-0156January 15, 2024$1,245.00Office Supplies Plus
Use case: parse invoices google sheets
Entity Extractionβ€’Extract Event Information
Formula
=AI_EXTRACT(A1, "Extract event name, date, time, location, cost")
Input Text
Join us for the Annual Tech Summit 2025 on March 15-17, 2025 at the Austin Convention Center. Sessions run from 9 AM to 5 PM daily. Early bird tickets: $599.
Extracted Data
AI
2D Array2 rows Γ— 5 cols
[0]EventDateTimeLocationCost
[1]Annual Tech Summit 2025March 15-17, 20259 AM - 5 PMAustin Convention Center$599
Use case: extract event data google sheets
Entity Extractionβ€’Extract Resume Information
Formula
=AI_EXTRACT(A1, "Extract name, years of experience, education, skills")
Input Text
Michael Chen - 8 years of experience in full-stack development. BS in Computer Science from Stanford. Proficient in React, Node.js, Python, AWS.
Extracted Data
AI
2D Array2 rows Γ— 4 cols
[0]NameExperienceEducationSkills
[1]Michael Chen8 yearsBS Computer Science, StanfordReact, Node.js, Python, AWS
Use case: parse resume data google sheets
Address Parsingβ€’Parse Messy Addresses
Formula
=AI_EXTRACT(A1, "Extract street, city, state, zip code")
Input Text
Please ship to: 123 Main Street, Suite 400, San Francisco, CA 94102
Extracted Data
AI
2D Array2 rows Γ— 4 cols
[0]StreetCityStateZip
[1]123 Main Street, Suite 400San FranciscoCA94102
Use case: parse addresses google sheets
Entity Extractionβ€’Clean and Normalize Data
Formula
=AI_EXTRACT(A1, "Extract revenue, expenses, profit margin, growth rate")
Input Text
Q4 Results: Revenue $2.4M (+15% YoY), Operating Expenses $1.8M, Net Profit Margin 25%, Customer Growth 32%
Extracted Data
AI
2D Array2 rows Γ— 4 cols
[0]RevenueExpensesProfit MarginGrowth
[1]$2.4M$1.8M25%32%
Use case: clean data google sheets
Real-World Use Cases

From messy text to structured data

See how teams use AI extraction to automate data processing workflows.

Customer Review Analysis

Extract insights, sentiment, and key themes from reviews

Formula=AI_EXTRACT(A2, "Extract customer name, sentiment (positive/negative/neutral), main issue, urgency (high/medium/low), product mentioned")
INPUTRaw text
Customer Feedback
Hey team, this is Sarah from Account #1234. Really frustrated with the mobile app crashing constantly. This is costing us productivity! Please fix ASAP - high priority.
Love the new dashboard! John here from TechCorp. The analytics feature is exactly what we needed. Great work on the update.
Quick question - does the premium plan include API access? - Emily Watson
URGENT: We can't access our data since yesterday. This is a critical issue for our business. Contact: Mike Johnson, mike@company.com
EXTRACTEDStructured data
πŸ“Š CUSTOMER FEEDBACK ANALYSIS TICKET #1: πŸ‘€ Customer: Sarah (Account #1234) 😀 Sentiment: Negative πŸ”§ Issue: Mobile app crashing ⚠️ Urgency: High πŸ“¦ Product: Mobile App β†’ ACTION: Escalate to dev team - critical bug TICKET #2: πŸ‘€ Customer: John (TechCorp) 😊 Sentiment: Positive πŸ’¬ Feedback: Analytics feature appreciated ⚠️ Urgency: Low πŸ“¦ Product: Dashboard β†’ ACTION: Share with product team, request testimonial TICKET #3: πŸ‘€ Customer: Emily Watson 😐 Sentiment: Neutral ❓ Question: API access in premium plan ⚠️ Urgency: Medium πŸ“¦ Product: Premium Plan β†’ ACTION: Sales follow-up, send pricing info TICKET #4: πŸ‘€ Customer: Mike Johnson (mike@company.com) 😀 Sentiment: Negative πŸ”₯ Issue: Data access blocked ⚠️ Urgency: High πŸ“¦ Product: Core Platform β†’ ACTION: URGENT - technical support immediately πŸ“ˆ SUMMARY: β€’ Total Feedback: 4 items β€’ Urgent Issues: 2 (50%) β€’ Sentiment: 50% Negative, 25% Positive, 25% Neutral β€’ Product Areas: Mobile App, Dashboard, Premium Plan, Core Platform 🎯 IMMEDIATE ACTIONS: 1. Address data access issue (Mike Johnson) - CRITICAL 2. Investigate mobile app crashes (Sarah) - HIGH 3. Follow up on API question (Emily) - MEDIUM 4. Thank positive feedback (John) - LOW πŸ’‘ INSIGHTS: β€’ Mobile app stability issues recurring β€’ Dashboard update well-received β€’ Premium plan features need clarification

Lead Enrichment Pipeline

Extract and structure contact information from messy lead data

Formula=AI_EXTRACT(B2, "Extract full name, job title, company, company size, industry, email, phone, LinkedIn URL, pain points")
INPUTRaw text
Lead SourceRaw Lead Data
LinkedInJennifer Martinez, Director of Sales at CloudTech Solutions (200 employees). SaaS company struggling with lead generation. jennifer.m@cloudtech.io | linkedin.com/in/jennifermartinez
ConferenceMet David Park - CMO @ StartupXYZ, early stage fintech (30 people). Needs marketing automation. Call: 555-0123
WebinarAmanda Foster signed up. VP Product, MedTech Innovations, healthcare tech, ~500 employees. Looking for better analytics. amanda.foster@medtech.com
EXTRACTEDStructured data
🎯 ENRICHED LEAD DATABASE LEAD #1: πŸ‘€ Name: Jennifer Martinez πŸ’Ό Title: Director of Sales 🏒 Company: CloudTech Solutions πŸ‘₯ Size: 200 employees 🏭 Industry: SaaS πŸ“§ Email: jennifer.m@cloudtech.io πŸ“± Phone: - πŸ”— LinkedIn: linkedin.com/in/jennifermartinez πŸ’¬ Pain Points: Lead generation struggles πŸ“Š Lead Score: 85/100 (Mid-market SaaS, high intent) 🎯 Next Action: Schedule demo, share case study LEAD #2: πŸ‘€ Name: David Park πŸ’Ό Title: CMO 🏒 Company: StartupXYZ πŸ‘₯ Size: 30 employees 🏭 Industry: Fintech πŸ“§ Email: - πŸ“± Phone: 555-0123 πŸ”— LinkedIn: - πŸ’¬ Pain Points: Marketing automation needs πŸ“Š Lead Score: 72/100 (Early stage, budget conscious) 🎯 Next Action: Call to qualify, send startup pricing LEAD #3: πŸ‘€ Name: Amanda Foster πŸ’Ό Title: VP Product 🏒 Company: MedTech Innovations πŸ‘₯ Size: ~500 employees 🏭 Industry: Healthcare Tech πŸ“§ Email: amanda.foster@medtech.com πŸ“± Phone: - πŸ”— LinkedIn: - πŸ’¬ Pain Points: Analytics capabilities πŸ“Š Lead Score: 91/100 (Enterprise healthcare, specific need) 🎯 Next Action: Custom analytics demo, compliance info πŸ“Š PIPELINE SUMMARY: β€’ Total Leads: 3 β€’ Average Lead Score: 82.7/100 β€’ Industries: SaaS (1), Fintech (1), Healthcare (1) β€’ Company Sizes: Enterprise (1), Mid-market (1), Startup (1) 🎯 RECOMMENDED APPROACH: 1. Priority: Amanda Foster (highest score, enterprise) 2. Follow-up: Jennifer Martinez (strong fit, active pain point) 3. Qualify: David Park (early stage, confirm budget) πŸ“ˆ AUTOMATION OPPORTUNITIES: β€’ Add to CRM (all 3) β€’ Trigger nurture sequence based on company size β€’ Assign to appropriate sales rep (enterprise vs SMB) β€’ Schedule follow-up tasks

Address Standardization

Parse and normalize addresses from various formats

Formula=AI_EXTRACT(A2, "Extract parties involved, effective date, termination date, payment terms, key obligations")
INPUTRaw text
Document TypeDocument Text
Service AgreementThis Agreement between Acme Corp (Client) and Tech Services LLC (Provider) effective January 1, 2025 through December 31, 2025. Payment: $5,000/month, due within 30 days. Provider shall deliver monthly reports and maintain 99.9% uptime.
Lease AgreementLease between Property Owner: Smith Realty and Tenant: Jane's Coffee Shop. Term: March 1, 2025 - February 28, 2028. Rent: $3,500/month due on 1st of month. Tenant responsible for utilities and maintenance.
EXTRACTEDStructured data
πŸ“„ DOCUMENT EXTRACTION REPORT DOCUMENT #1: Service Agreement ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ πŸ‘₯ Parties: β€’ Client: Acme Corp β€’ Provider: Tech Services LLC πŸ“… Dates: β€’ Effective: January 1, 2025 β€’ Termination: December 31, 2025 β€’ Duration: 12 months πŸ’° Payment Terms: β€’ Amount: $5,000/month β€’ Due: Within 30 days β€’ Annual Value: $60,000 πŸ“‹ Key Obligations: βœ“ Provider: Monthly reports βœ“ Provider: 99.9% uptime SLA βœ“ Client: Timely payment ⚠️ ALERTS: β€’ Renewal: December 1, 2025 (60 days before) β€’ SLA Monitoring: Set up uptime tracking β€’ Payment Tracking: Auto-invoice 30 days before due ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ DOCUMENT #2: Lease Agreement ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ πŸ‘₯ Parties: β€’ Owner: Smith Realty β€’ Tenant: Jane's Coffee Shop πŸ“… Dates: β€’ Start: March 1, 2025 β€’ End: February 28, 2028 β€’ Duration: 36 months (3 years) πŸ’° Payment Terms: β€’ Rent: $3,500/month β€’ Due: 1st of each month β€’ Total Value: $126,000 (3 years) πŸ“‹ Key Obligations: βœ“ Tenant: Pay rent on 1st βœ“ Tenant: Utilities βœ“ Tenant: Maintenance βœ“ Owner: Property ownership ⚠️ ALERTS: β€’ Renewal Discussion: December 2027 (3 months before) β€’ Rent Review: March 1, 2026 (annual check) β€’ Maintenance Budget: Track tenant expenses ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ πŸ“Š PORTFOLIO SUMMARY: β€’ Active Contracts: 2 β€’ Total Annual Value: $102,000 β€’ Expiring in 2025: 1 contract β€’ Expiring in 2026+: 1 contract πŸ”” UPCOMING ACTIONS: 1. Q4 2025: Service Agreement renewal (Acme Corp) 2. Q1 2026: First rent review (Jane's Coffee Shop) 3. Monitor SLA compliance (Tech Services) 4. Track tenant maintenance costs πŸ’Ό CONTRACT MANAGEMENT: β€’ Set calendar reminders for all dates β€’ Create payment tracking dashboard β€’ Monitor obligation compliance β€’ Plan renewal negotiations

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