Platform Usage Examples

Real examples from the codebase showing how valuations are calculated, listings are matched, and the platform is used. No fabricated testimonials—just actual implementation patterns.

Sample Valuations from Test Data

These are actual test cases included in the codebase (app/admin/import-listings/page.tsx). They demonstrate how the valuation engine processes different business types.

Example Coffee Shop

Food Service • Austin, TX

Asking Price
$50,000
Annual Revenue
$250,000
Normalized SDE
$180,000
Year Established
2018
Business Age
6 years

Valuation Analysis

Industry Multiple (Coffee Shop):1.5x - 2.8x SDE
Base Valuation Range:$270K - $504K
Asking vs. Low:-81% (underpriced)

Analysis: Asking price significantly below calculated range. Potential asset sale or distressed situation. SDE appears high relative to revenue (72% margin unusual for coffee shop).

Example Restaurant

Full-Service Dining • Dallas, TX

Asking Price
$80,000
Annual Revenue
$500,000
Normalized SDE
$400,000
Year Established
2015
Business Age
9 years

Valuation Analysis

Industry Multiple (Restaurant):1.8x - 3.2x SDE
Base Valuation Range:$720K - $1.28M
Asking vs. Low:-89% (severely underpriced)

Analysis: Extremely high SDE margin (80% of revenue) is unrealistic for full-service dining. Likely data error or asset-only sale. Normal restaurant SDE margins: 10-20%.

Note: These are test cases with intentionally unrealistic financials to validate edge case handling. Real-world valuations would flag these margin inconsistencies in the confidence scoring.

Buyer-Listing Matching Algorithm

Our matching engine runs nightly (/api/match/run) to score buyer preferences against listings. Matches require ≥40 points to be stored.

Scoring Breakdown (100-point scale)

Industry Match
Listing industry in buyer's preferred industries
+40
State Match
Listing state in buyer's target states
+15
Revenue Meets Minimum
Annual revenue ≥ buyer's minimum requirement
+15
EBITDA/SDE Meets Minimum
Cash flow ≥ buyer's minimum earnings requirement
+10
Owner Hours Acceptable
Weekly hours ≤ buyer's maximum acceptable commitment
+10
Price Within Budget
Asking price ≤ buyer's maximum budget
+10
Keyword Matches
Buyer's search keywords found in listing description
+5 each (max +20)
Freshness Bonus
Listing created within last 14 days
+10

Example: Strong Match (Score: 90)

Industry match (HVAC in preferred list)+40
State match (Texas)+15
Revenue exceeds minimum ($500K> $250K)+15
Price within budget ($300K< $500K max)+10
Freshness (listed 5 days ago)+10
Total Score90

Example: Weak Match (Score: 25 - Not Stored)

Industry mismatch (0 points)+0
State match (California)+15
Price within budget+10
Revenue below minimum (0 points)+0
Stale listing (45 days old, 0 points)+0
Total Score25 (rejected)

Threshold: Only matches scoring ≥40 are stored in the database and shown to buyers. This prevents noise from low-quality matches.

Actual Usage Limits & Plan Tiers

Real quota enforcement from /lib/utils/plan-limitations.ts. These are not marketing numbers—they're enforced at API level with violation logging.

FeatureFreeBetaStarterProfessionalEnterprise
Daily Analyses2201050Unlimited
Monthly Analyses1010050500Unlimited
Questions per Hour21052050
Follow-ups (each type)251050Unlimited
PDF Export✗✓✓✓✓
Advanced Metrics✗✓✗✓✓
Conversation History✗✓✓✓✓

Usage Tracking Implementation

  • •Database-backed: Uses RPC function get_user_current_usage for quota checks
  • •Fallback mode: In-memory tracking if database RPC unavailable
  • •Violation logging: All limit-exceeded events logged to usage_violations table with IP + user agent
  • •Cost tracking: ~$0.06 per follow-up question calculated and stored daily

What's Not Included Here

To be transparent about what this page does NOT contain:

  • •No customer testimonials: We haven't fabricated reviews or success stories. User feedback is collected but not yet published.
  • •No transaction closure data: We don't track whether valuations led to actual deals. Platform focuses on valuation accuracy, not deal facilitation.
  • •No ROI claims: We can't demonstrate that our valuations increase sale prices or reduce time-to-close. That would require longitudinal tracking we haven't implemented.
  • •No third-party validation: While methodology aligns with IBBA/NACVA standards, we haven't pursued formal certification or endorsement.

This page shows actual code examples and implementation details. When we have real case studies with user permission, we'll add them here.

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