debt_leverage_profile
-0.31x Total Debt / Equity (Negative equity)
High
SEC-XBRL
interest_rate_sensitivity
Atlantis Glory is a small-cap shipping/marine company; rising rates by 20bps modestly increase vessel financing costs, estimated 2-4% net income drag given typical asset-heavy leverage ratios
Inferred
Agent_Inference
geopolitical_supply_exposure
Medium intensity; US-China trade tariffs on metals and components disrupt supply chains.
Medium
GICS-commodity-overlay-v1
supply_chain_dependency
South China Sea shipping lanes and Panama Canal transit bottlenecks represent primary geopolitical chokepoints for vessel routing and cargo operations
Inferred
Agent_Inference
international_expansion_readiness
Exposed to USD/Asian currency volatility (RMB, PHP, SGD) across primary Asia-Pacific revenue markets; unhedged devaluation risk moderate given regional trade focus
Inferred
Agent_Inference
geographic_footprint
Primary exposure in Southeast Asia and Pacific Rim markets; RMB, Philippine Peso, and Singapore Dollar devaluation risk is moderate with limited formal hedging disclosed
Inferred
Agent_Inference
commodity_exposure_profile
Medium intensity; commodities: Steel, Aluminum, Copper, Crude Oil (fuel), Rare Earth Elements, Plastics/Resins; geopolitical: US-China trade tariffs on metals and components disrupt supply chains.
Medium
GICS-commodity-overlay-v1
vendor_lock_dependency_score
Fuel suppliers and port service operators likely represent concentrated input costs; single fuel vendor dependency plausible above 30% threshold in niche shipping operations
Inferred
Agent_Inference
business_model_type_primary
Cloud infrastructure dependency appears low; core operations are vessel-based physical logistics, so 30-day cloud termination would disrupt booking/admin systems but not core revenue delivery
Inferred
Agent_Inference
business_model_type_secondary
Secondary digital systems (freight management, customer portals) could face 2-4 week operational disruption; manual fallback via legacy systems is operationally feasible short-term
Inferred
Agent_Inference
switching_cost_profile
API coupling risk is low; company operates primarily in physical freight/shipping, with minimal deep API integrations to third-party platforms creating lock-in
Inferred
Agent_Inference
howey_test_risk_index
Primary revenue from freight and vessel charter services; low Howey Test risk as revenue model is service-based, not investment-contract structured — securities classification risk minimal
Inferred
Agent_Inference
regulatory_burden_tier
Medium
Medium
GICS-regulatory-overlay-v1
data_sovereignty_risk
Moderate GDPR/CCPA exposure given cross-border shipping data handling; customer manifest and cargo data flows across jurisdictions require compliance infrastructure often underdeveloped in small-cap shippers
Inferred
Agent_Inference
antitrust_exposure_flag
Low antitrust exposure; Atlantis Glory is a minor player in fragmented global shipping market with no dominant market share position warranting regulatory scrutiny
Inferred
Agent_Inference
regulatory_exposure_profile
Medium burden; regimes: FAA, DOT, OSHA, EPA, ITAR, FTC; Export controls and defense procurement rules create contract concentration risk.
Medium
GICS-regulatory-overlay-v1
revenue_model_type
Predominantly transactional (estimated 70-80% spot charter/freight contracts); limited long-term recurring contract revenue typical of small independent shipping operators
Inferred
Agent_Inference
monetization_vector
Primary monetization via voyage charter fees and freight rate billings; secondary from time-charter agreements providing limited revenue predictability
Inferred
Agent_Inference
pricing_architecture
Pricing tied to volatile spot freight rate indices (Baltic Dry Index proxies); limited pricing power under rate compression — revenue highly sensitive to global shipping demand cycles
Inferred
Agent_Inference
pricing_power_rating
Weak pricing power (2/10); commodity-like freight services with rates dictated by market indices, not proprietary value — minimal ability to hold rates during downturns
Inferred
Agent_Inference
target_gross_margin_bracket
24.9% Gross Margin (Moderate (20-40%))
High
SEC-XBRL
churn_vulnerability_index
Low free-rider leakage risk; physical shipping services require direct payment — no digital free-tier model. High churn vulnerability from spot-market customers with zero switching costs
Inferred
Agent_Inference
headcount_cost_structure
Revenue growth is largely sublinear to shore-based headcount but linear to vessel crew count; doubling revenue requires proportional vessel/crew expansion — capital and labor intensive
Inferred
Agent_Inference
marginal_cost_of_growth
High marginal cost of growth; each revenue increment requires additional vessel capacity, crew, fuel, and port fees — no software-style leverage in cost structure
Inferred
Agent_Inference
franchise_compliance_risk
Not a franchise model; compliance drift risk is N/A for franchise networks, though IMO/maritime regulatory compliance drift across vessel fleet is a relevant operational analog
Inferred
Agent_Inference
customer_acquisition_metric
At 10x scale, CAC efficiency likely deteriorates; freight brokerage relationships and direct shipper contracts require proportional sales/ops headcount — no scalable digital acquisition funnel evident
Inferred
Agent_Inference
network_effect_present
No meaningful network effects present; shipping capacity is a commodity service with no cross-side or same-side network dynamic — scale provides minor route efficiency advantages only
Inferred
Agent_Inference
asset_efficiency_ratio
-435.5% Return on Assets (Negative equity)
High
SEC-XBRL
recession_resistance_tier
Tier 3 (cyclical/vulnerable); dry bulk and general cargo shipping volumes are highly correlated with global trade cycles and contract sharply in recessions
Inferred
Agent_Inference
customer_segment_primary
Industrial commodity shippers and trading companies represent primary customer segment; high concentration risk likely with top 3-5 customers representing estimated 40-60% of revenue
Inferred
Agent_Inference
customer_segment_secondary
Secondary segment includes regional importers/exporters in Southeast Asia; similarly transactional with low loyalty, high price sensitivity, and easy competitor substitution
Inferred
Agent_Inference
characteristic_occupations
["11-0000 Management Occupations", "13-0000 Business and Financial Operations Occupations", "15-0000 Computer and Mathematical Occupations", "17-0000 Architecture and Engineering Occupations", "23-0000 Legal Occupations", "33-0000 Protective Service Occupations", "37-0000 Building and Grounds Cleaning and Maintenance Occupations", "41-0000 Sales and Related Occupations", "43-0000 Office and Administrative Support Occupations", "47-0000 Construction and Extraction Occupations", "49-0000 Installation, Maintenance, and Repair Occupations", "51-0000 Production Occupations", "53-0000 Transportation and Material Moving Occupations"]
High
SOC-2018/GICS-overlay
agent_automatable_labor_share
0.3 (HIL — ~30% of characteristic roles agent-automatable)
Medium
SOC-2018 + agentic-exposure-v1
capital_expenditure_profile
131.6% CapEx / Revenue (High-CapEx Infrastructure)
High
SEC-XBRL
sec_cik
0001673504
High
SEC-EDGAR
ticker
AGLY
High
SEC-EDGAR