BREAKING: AI can now automate entire workflows like McKinsey's QuantumBlack AI division (for free).
Here are 15 insane Claude prompts that replace $500K/year automation consultants (Save for later)
1. The QuantumBlack Workflow Discovery Audit
"You are a senior automation strategist at McKinsey's QuantumBlack who audits Fortune 500 operations to find every workflow that can be automated, saving companies $10M+ annually.
I need a complete workflow automation audit for my business.
Discover:
- Full process inventory: list every recurring workflow in my business with estimated time spent
- Automation readiness score for each workflow (1-10) based on complexity and repetitiveness
- Quick wins: workflows that can be automated this week with existing tools and zero coding
- High-impact targets: workflows consuming the most employee hours per month
- Decision logic mapping: the if-then rules behind each workflow written out step by step
- Data dependency map: what information flows between workflows and where bottlenecks exist
- Human judgment checkpoints: which steps actually require a human brain vs just feel like they do
- Tool recommendation for each automatable workflow (Zapier, Make, n8n, custom script)
- Risk assessment: what breaks if each automation fails and how to prevent it
- ROI calculation: hours saved × hourly cost = dollar value for each automation opportunity
Format as a QuantumBlack-style automation opportunity assessment with a prioritization matrix and ROI summary table.
My business: [DESCRIBE YOUR BUSINESS TYPE, TEAM SIZE, MAIN RECURRING TASKS, TOOLS YOU CURRENTLY USE, AND BIGGEST TIME WASTERS]"
2. The Accenture Process Mining Engineer
"You are a principal process mining consultant at Accenture who reverse-engineers messy business operations into clean, optimized workflows before automating them for global enterprises.
I need a complete process map of my current workflow before I automate anything.
Map:
- Step-by-step process flow with every action, decision point, and handoff documented
- Time analysis: how long each step takes on average and where delays happen
- Role assignment: who does what at each stage (person, team, or tool)
- Input and output for every step: what goes in, what comes out, what format
- Decision tree logic: every branching point mapped with the criteria for each path
- Exception handling: what happens when things go wrong at each step and how often it occurs
- Rework loops: where work gets sent back for corrections and what causes it
- Dependency chain: which steps must finish before others can start
- Waste identification: steps that add no value and could be eliminated entirely
- Optimized process design: the ideal workflow after removing waste and adding automation
Format as an Accenture-style process mining report with current-state flow, future-state flow, and a gap analysis table.
My workflow: [DESCRIBE YOUR WORKFLOW FROM START TO FINISH, WHO IS INVOLVED, TOOLS USED, AND WHERE THINGS TYPICALLY SLOW DOWN OR BREAK]"
3. The UiPath RPA Blueprint Builder
"You are a senior RPA solutions architect at UiPath who designs robotic process automation blueprints for companies automating thousands of repetitive tasks across finance, HR, and operations.
I need a complete RPA implementation plan for my repetitive business processes.
Blueprint:
- Bot inventory: every repetitive task that should be handled by a software robot
- Process definition document for each bot: trigger, steps, inputs, outputs, exceptions
- Bot type classification: attended (human assists) vs unattended (fully automatic) for each
- Data extraction rules: exactly what information each bot reads from screens, emails, or documents
- Decision logic: every business rule the bot must follow written as clear if-then statements
- Integration map: which systems each bot connects to (CRM, email, spreadsheets, databases)
- Error handling playbook: what each bot does when it encounters unexpected data or system failures
- Testing scenario matrix: 20+ test cases per bot covering normal, edge, and failure conditions
- Deployment sequence: which bots to launch first based on value and complexity
- Maintenance schedule: how often to review and update each bot as business rules change
Format as a UiPath-style RPA solution design document with bot specifications, process definition tables, and a deployment timeline.
My repetitive tasks: [DESCRIBE YOUR MOST TIME-CONSUMING REPETITIVE TASKS, SYSTEMS INVOLVED, DATA FORMATS, AND VOLUME OF TRANSACTIONS]"
4. The Zapier Integration Architect
"You are the head of solutions engineering at Zapier who designs no-code automation systems connecting 7,000+ apps for businesses ranging from startups to Fortune 500 companies.
I need a complete no-code automation system connecting all my business tools.
Design:
- Tool ecosystem map: every app I use and how data should flow between them
- Zap inventory: every automation I need with trigger app, action app, and data passed
- Multi-step workflow designs for my 5 most complex processes with branching logic
- Filter and formatting rules: how to transform data between incompatible tools
- Error notification system: how I find out when an automation fails and what to do
- Scheduling strategy: which automations run in real-time vs on a schedule
- Data validation rules: checks that prevent bad data from flowing through automations
- Lookup tables and databases: storing reference data that multiple automations share
- Scaling plan: how the system handles 10x volume without hitting plan limits or rate caps
- Monthly automation audit checklist: what to review to keep everything running smoothly
Format as a Zapier-style automation architecture document with Zap specifications, data flow diagrams, and a monitoring checklist.
My tools: [LIST ALL YOUR BUSINESS TOOLS, WHAT DATA LIVES IN EACH, AND WHICH MANUAL TASKS CONNECT THEM]"
5. The Palantir Decision Automation Engine
"You are a senior forward-deployed engineer at Palantir who builds decision automation systems that replace manual judgment calls with data-driven logic for government agencies and Fortune 100 companies.
I need a complete decision automation framework that removes human bottlenecks from routine business decisions.
Automate:
- Decision inventory: list every recurring decision made in my business with frequency
- Decision logic extraction: the exact criteria, thresholds, and rules behind each decision
- Decision tree construction: visual if-then-else logic for the top 10 automatable decisions
- Data requirements: what information each automated decision needs and where it comes from
- Confidence scoring: rate each automated decision's reliability (high, medium, needs human review)
- Escalation rules: which edge cases must be sent to a human and what triggers escalation
- Approval workflow design: multi-level sign-off chains for high-stakes automated decisions
- Audit trail specification: what to log for compliance and post-decision review
- Feedback loop: how to track decision outcomes and improve automation rules over time
- Implementation priority: which decisions to automate first based on frequency and impact
Format as a Palantir-style decision automation specification with decision trees, data requirement tables, and escalation flowcharts.
My decisions: [DESCRIBE YOUR MOST COMMON RECURRING DECISIONS, WHO MAKES THEM, WHAT DATA THEY USE, AND WHICH ONES SLOW YOUR BUSINESS DOWN]"
6. The Amazon Operations Automation Playbook
"You are a senior operations automation manager at Amazon who designed the automated fulfillment workflows that process millions of orders daily with near-zero error rates.
I need a complete operations automation playbook for my business processes.
Build:
- Operations workflow catalog: every operational process with current manual steps documented
- Automation layer design: which steps get fully automated, partially automated, or stay manual
- Trigger system: what event starts each automated workflow (time, action, threshold, or request)
- SLA definition: how fast each automated process must complete with acceptable error rates
- Queue management: how work items get prioritized when multiple tasks compete for resources
- Notification framework: who gets alerted about what, when, and through which channel
- Capacity planning: how the automation handles demand spikes without breaking
- Quality control checkpoints: automated verification steps that catch errors before they cascade
- Performance metrics: the 5 KPIs to track for each automated operation
- Continuous improvement cycle: monthly review process to find and fix automation inefficiencies
Format as an Amazon-style operations playbook with workflow specifications, SLA tables, and a performance monitoring dashboard design.
My operations: [DESCRIBE YOUR DAILY OPERATIONS, ORDER VOLUME, TEAM SIZE, CURRENT BOTTLENECKS, AND TOOLS USED]"
7. The Salesforce CRM Automation Architect
"You are a senior Salesforce solutions architect who builds CRM automation systems for enterprise sales teams, turning manual data entry and follow-ups into fully automated revenue machines.
I need a complete CRM automation strategy that eliminates manual sales busywork.
Automate:
- Lead scoring automation: rules that automatically rank leads by likelihood to buy
- Lead routing logic: assign leads to the right rep based on territory, size, and expertise
- Email sequence triggers: automated follow-up chains based on prospect behavior
- Pipeline stage automation: auto-move deals when specific criteria are met
- Task creation rules: automatically generate to-dos for reps at each deal stage
- Data enrichment automation: pull company and contact info from external sources automatically
- Report generation: scheduled reports and dashboards that build themselves
- Alert system: notify managers when deals stall, quotas are at risk, or big opportunities appear
- Duplicate detection and merge automation: keep the CRM clean without manual cleanup
- Renewal and upsell triggers: flag existing customers approaching renewal or expansion opportunities
Format as a Salesforce-style CRM automation blueprint with automation rule specifications, workflow triggers, and a field mapping document.
My sales process: [DESCRIBE YOUR CRM TOOL, SALES STAGES, TEAM SIZE, LEAD SOURCES, AND BIGGEST MANUAL TIME SINKS]"
8. The Deloitte Financial Process Automator
"You are a senior finance transformation consultant at Deloitte who automates accounting, invoicing, and financial reporting workflows for companies doing $50M-$500M in annual revenue.
I need a complete financial process automation plan that eliminates manual spreadsheet work.
Automate:
- Invoice processing pipeline: from receipt to approval to payment with zero manual data entry
- Expense categorization rules: auto-classify every transaction into the correct account
- Reconciliation automation: match bank transactions to records and flag discrepancies instantly
- Monthly close acceleration: automate journal entries, accruals, and reconciliation steps
- Accounts receivable automation: auto-send invoices, reminders, and escalation notices on schedule
- Budget vs actual reporting: automated variance reports generated weekly without manual pulling
- Cash flow forecasting: auto-update projections as new data arrives from bank and accounting systems
- Approval routing: send spend requests to the right approver based on amount and category
- Tax compliance automation: track and categorize deductible expenses in real-time
- Audit trail generation: auto-document every financial transaction for end-of-year review
Format as a Deloitte-style finance automation roadmap with process specifications, tool recommendations, and a phased implementation timeline.
My finance setup: [DESCRIBE YOUR ACCOUNTING SOFTWARE, MONTHLY TRANSACTION VOLUME, TEAM SIZE, AND MOST PAINFUL MANUAL FINANCIAL TASKS]"
9. The HubSpot Marketing Automation Engine
"You are the VP of Marketing Automation at HubSpot who designs the automated marketing systems that generate 100,000+ qualified leads per month without manual campaign management.
I need a complete marketing automation system that runs campaigns on autopilot.
Build:
- Lead capture automation: forms, chatbots, and pop-ups that trigger instant follow-up sequences
- Segmentation engine: auto-tag contacts based on behavior, demographics, and engagement level
- Nurture sequence library: 5 automated email chains for different buyer stages and personas
- Lead scoring model: points-based system that automatically qualifies leads for sales handoff
- Content delivery automation: send the right content at the right time based on user behavior
- Social media scheduling system: automated posting cadence with content recycling rules
- Webinar and event automation: registration, reminders, follow-ups, and replay sequences
- Re-engagement campaigns: auto-trigger win-back sequences for inactive contacts
- Attribution tracking: automatically tag every conversion with the channel and campaign that drove it
- Performance reporting: automated weekly marketing dashboards sent to stakeholders
Format as a HubSpot-style marketing automation playbook with workflow diagrams, email sequence outlines, and scoring model specifications.
My marketing setup: [DESCRIBE YOUR MARKETING TOOLS, LIST SIZE, CONTENT TYPES, SALES HANDOFF PROCESS, AND BIGGEST LEAD GENERATION CHALLENGE]"
10. The ServiceNow IT Workflow Automator
"You are a principal IT automation consultant at ServiceNow who builds self-healing IT operations systems for enterprises with 10,000+ employees, reducing ticket volume by 60% through intelligent automation.
I need a complete IT workflow automation strategy that eliminates repetitive IT tasks.
Automate:
- Ticket classification and routing: auto-categorize incoming requests and assign to the right team
- Password reset automation: self-service flow that handles 30% of all IT tickets without human touch
- Employee onboarding automation: account creation, access provisioning, and equipment requests triggered automatically
- Software request and approval workflow: catalog, request, approve, and deploy without emails
- Incident auto-remediation: detect common issues and run fix scripts before a human sees the ticket
- Knowledge base auto-suggestion: show relevant articles to users before they submit a ticket
- Escalation automation: auto-escalate unresolved tickets based on time and priority thresholds
- Asset lifecycle automation: track, assign, and retire IT equipment through automated workflows
- Change management automation: approval chains, risk scoring, and deployment scheduling
- SLA monitoring and alerting: track response and resolution times with auto-alerts when breached
Format as a ServiceNow-style IT automation design document with workflow specifications, automation rule tables, and an expected ticket reduction forecast.
My IT setup: [DESCRIBE YOUR IT TEAM SIZE, TICKET VOLUME, TOOLS USED, MOST COMMON TICKET TYPES, AND EMPLOYEE COUNT]"
11. The Notion AI Knowledge Automation System
"You are the head of knowledge operations at Notion who built the internal systems that automatically organize, update, and distribute company knowledge to 30M+ users without manual documentation overhead.
I need a complete knowledge management automation system that keeps my company wiki alive without manual effort.
Design:
- Auto-capture framework: turn Slack messages, meeting notes, and emails into wiki entries automatically
- Template automation: pre-built templates for every recurring document type that auto-populate fields
- Knowledge routing: automatically notify the right people when relevant documentation is created or updated
- Stale content detection: flag documents not updated in 90 days and assign review to the owner
- Search optimization: auto-tagging, cross-linking, and metadata enrichment for instant findability
- Onboarding automation: new hire knowledge paths that serve the right docs in the right sequence
- Meeting-to-action pipeline: extract action items from meeting transcripts and create tasks automatically
- Version control automation: track who changed what, when, and why with rollback capability
- Knowledge gap detection: identify questions asked in Slack that have no matching wiki documentation
- Monthly knowledge health report: auto-generated metrics on coverage, freshness, and usage
Format as a knowledge operations blueprint with automation workflows, template libraries, and a content governance framework.
My setup: [DESCRIBE YOUR TEAM SIZE, KNOWLEDGE TOOLS, BIGGEST DOCUMENTATION PAIN POINTS, AND HOW INFORMATION CURRENTLY GETS SHARED]"
12. The Workato Enterprise Integration Orchestrator
"You are a senior enterprise integration architect at Workato who builds complex multi-system automation orchestrations connecting 50+ enterprise tools into a unified automated workflow for Global 2000 companies.
I need a complete enterprise integration strategy that connects all my systems into automated end-to-end workflows.
Orchestrate:
- System inventory: every business tool and database with the data it holds and APIs available
- Integration map: which systems need to talk to each other and what data flows between them
- Master data strategy: which system is the single source of truth for each data type
- Event-driven triggers: what happens in System A that should automatically trigger action in System B
- Data transformation rules: how to map fields between systems with different formats and naming
- Conflict resolution logic: what happens when two systems have conflicting data for the same record
- Batch vs real-time decision: which integrations need instant sync and which can run on a schedule
- Error handling and retry strategy: what happens when an integration fails at 3 AM
- Security and authentication: how each system connection is secured and credentials are managed
- Monitoring dashboard: real-time visibility into every integration's health and data volume
Format as a Workato-style enterprise integration architecture with system maps, data flow specifications, and error handling procedures.
My systems: [LIST ALL YOUR BUSINESS TOOLS, WHAT DATA LIVES IN EACH, CURRENT MANUAL DATA TRANSFERS, AND INTEGRATION PAIN POINTS]"
13. The Tesla AI-Powered Workflow Intelligence
"You are a senior AI automation engineer at Tesla who builds intelligent automation systems that learn from data patterns and adapt workflows automatically without human reprogramming.
I need an AI-powered automation layer that makes my workflows smarter over time.
Build:
- Pattern recognition: identify recurring patterns in my data that signal automation opportunities
- Predictive triggers: launch workflows based on predicted events before they actually happen
- Smart routing: use historical data to route tasks to the person or system most likely to handle them fastest
- Anomaly-triggered workflows: auto-launch investigation processes when data looks unusual
- Natural language intake: let users trigger complex automations by describing what they need in plain English
- Auto-classification: train simple models to categorize incoming requests, documents, or messages
- Dynamic prioritization: automatically re-order task queues based on urgency, impact, and deadlines
- Feedback-driven optimization: track automation outcomes and suggest rule improvements monthly
- Capacity forecasting: predict when workflow volume will spike and pre-allocate resources
- Self-healing logic: detect when automations produce bad outputs and switch to fallback rules automatically
Format as an AI automation strategy document with intelligence layer architecture, model specifications, and a learning loop design.
My workflows: [DESCRIBE YOUR HIGHEST-VOLUME WORKFLOWS, DATA AVAILABLE FOR PATTERN DETECTION, AND WHERE HUMAN JUDGMENT CURRENTLY SLOWS THINGS DOWN]"
14. The McKinsey Change Management Automation Plan
"You are a senior change management partner at McKinsey who ensures new automation deployments actually get adopted by employees instead of abandoned, drawing on behavioral science and Fortune 500 rollout experience.
I need a complete change management plan for rolling out automation across my organization.
Plan:
- Stakeholder impact analysis: who is affected by each automation and how their job changes
- Communication plan: messaging for executives, managers, and frontline employees with different frames
- Resistance prediction: which teams will push back and specific strategies to address each concern
- Training curriculum: what each role needs to learn, how they learn it, and when
- Champion network: identify and empower automation advocates in every department
- Quick win showcase: which automations to launch first to build momentum and trust
- Feedback collection system: how employees report automation issues and suggest improvements
- Success metrics: how to prove automation is working using numbers leadership cares about
- Role evolution guide: how each affected role transforms rather than disappears
- 90-day adoption roadmap: week-by-week plan from announcement to full adoption
Format as a McKinsey-style change management playbook with stakeholder maps, communication templates, and an adoption tracking dashboard.
My organization: [DESCRIBE YOUR COMPANY SIZE, DEPARTMENTS AFFECTED, AUTOMATIONS BEING DEPLOYED, CURRENT EMPLOYEE SENTIMENT, AND BIGGEST ADOPTION RISK]"
15. The Deloitte Automation ROI Dashboard
"You are a senior analytics partner at Deloitte who builds automation ROI measurement frameworks proving the business case for continued investment in workflow automation to skeptical CFOs.
I need a complete ROI measurement system that tracks the real value of every automation I build.
Measure:
- Time savings calculation: hours saved per automation per week converted to dollar value
- Error reduction tracking: manual error rate before vs after automation with cost-of-error estimates
- Throughput improvement: volume processed before vs after with capacity headroom analysis
- Employee satisfaction impact: survey framework measuring how automation affects team morale
- Customer experience improvement: response time and resolution speed changes post-automation
- Cost avoidance projection: hiring I would have needed without automation at projected growth
- Cumulative ROI dashboard: running total of investment vs returns across all automations
- Break-even timeline: how long each automation takes to pay for itself
- Opportunity cost analysis: what employees now do with the time automation freed up
- Executive reporting template: monthly one-page automation impact summary for leadership
Format as a Deloitte-style ROI framework with calculation formulas, dashboard specifications, and executive reporting templates.
My automations: [DESCRIBE YOUR CURRENT AUTOMATIONS, TEAM SIZE, HOURLY LABOR COSTS, AND WHAT METRICS YOUR LEADERSHIP CARES ABOUT MOST]"
These 15 prompts replace an entire automation consulting division:
I reverse-engineered how top consultants at McKinsey, Goldman Sachs, & JP Morgan use it.
The difference is night and day.
Here are 12 insane Claude Opus 4.6 prompts they don't want you to know (Save for later)
1. Market Sizing (TAM/SAM/SOM) from Scratch
Most founders pay consultants $3K just for a market sizing slide.
Claude does it in 30 seconds with actual logic:
Prompt:
You are a senior market research analyst at McKinsey.
Calculate the TAM, SAM, and SOM for [YOUR PRODUCT/SERVICE] in [TARGET MARKET].
For each:
- Show your math (top-down AND bottom-up approach)
- Cite the assumptions you're making
- Flag where your estimates are weakest
- Compare to any known market reports if applicable
Format as an investor-ready slide with numbers, not paragraphs. If my market is smaller than I think, tell me now.
2. Customer Persona Builder (Based on Real Data, Not Guesswork)
Consultants charge $5K to interview 10 people and hand you a persona deck with stock photos.
This is better:
Prompt:
You are a consumer insights researcher at Goldman Sachs
Build 3 detailed customer personas for [YOUR PRODUCT] in [INDUSTRY]
For each persona:
- Demographics + psychographics (what do they read, follow, trust?)
- Buying trigger: What event makes them Google your solution?
- Decision process: Who else influences their purchase?
- Objections: What's their #1 reason to say no?
- Exact phrases they'd use to describe their problem (for ad copy)
- No generic "35-year-old marketing manager" personas
- Base everything on behavioral patterns, not demographics
- Each persona should suggest a different acquisition channel
BREAKING: AI can now build RAG pipelines like Google Brain's retrieval research team (for free).
Here are 12 insane Claude prompts that replace $380K/year ML engineers at top AI labs (Save for later)
1. The Google Brain RAG Architecture Designer
"You are a principal research engineer at Google Brain who architected the retrieval-augmented generation systems powering Google's most advanced AI search and knowledge products.
I need a complete RAG system architecture designed from scratch for my use case.
Design:
- End-to-end architecture overview with every component and how they connect
- Ingestion pipeline: how documents enter the system and get processed
- Retrieval layer: how the system finds relevant information when a user asks a question
- Generation layer: how retrieved context gets combined with the LLM to produce answers
- Technology stack recommendation for each component with reasoning
- Latency budget breakdown: maximum time allowed for each pipeline stage
- Scaling strategy: how this architecture handles 10x and 100x data growth
- Cost estimate: monthly infrastructure spend at 1K, 10K, and 100K queries per day
- Failure modes: what can go wrong at each stage and how to handle it gracefully
- Build vs buy decision for each component with vendor recommendations
Format as a Google Brain-style system design document with architecture descriptions, component specifications, and a build timeline.
My use case: [DESCRIBE YOUR DATA TYPE, QUERY PATTERNS, ACCURACY REQUIREMENTS, SCALE, AND BUDGET]"
2. The Pinecone Chunking Strategy Engineer
"You are the head of solutions architecture at Pinecone who has optimized chunking strategies for thousands of production RAG deployments across enterprise clients.
I need a complete document chunking strategy tailored to my specific content type.
Build:
- Chunking method selection: fixed-size, semantic, recursive, or document-structure-based with reasoning
- Optimal chunk size determination based on my content type and query patterns
- Overlap strategy: how many tokens to overlap between chunks and why
- Boundary rules: where to split and where to never split (sentences, paragraphs, sections)
- Metadata extraction plan: what to tag each chunk with for filtering and retrieval
- Parent-child chunking architecture: small chunks for retrieval, large chunks for context
- Special content handling: tables, code blocks, lists, images, and headers
- Chunk quality validation: how to test if your chunks actually produce good retrieval
- Pre-processing pipeline: cleaning, normalizing, and enriching text before chunking
- A/B testing framework: how to compare two chunking strategies with real queries
Format as a chunking strategy specification with decision trees, configuration examples, and a validation test suite.
My content: [DESCRIBE YOUR DOCUMENT TYPES, AVERAGE LENGTH, STRUCTURE, LANGUAGE, AND WHAT USERS TYPICALLY ASK ABOUT]"
BREAKING: AI can now build ML models like Goldman Sachs' AI trading desk (for free).
Here are 12 insane Claude prompts that replace $400K/year quant researchers (Save for later)
1/ Time Series Forecasting Model
You are a Quantitative Researcher at Goldman Sachs Global Markets. I need a complete time series forecasting model for [STOCK/ASSET].
Please provide:
- Data preprocessing: How to clean price data and handle missing values
- Feature engineering: Technical indicators (moving averages, RSI, MACD, Bollinger Bands)
- Model selection: Compare ARIMA, LSTM neural networks, and Prophet models
- Training approach: Train-test split ratios and cross-validation strategy
- Performance metrics: MAE, RMSE, directional accuracy for predictions
- Backtesting framework: How to test strategy on historical data
- Risk management: Stop-loss rules and position sizing based on confidence
- Implementation code: Python pseudocode with library recommendations
Format as quantitative research report with model specifications and expected accuracy.
Asset: [DESCRIBE STOCK/CRYPTO/COMMODITY, TIME PERIOD, DATA SOURCE]
2/ Mean Reversion Trading Strategy
You are a VP of Quantitative Trading at JP Morgan's Systematic Trading desk. I need a mean reversion algorithm for [MARKET/ASSET].
Please provide:
- Statistical foundation: Z-score calculation and standard deviation bands
- Entry signals: When price deviates X standard deviations from mean
- Exit signals: When price returns to mean or stop-loss triggers
- Pair selection: How to find correlated assets for pairs trading
- Cointegration testing: Statistical tests to validate pair relationships
- Position sizing: Kelly Criterion or fixed-fraction approach
- Risk parameters: Maximum drawdown limits and exposure caps
- Backtesting results: Expected Sharpe ratio and win rate over 3+ years
Format as algorithmic trading strategy document with entry/exit rules.
BREAKING: AI can now evaluate startups like Sequoia Capital partners (for free).
Here are 12 insane Grok prompts that replace $400K/year VC analysts (Save for later)
1/ Market Sizing & TAM Analysis
You are a Partner at Sequoia Capital. I need a complete market size analysis for [STARTUP/INDUSTRY].
Please provide:
- Total Addressable Market: Global market size with data sources
- Serviceable Available Market: Realistic portion startup can reach
- Serviceable Obtainable Market: What startup can capture in 3-5 years
- Market growth rate: CAGR for next 5 years with trend drivers
- Market segments: Break TAM into customer types or use cases
- Bottoms-up validation: Unit economics × potential customers calculation
- Comparable markets: Similar industries that scaled and their trajectory
- Red flags: Reasons market might be smaller than claimed
Format as investment memo market section with specific dollar figures.
You are a VC analyst at Andreessen Horowitz. I need a competitive analysis for [STARTUP] in [INDUSTRY].
Please provide:
- Direct competitors: Top 5 companies solving same problem
- Indirect competitors: 5 adjacent solutions customers use today
- Competitive positioning: Where startup fits on market map (price vs. features)
- Moat analysis: What makes each competitor defensible
- White space: Gaps no one is filling that startup could own
- Threat level: Rate each competitor as low/medium/high threat with reasoning
- Market share estimates: Current revenue or user distribution
- Strategic moves: Recent funding, acquisitions, or pivots by competitors
Format as competitive intelligence brief with comparison matrix.
Startup: [DESCRIBE PRODUCT, MAIN COMPETITORS, DIFFERENTIATION]
BREAKING: AI can now build financial models like Goldman Sachs analysts (for free).
Here are 12 Claude prompts that replace $150K/year investment banking work (Save for later)
1/ DCF Valuation Model
You are a Senior Analyst at Goldman Sachs. I need a complete DCF (Discounted Cash Flow) valuation model for [COMPANY NAME].
Please provide:
- Free cash flow projections: Next 5 years with growth assumptions
- WACC calculation: Cost of equity + cost of debt breakdown
- Terminal value: Both perpetuity growth and exit multiple methods
- Sensitivity analysis: How value changes with different assumptions
- Discount rate justification: Why we chose this WACC
- Key drivers: What makes cash flow go up or down
- Comparable companies: How our assumptions compare to peers
- Valuation range: Bull case, base case, bear case scenarios
Format as investment banking pitch book valuation page with clear formulas.
Company: [DESCRIBE COMPANY, INDUSTRY, FINANCIALS]
2/ Three-Statement Financial Model
You are a VP at Morgan Stanley. I need a complete three-statement model for [COMPANY NAME].
Please provide:
- Income statement: Revenue, costs, EBITDA, net income (5 years)
- Balance sheet: Assets, liabilities, equity (5 years)
- Cash flow statement: Operating, investing, financing activities (5 years)
- Link formulas: How statements connect (net income → cash flow → balance sheet)
- Working capital: How AR, inventory, and AP change
- Debt schedule: Principal payments and interest expense
- Key assumptions: Revenue growth, margins, capex as % of sales
- Error checks: Balance sheet balancing and circular references
Format as Excel-style model with formulas explained in plain English.
Company: [DESCRIBE BUSINESS, CURRENT FINANCIALS, GROWTH STAGE]