How to build a $10M ARR B2B Startup

The clearest path to $10M ARR with a B2B AI agent startup

At this point, we all know that AI agents is a mega opportunity, but the question is what AI agent startup idea should you build? Today's post is about giving you a framework for how to do that.

It involves finding industries where expensive human labor is still trapped in spreadsheet hell, document review nightmares, and endless email chains that specialized AI workflows could automate away overnight.

Look for industries still running on Excel and email. But I’m not talking about the obvious ones everyone’s targeting.

I’m talking about "boring" specialty insurance, equipment leasing, commodities trading, commercial real estate operations, logistics management, or medical claims processing. Places where:

• Middle managers are drowning in spreadsheets

• Six-figure mistakes happen monthly

• Documents need manual review by highly-paid professionals

• Data lives in silos that don’t talk to each other

• Each deal requires 20+ emails and 5+ meetings to close

These industries have been resistant to previous waves of software because their workflows are too specialized, their regulations too complex, or their stakeholders too traditional.

But they’re perfect for AI agents.

Why Traditional SaaS Failed Where AI Agents Will Succeed

Let me tell you a story about commercial real estate operations.

A friend of mine runs a CRE company managing 42 properties. His team spends 30+ hours weekly extracting data from leases, creating reports, and flagging issues. They evaluated a dozen software solutions over five years. None stuck.

Why? Because traditional SaaS forces humans to adapt to rigid workflows. The software required his team to fundamentally change how they worked, categorizing everything precisely, entering data in specific formats, following exact processes.

AI agents flip this model. They adapt to how humans already work.

His team can upload leases in any format. Email conversations can stay in email. Excel spreadsheets can remain Excel spreadsheets. The agents meet users where they are, rather than forcing them to change decades of established practices.

That’s the key insight most AI founders miss.

The building blocks approach is small, specialized Agents

Don’t build one massive agent trying to automate everything. That approach fails because:

1. It requires too much context to work reliably

2. It makes complex decisions without transparency

3. It’s harder to train and maintain

4. It’s difficult to sell to risk-averse industries

Instead, build small, specialized agents that each handle one discrete part of a workflow:

• An extraction agent that pulls specific data points from PDFs, emails, and documents

• A comparison agent that analyzes current data against historical patterns

• An anomaly detection agent that flags unusual terms, conditions, or numbers

• A drafting agent that creates initial responses, summaries, or reports

• A coordination agent that routes information between systems and people

Chain these together and you’ve automated entire workflows that used to take teams of people.

This approach has three massive advantages:

1. Easier to sell. You can start with one agent that solves an immediate pain point

2. More reliable. Each agent has a narrow focus, making performance more consistent

3. Better visibility. Customers can see exactly what’s happening at each step

Real example: commercial lease operations

Let’s break down what this actually looks like for a specific industry.

In commercial real estate operations, a typical lease review process involves:

1. Extracting key terms from 50+ page lease documents (dates, rates, escalations, renewal options)

2. Comparing terms to market standards and portfolio averages

3. Identifying discrepancies or unusual clauses

4. Creating lease abstracts and summary reports

5. Setting up critical date reminders

6. Communicating findings to stakeholders

Here’s how you’d implement the agent chain:

Agent 1. The Extractor - Takes lease PDFs as input - Identifies and extracts 50+ specific data points - Structures data in a consistent format - Confidence scores for each extraction

Agent 2. The Analyzer - Compares extracted data against portfolio benchmarks - Identifies terms outside standard deviations - Flags potential risks or opportunities - Validates against business rules

Agent 3. The Abstractor - Creates human-readable lease abstracts - Generates executive summaries - Prepares data visualizations - Formats for different stakeholders

Agent 4. The Scheduler - Sets up critical date notifications - Creates calendar entries - Establishes workflow triggers - Manages renewal processes

Agent 5. The Communicator - Drafts stakeholder emails - Creates updates for tenants - Prepares board-level reporting - Answers common questions

One client implementing this system reduced a 6-hour process to 15 minutes and eliminated $230,000 in annual errors.

The Distribution Playbook: How to Reach $10M ARR

The biggest mistake AI startups make is expecting the product to sell itself. It won’t. You need a systematic approach to distribution.

Phase 1: Establish Authority Through Content (0-$300K ARR)

Flood LinkedIn/X/YouTube with 60-second videos solving real industry problems:

• Show the exact pain point (preferably with screen recording)

• Demonstrate your solution (with actual results)

• End with a clear call to action

Content Strategy: - 3 platform-native posts daily - 2 video demonstrations weekly - 1 in-depth case study monthly - End every piece with a free lead magnet (templates, calculators, free AI agent)

Example Video: “Commercial property managers spend 12 hours weekly updating tenant spreadsheets. Here’s how our Extraction Agent pulls this data automatically in 45 seconds. [Shows demo] Want to try it yourself? Click the link for our free Lease Extraction Template that works with any AI.”

Key Metrics: - When a video hits 2%+ CTR, you’ve found a resonant message - Aim for 3-5% conversion on lead magnets - Target $50-100 CAC through organic content

Phase 2: Scale Winners with Paid Acquisition ($300K-$3M ARR)

Once you know what messages resonate, amplify them:

• Pour ad spend into your highest-performing organic content

• Target $3 per lead, aiming for 100 leads/day

• Build a lead magnet factory - new free lead magnet every 4 weeks

• Use retargeting to move leads through increasingly valuable offers

Example Lead Magnet Progression: 1. Free template → 2. Free basic agent → 3. Industry benchmark report → 4. ROI calculator → 5. Demo → 6. Pilot

Funnel Metrics: - Lead magnet → Email list (40-60% conversion) - Email list → Demo request (5-10% conversion) - Demo → Paid pilot (30-40% conversion) - Pilot → Customer (70-80% conversion)

Phase 3: Add High-Ticket Acquisition ($3M-$10M ARR)

At this stage, add a webinar funnel to your arsenal:

• Weekly industry-specific workshops

• Thought leadership positioning

• Behind-the-scenes demos of full agent chains

• Limited-time implementation offers

Webinar Economics: - $100-300 per registrant - 40-60% attendance rate - 10-15% conversion to sales call - 30% close rate - $30K-$50K average contract value

The Expansion Strategy - Horizontal or Vertical?

Once your agent network is crushing it in one vertical (let’s say commercial real estate), you’ll spot similar workflows in other industries. The same system that abstracts leases could handle insurance policies or equipment contracts with minor tweaks.

You have two primary expansion paths:

Vertical Expansion

• Deeper into your initial industry

• Add more specialized agents for adjacent workflows

• Integrate with industry-specific platforms

• Build industry-specific data models and benchmarks

Example: Moving from lease abstraction to entire lease lifecycle management, including negotiations, renewals, and facilities management.

Horizontal Expansion

• Apply your agent architecture to similar workflows in different industries

• Partner with industry experts to customize for new verticals

• Acquire small players with industry expertise but weaker technology

• License your agent framework to solutions providers

Example: Taking your document extraction, comparison, and anomaly detection agents from commercial real estate to specialty insurance underwriting.

The Exit Options

After 3-5 years of execution on this playbook, you’ll have options:

1. Keep scaling the business

   • $10M ARR is just the beginning for a capital-efficient business

   • 70%+ gross margins are common in this model

   • Perfect for venture capital at this stage due to proven economics

2. Strategic acquisition

   • Traditional software providers need AI capabilities

   • Industry-specific platforms need automation

   • 5-8x ARR exits are common for specialized B2B SaaS with strong metrics

3. Private equity rollup

   • PE firms are creating industry-specific automation platforms

   • Your specialized approach makes you a valuable component

   • Clean unit economics make financing straightforward

Why This Works When General AI Applications Struggle

The B2B vertical-specific AI agent approach succeeds because:

1. Clear ROI. You’re replacing expensive labor with automation

2. Defensible position. Deep industry knowledge creates barriers to entry.

3. Practical implementation. You’re enhancing existing workflows, not replacing them

4. Immediate value. Even a single agent delivers measurable benefits

5. Compounding advantage. Each new agent makes your system more valuable

6. Data moat. Every document processed improves your models

What It Takes to Execute

I'm not saying this is easy money, any startup is hard and this is no exception. To execute this playbook, you need:

• Industry expertise. Deep understanding of the vertical you’re targeting (or some way to learn quickly!)

• AI capabilities. Skill in designing, training, and connecting specialized agents

• Sales acumen. Ability to speak the language of your industry and sell value

• Content creation. Discipline to consistently produce high-quality content to build audience/community

• System thinking: Vision to see how components connect into workflows

But you don’t need to be an AI research scientist or have raised millions in venture capital.

Getting Started This Week

If you’re convinced this is the path, here’s your action plan for the next 7 days:

1. Days 1-2: Identify 3 industries still running on Excel and email. Research their specific workflows, pain points, and economic structure. Start building content to attract an audience/community. Create daily.

2. Days 3-4: For each industry, list the top 5 repetitive, document-centric tasks that highly-paid professionals hate doing. Estimate time spent and error costs.

3. Days 5-7: Design a single agent that could address one specific task. Outline:

   • Exact inputs it would accept

   • Precise outputs it would produce

   • Integration points with existing workflows

   • Value proposition in time and error reduction

The Bottom Line

Building agents that revolutionize how traditional industries work with information is the clearest path to building a $10M ARR AI business in 2025. My hope is that this got your creative juices flowing.

Build for one industry’s pain point, end up with a playbook for automating any business process that still runs on humans and Excel.

That’s how you could actually build a $10M B2B AI company.

Note: I write posts like this every week, packed with free startup ideas, insights on business building, and strategies for succeeding in the online world. It’s called Greg’s letter.

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