STRATEGIC BUSINESS PLAN

Market Entry Strategy

How a small, AI-skilled team can capture $117M market opportunity in private company intelligence

Executive Summary

The Opportunity: Mid-market firms (PE funds, family offices, corp dev teams) need professional-grade private company intelligence but can't afford PitchBook's $25K pricing. This creates a $117M underserved market.

Our Wedge: Real-time private company intelligence from automated news monitoring and web scraping. We can detect funding rounds, leadership changes, and company updates within 1 hour vs. competitors' 24-48 hour manual research delay.

The Ask: $500K-750K investment over 18 months, deployed in 3 milestone-based phases with clear go/no-go gates.

The Return: Path to $9.1M ARR in 3 years (650 customers @ $14K avg), 40%+ operating margins, payback in 18-24 months.

Why Now: AI window of opportunity (2025-2027). We have a 9-12 month technical head start before incumbents catch up. We must move fast.

The Market Opportunity

$117M

Total Addressable Market (TAM)

Underserved middle-market segment

NOT Addressable

Large PE/VC Firms

500 firms × $25K = $12.5M

❌ Locked into PitchBook

Startups/Small VCs

10K firms × $199/mo = $24M/yr

❌ Locked into Crunchbase, low margin

OUR Opportunity

Mid-Size PE Funds

2K firms × $10K/yr = $20M

✓ Can't afford PitchBook, outgrew Crunchbase

Family Offices

5K offices × $8K/yr = $40M

✓ Need pro tools, budget-conscious

Corp Dev Teams

3K companies × $15K/yr = $45M

✓ M&A target screening, real-time monitoring

Consulting/Research Firms

1K firms × $12K/yr = $12M

✓ Data for client projects

Realistic Target: 5% Market Share in 3 Years

$9.1M

ARR Target

650

Customers

$14K

Avg Contract

How Customers Make Money With Our Data

Understanding customer ROI drives everything: pricing, features, and go-to-market

PE/VC Funds

Highest Value Users

How They Monetize:

Invest $10M → Exit at $50M+ (5x return)

What They Pay For:

• Find Series A/B deals BEFORE competitors
• Exit timing signals (new CFO = sale prep)
• Due diligence shortcuts

ROI: 3,000x

One $50M exit pays for 3,000 years of subscriptions

Investment Banks

M&A Advisory

How They Monetize:

M&A fees: 1-5% of deal value ($5-25M per deal)

What They Pay For:

• Find acquisition targets for clients
• Identify potential acquirers
• Timing signals (new CEO = sale soon)

ROI: 1,000x

One M&A mandate pays for 1,000 years of data

Corp Dev Teams

Strategic Acquirers

How They Monetize:

Strategic acquisitions to grow revenue or eliminate competition

What They Pay For:

• Target screening in adjacent markets
• Competitive tracking (who's raising?)
• Technology/talent scouting

ROI: 5,000x

$15K data vs $100M acquisition budget = rounding error

The Key Insight: We're Underpriced

If our data helps a PE fund find ONE Series A deal that exits at $50M, we've delivered $50M of value for a $15K subscription. That's a 3,000x ROI. We're not selling data—we're selling deal flow, timing intelligence, and competitive advantage.

Our Wedge: Timing Advantage

Being 24-48 hours FASTER is worth 100x more than having 10x the data

What Customers Can't Get Anywhere Else

< 1 hour

News Detection Speed

vs. 24-48 hours for manual research

48K+

Private Companies

Currently tracked, expanding to 1M+

251K

Leadership Profiles

Auto-extracted from websites

Example: PE Fund Use Case

"Get alerts within 1 hour when a private company in your deal pipeline:"

→ Announces Series B funding round (competitive intelligence)
→ Appoints new CEO or CFO (leadership transition signal)
→ Announces major partnership or customer win (growth signal)
→ Mentioned in M&A rumors or acquisition talks (timing opportunity)

Value Proposition: Worth $10-15K/year to mid-market PE funds

Automated monitoring that PitchBook does manually with $150M analyst team

Wedge → Expansion Strategy

1. Hook

Real-time funding news alerts they can't get elsewhere

2. Upsell

Full company profiles with bios, deals, relationships

3. Expand

Scale to 1M+ companies via web scraping & data aggregation

Must-Have Features (What Customers Demand)

Built from customer interviews and competitive research

Real-Time Alerts

• Email/Slack/SMS notifications
• Custom watchlists (portfolios, competitors)
• Sub-1 hour latency from news event

Table stakes - everyone needs this

Predictive Signals

• New CFO hire = "Company preparing for sale"
• Funding velocity = "Next round timing"
• Leadership turnover patterns

🔥 DIFFERENTIATOR - competitors don't have this

Relationship Graph

• CEO → Previous companies
• Board member connections
• Investor network mapping

High value for BD/sales teams

Competitive Intel

• Track competitors' funding rounds
• Monitor exec hires in your space
• Partnership announcements

Critical for strategic planning

Contact Data

• CEO/CFO/CTO email addresses
• LinkedIn profiles
• Direct dial phone numbers (where available)

Required for outbound sales

API Access

• Integrate into internal CRM/tools
• Webhooks for real-time data push
• Bulk export capabilities

Enterprise customers require this

Recommended Tiered Pricing

Anchor pricing to customer ROI, not our costs

STARTER

$5K

per year

✓ Real-time alerts (50 companies)
✓ Basic company profiles
✓ Email alerts only
✗ No predictive signals
✗ No API access

Target: Small VCs, family offices

PROFESSIONAL

Why We Can Win (Competitive Moat)

Speed to Market

6-12 month lead: We can launch Q1 2026, they need 18-24 months to pivot from manual research to AI-powered automation. First-mover advantage in real-time private company intelligence.

Cost Structure Moat

Our marginal cost: $2/customer/month (LLM costs)

PitchBook's marginal cost: ~$500/customer/month (analyst labor)

→ We can undercut on price and still have better margins

Agility Advantage

5-person team pivots in weeks. PitchBook needs 12-month planning cycles. We can experiment, they can't.

Our Unfair Advantage

What PitchBook Would Need to Replicate:

• Mars data infrastructure (3 years dev = $750K sunk cost)
• Real-time news processing pipeline (Benzinga integration)
• 150-180 prompts/sec LLM extraction infrastructure
• Web scraping at scale (Apify + custom crawlers)
• Historical database (48K companies, 3 years of deals/bios)

12-18 mo

Their build time

$2M+

Their investment

We Can Launch Q1 2026 for $50K MVP

9-12 month head start before they react

Financial Model

$500K - $750K

Total Investment Required (18 months to profitability)

Data Infrastructure

$150K

Apify, data sources, compute

AI/LLM Costs

$200K

Extraction, models, APIs

Engineering

$250K

2 engineers × 12 months

Sales/Marketing

$150K

1 sales, marketing, events

Revenue Projections (Conservative)

YEAR 1 (2026)

$650K

ARR

Q1-Q2 Beta: $50K

10 customers @ $5K (Starter tier)

Q3-Q4 Launch: $600K

50 customers @ $12K avg (mix of tiers)

YEAR 2 (2027)

$2.6M

ARR

Customers: 200

Avg Contract: $13K

Gross Margin: 85%

Software margins (mostly Professional tier)

YEAR 3 (2028)

$9.1M

ARR

Customers: 650

Avg Contract: $14K

Operating Margin: 40%+

Scaled SaaS, some Enterprise customers

Payback Period

18-24 mo

Projected IRR

150%+

Phased Approach with Go/No-Go Gates

Milestone-based investment to reduce risk

PHASE 1: VALIDATION

3 months

$50K investment

Goal: Prove customers will pay

• Build MVP event alert system
• Test with 5-10 beta customers
• Validate pricing and value prop

GO/NO-GO:

If 3+ customers pay $5K+ → Phase 2

PHASE 2: PRODUCT

6 months

$250K investment

Goal: Build scalable platform

• Event alerts + basic company profiles
• 25-50 paying customers
• Refine product-market fit

GO/NO-GO:

If $150K ARR + 80% retention → Phase 3

PHASE 3: SCALE

9 months

$450K investment

Goal: Scale to $1M ARR

• Full platform + sales team
• 100-150 customers
• Expand to predictive models

GO/NO-GO:

If $1M ARR + unit economics work → Continue

Kill Criteria (When to Abandon)

Phase 1 Failure: Can't get 10 beta customers to try it (lack of demand)

Phase 2 Failure: Churn > 30% annually (product not sticky)

Unit Economics: CAC > LTV (can't scale profitably)

Competitive Response: Incumbent launches competitive product and undercuts us

Why This Moment in Time Matters

The AI Window: 2025-2027

A brief window of opportunity for small, AI-skilled teams

2023-2024

LLMs Mature

We built Tester infrastructure. Production-quality LLMs available.

2025 ← WE ARE HERE

Window Opens

AI good enough to replace analysts, but incumbents haven't integrated. Small teams move faster.

2026-2027

Window Closing

PitchBook/Crunchbase launch AI features. New startups enter. Being first matters.

2028+

Window Closed

Incumbents caught up. Market consolidates. Harder to differentiate.

We Must Move NOW to Capture This Window

Launch Q1 2026 to be first-to-market. Build brand as "the AI-powered" player. Lock in customers before incumbents react.

Early Customer Validation

Next Step: Customer Discovery

Before requesting Phase 1 budget, validate demand with potential customers

Action Plan (Next 2-4 Weeks)

1. Identify 10 potential customers (PE, family offices, corp dev)
2. Create mockup of event alert product
3. Schedule 30-min discovery calls
4. Ask: "Would you pay $5-10K/year for this?"
5. Record testimonials (video if possible)

Success Criteria

Strong Signal:

6+ out of 10 say "yes, we'd pay $8K+"

Mixed Signal:

3-5 interested, but want to see product first

Weak Signal:

< 3 interested (may need to pivot value prop)

Tip: Present validation results to Raul. "I talked to 8 potential customers. 6 said they'd pay $8K/year. Here's what they said..." → This is 100x more convincing than projections.

Risk Mitigation

Key Risks

Incumbents React Faster Than Expected

PitchBook launches AI features in 12 months vs. our 18 month estimate

Customer Acquisition Harder Than Expected

Target customers don't value real-time events enough to switch

Data Quality Issues

LLM extraction accuracy not good enough for production

Resource Constraints

Team bandwidth stretched between this and existing products

Mitigation Strategies

Speed is Our Defense

Launch Q1 2026. Lock in customers before incumbents react. Build switching costs.

Customer Validation First

Get 6+ paying beta customers before full investment. Proof > Theory.

Continuous Quality Monitoring

User feedback loop. Ground truth dataset. 90%+ accuracy threshold.

Phased Resource Allocation

Clear milestones with go/no-go gates. Can abandon if not working.

Downside Protection

If market entry fails, we can pivot to selling Events API to existing Kscope customers. The Tester infrastructure we build has value regardless. Not a total loss.

The Ask

What We Need from Leadership

Milestone-based commitment to reduce risk

IMMEDIATE (Q4 2025)

Phase 1 Green Light

• $50K budget (validation)
• 1 engineer 50% time (3 mo)
• Permission to approach customers

IF PHASE 1 SUCCEEDS (Q1 2026)

Phase 2 Funding

• $250K budget (product build)
• 2 engineers full-time (6 mo)
• 1 sales/BD contractor

IF PHASE 2 SUCCEEDS (Q3 2026)

Phase 3 Scale-Up

• $450K budget (scale)
• Formal product line
• Hire head of product

What You Get

✓ Milestone-based investment (reduce risk)
✓ Clear kill criteria (know when to stop)
✓ Potential $5M+ ARR business in 3 years
✓ Strategic optionality (could sell to incumbent)
✓ Leverage existing Tester (sunk cost → asset)

What's At Stake If We Don't

✗ Tester remains a cost center (no revenue)
✗ We miss the AI window (2025-2027)
✗ Competitors own event extraction market
✗ In 2028, we look back and regret not moving
✗ 3 years of Tester R&D underutilized

Summary: Why This Is the Right Move

$117M

Underserved market opportunity we can capture

$9.1M

Year 3 ARR target (650 customers @ $14K avg)

3,000x

Customer ROI (one deal pays for 3,000 years of data)

We have a unique window of opportunity (2025-2027) to enter the private company intelligence market. Our 3 years of Tester development gives us a technical moat that would cost incumbents $2M+ and 18+ months to replicate.

The phased approach with clear go/no-go gates reduces risk. We validate demand before committing full resources. If it doesn't work, we know when to stop.

The question is not "can we compete?" — it's "can we afford NOT to try?"