If you're searching for equity grant recommendation engine AI, you're trying to solve a real problem, not collect definitions. This guide walks through it step by step, the way we'd explain it to a founder sitting across the table.

Quick Answer

Equity grant recommendation engine AI comes down to your specific numbers, not a generic rule of thumb — the fastest way to get a real answer is to model it against your actual cap table instead of a spreadsheet estimate.

• Start from your real numbers, not an industry average
• Revisit this every time you issue new equity or close a round
• Use a live cap table so the math updates automatically

The Equity Grant Decision

The Equity Grant Decision. Here's what that covers: you're hiring: "how much equity should this person get?", founder anxiety: "too much = regret later, too little = they leave", and how it plays out in practice.

You're hiring: "How much equity should this person get?"

You're hiring: "How much equity should this person get?". This is the step most founders underestimate — worth getting right before it turns into a bigger cleanup job later.

Founder anxiety: "Too much = regret later, Too little = they leave"

Founder anxiety: "Too much = regret later, Too little = they leave". It sounds minor until it isn't, usually right when an investor or new hire is looking at the numbers.

No data: Each company, role, stage is different

No data: Each company, role, stage is different. Get this wrong early and it compounds quietly until your next round forces the issue.

Risk: Make bad decision, spend 4 years regretting it

Risk: Make bad decision, spend 4 years regretting it. This is exactly the kind of detail that's easy to skip and expensive to fix retroactively.

Lovie AI Grant Recommendations

Lovie AI Grant Recommendations. Here's what that covers: input: role, seniority, stage, salary, output: recommended equity % + range, and how it plays out in practice. This is where benchmark actually shows up on your cap table.

Input: Role, seniority, stage, salary

Input: Role, seniority, stage, salary. Get this wrong early and it compounds quietly until your next round forces the issue.

Output: Recommended equity % + range

Output: Recommended equity % + range. This is exactly the kind of detail that's easy to skip and expensive to fix retroactively.

Data: Benchmarks from Carta + anonymized Lovie data

Data: Benchmarks from Carta + anonymized Lovie data. Most spreadsheet-based cap tables miss this until someone asks a question they can't answer on the spot. Equity grant decisions are the most stressful founder moments.

Explanation: Here's why this is fair

Explanation: Here's why this is fair. This is the step most founders underestimate — worth getting right before it turns into a bigger cleanup job later.

Alternatives: Here's the range, here's why

Alternatives: Here's the range, here's why. It sounds minor until it isn't, usually right when an investor or new hire is looking at the numbers.

How the Recommendation Engine Works

How the Recommendation Engine Works. Here's what that covers: step 1: analyze your cap table structure, step 2: compare to similar companies, and how it plays out in practice. This is where cap table actually shows up in practice.

Step 1: Analyze your cap table structure

Step 1: Analyze your cap table structure. Most spreadsheet-based cap tables miss this until someone asks a question they can't answer on the spot.

Step 2: Compare to similar companies

Step 2: Compare to similar companies. — specifically, stage, industry.

Step 3: Factor in role/seniority

Step 3: Factor in role/seniority. — specifically, CTO vs junior engineer. Lovie removes guesswork: 'AI recommends 2-3% based on benchmarks, here's why.' Founders feel confident + fair.

Step 4: Consider market rates

Step 4: Consider market rates. — specifically, salary competitiveness.

Step 5: Calculate recommendation + range

Step 5: Calculate recommendation + range. This is exactly the kind of detail that's easy to skip and expensive to fix retroactively.

Step 6: Explain reasoning

Step 6: Explain reasoning. — specifically, transparency.

Real Example: First Engineering Hire

Real Example: First Engineering Hire. Here's what that covers: company: series a, 10 employees, $5m seed, role: cto, and how it plays out in practice.

Company: Series A, 10 employees, $5M seed

Company: Series A, 10 employees, $5M seed. It sounds minor until it isn't, usually right when an investor or new hire is looking at the numbers.

Role: CTO

Role: CTO. — specifically, founding-level engineer.

Salary: $150K

Salary: $150K. This is exactly the kind of detail that's easy to skip and expensive to fix retroactively.

AI recommendation: 2-3% fully diluted equity

AI recommendation: 2-3% fully diluted equity. Most spreadsheet-based cap tables miss this until someone asks a question they can't answer on the spot.

Reasoning: Benchmarks show 1.5-3% for CTO at Series A

Reasoning: Benchmarks show 1.5-3% for CTO at Series A. This is the step most founders underestimate — worth getting right before it turns into a bigger cleanup job later.

Range: 1.5% = low offer, 3% = competitive, 4% = generous

Range: 1.5% = low offer, 3% = competitive, 4% = generous. It sounds minor until it isn't, usually right when an investor or new hire is looking at the numbers.

AI Accounts for Special Circumstances

AI Accounts for Special Circumstances. Here's what that covers: salary trade-off: lower salary = higher equity, market rate: tech talent costs more, and how it plays out in practice.

Salary trade-off: Lower salary = higher equity

Salary trade-off: Lower salary = higher equity. This is exactly the kind of detail that's easy to skip and expensive to fix retroactively.

Market rate: Tech talent costs more

Market rate: Tech talent costs more. Most spreadsheet-based cap tables miss this until someone asks a question they can't answer on the spot.

Timing: Earlier hire = higher equity

Timing: Earlier hire = higher equity. This is the step most founders underestimate — worth getting right before it turns into a bigger cleanup job later.

Seniority: More experience = higher percentage

Seniority: More experience = higher percentage. It sounds minor until it isn't, usually right when an investor or new hire is looking at the numbers.

Geography: Bay Area vs remote vs other markets

Geography: Bay Area vs remote vs other markets. Get this wrong early and it compounds quietly until your next round forces the issue. Position as 'Make equity grant decisions with confidence.'.

Avoiding Grant Mistakes

Avoiding Grant Mistakes. Here's what that covers: mistake 1: grant too little, lose them to competitor, mistake 2: grant too much, regret at series a, and how it plays out in practice.

Mistake 1: Grant too little, lose them to competitor

Mistake 1: Grant too little, lose them to competitor. This is the step most founders underestimate — worth getting right before it turns into a bigger cleanup job later.

Mistake 2: Grant too much, regret at Series A

Mistake 2: Grant too much, regret at Series A. It sounds minor until it isn't, usually right when an investor or new hire is looking at the numbers.

Mistake 3: Inconsistent

Mistake 3: Inconsistent. — specifically, unfair across team.

Mistake 4: Ignore salary/equity trade-off

Mistake 4: Ignore salary/equity trade-off. This is exactly the kind of detail that's easy to skip and expensive to fix retroactively.

AI helps: Avoid all 4 with data-driven recommendations

AI helps: Avoid all 4 with data-driven recommendations. Most spreadsheet-based cap tables miss this until someone asks a question they can't answer on the spot.

Grant Approval Workflow

Grant Approval Workflow. Here's what that covers: you enter hire details, ai recommends range, and how it plays out in practice.

You enter hire details

You enter hire details. Get this wrong early and it compounds quietly until your next round forces the issue.

AI recommends range

AI recommends range. This is exactly the kind of detail that's easy to skip and expensive to fix retroactively.

You decide within range

You decide within range. — specifically, or override with reasoning.

System records decision

System records decision. — specifically, audit trail.

Board approves

Board approves. — specifically, standard process.

Vesting starts

Vesting starts. Get this wrong early and it compounds quietly until your next round forces the issue.

Retention Angle

Retention Angle. Here's what that covers: insufficient equity = flight risk, competitive equity = retention signal, and how it plays out in practice.

Insufficient equity = flight risk

Insufficient equity = flight risk. Most spreadsheet-based cap tables miss this until someone asks a question they can't answer on the spot.

Competitive equity = retention signal

Competitive equity = retention signal. This is the step most founders underestimate — worth getting right before it turns into a bigger cleanup job later.

Generous equity = team morale + long-term thinking

Generous equity = team morale + long-term thinking. It sounds minor until it isn't, usually right when an investor or new hire is looking at the numbers.

AI balances all 3

AI balances all 3. Get this wrong early and it compounds quietly until your next round forces the issue.

Equity Refresh Recommendations

Equity Refresh Recommendations. Here's what that covers: year 1: employee hits 1-year mark, ai suggests: "consider refreshment grant (0.5%)", and how it plays out in practice.

Year 1: Employee hits 1-year mark

Year 1: Employee hits 1-year mark. It sounds minor until it isn't, usually right when an investor or new hire is looking at the numbers.

AI suggests: "Consider refreshment grant (0.5%)"

AI suggests: "Consider refreshment grant (0.5%)". Get this wrong early and it compounds quietly until your next round forces the issue.

Why: Retains best performers, recognizes growth

Why: Retains best performers, recognizes growth. This is exactly the kind of detail that's easy to skip and expensive to fix retroactively.

Alternative: Increase salary instead

Alternative: Increase salary instead. Most spreadsheet-based cap tables miss this until someone asks a question they can't answer on the spot.

Competitor Gap

Competitor Gap. Here's what that covers: carta: publishes benchmarks, no recommendations, pulley: no grant recommendation engine, and how it plays out in practice.

Carta: Publishes benchmarks, no recommendations

Carta: Publishes benchmarks, no recommendations. This is exactly the kind of detail that's easy to skip and expensive to fix retroactively.

Pulley: No grant recommendation engine

Pulley: No grant recommendation engine. Most spreadsheet-based cap tables miss this until someone asks a question they can't answer on the spot.

Lovie: AI recommends + explains + fairness scoring + audit trail

Lovie: AI recommends + explains + fairness scoring + audit trail. This is the step most founders underestimate — worth getting right before it turns into a bigger cleanup job later.

None of this has to live in a spreadsheet you're afraid to open. For more on equity grant recommendation engine AI, Lovie Cap Table is built to handle it alongside formation, funding, and equity tracking — not as three separate tools.