debt_leverage_profile
0.33x Total Debt / Equity (Conservative)
High
SEC-XBRL
interest_rate_sensitivity
High sensitivity; ~$800M+ debt load means 200bps rate rise adds ~$16M annual interest expense, compressing EBITDA margins ~2-3pts
Inferred
Agent_Inference
geopolitical_supply_exposure
High intensity; OPEC+ supply decisions and Russia-Ukraine conflict drive price volatility.
Medium
GICS-commodity-overlay-v1
supply_chain_dependency
1) Chinese rare-earth processing for power electronics; 2) Texas/Gulf Coast transformer manufacturing bottleneck for grid-scale equipment
Inferred
Agent_Inference
international_expansion_readiness
Minimal; Solaris operates almost entirely in US domestic markets, negligible sovereign currency devaluation exposure
Inferred
Agent_Inference
geographic_footprint
Predominantly US-based (Texas, Permian Basin focus); effectively zero material international revenue, no meaningful FX devaluation risk
Inferred
Agent_Inference
commodity_exposure_profile
High intensity; commodities: Crude Oil, Natural Gas, Coal, Refined Petroleum Products, Uranium, Steel (equipment); geopolitical: OPEC+ supply decisions and Russia-Ukraine conflict drive price volatility.
Medium
GICS-commodity-overlay-v1
vendor_lock_dependency_score
Caterpillar/CAT engines represent estimated 40-50% of mobile power unit input costs; high substitution friction given certified-parts requirements
Inferred
Agent_Inference
business_model_type_primary
Asset-heavy equipment rental/services; cloud infrastructure termination would cause operational disruption but not existential failure within 30 days
Inferred
Agent_Inference
business_model_type_secondary
Field operations and logistics management software dependency; 30-day cloud termination would impair dispatch and billing but physical assets remain deployable
Inferred
Agent_Inference
switching_cost_profile
Low-to-moderate API coupling risk; primary operational systems are fleet/logistics management tools with available alternatives within 60-90 day migration window
Inferred
Agent_Inference
howey_test_risk_index
Low Howey Test risk; revenue model is equipment rental and energy infrastructure services — clearly a commodity service, not an investment contract
Inferred
Agent_Inference
regulatory_burden_tier
High
Medium
GICS-regulatory-overlay-v1
data_sovereignty_risk
Low GDPR/CCPA exposure; customer base is primarily US oil & gas operators with limited consumer PII; no material EU data processing identified
Inferred
Agent_Inference
antitrust_exposure_flag
Low-moderate; fragmented mobile power/energy infrastructure market with competitors (NFES, others); no dominant market share triggering scrutiny
Inferred
Agent_Inference
regulatory_exposure_profile
High burden; regimes: EPA, FERC, DOE, CFTC, OSHA, SEC; Accelerating emissions mandates and methane rules threaten capex economics.
Medium
GICS-regulatory-overlay-v1
revenue_model_type
~70-75% recurring via multi-month field deployment contracts; ~25-30% transactional spot rentals and equipment sales
Inferred
Agent_Inference
monetization_vector
Per-unit equipment rental day-rates plus ancillary service fees (fuel, maintenance); secondary vector is equipment sales/remarketing
Inferred
Agent_Inference
pricing_architecture
Day-rate model tied to diesel/natural gas input costs; stress point is margin compression when fuel costs spike without contractual pass-through clauses
Inferred
Agent_Inference
pricing_power_rating
Moderate; differentiated by reliability and Permian Basin proximity, but commodity-adjacent pricing limits sustained above-market rate increases
Inferred
Agent_Inference
target_gross_margin_bracket
Estimated 35-45% gross margin; equipment rental model with high depreciation drag; below SaaS but above pure commodity services
Inferred
Agent_Inference
churn_vulnerability_index
Low free-rider risk; physical asset deployment requires contractual commitment; switching costs moderate due to mobilization/demobilization expenses
Inferred
Agent_Inference
headcount_cost_structure
Moderately headcount-linear; doubling revenue requires proportional field technicians and drivers, though HQ/G&A scales sublinearly above current base
Inferred
Agent_Inference
marginal_cost_of_growth
Capital-intensive marginal growth; each incremental revenue dollar requires fleet capex ~$0.30-0.40, limiting free cash flow leverage at scale
Inferred
Agent_Inference
franchise_compliance_risk
Not applicable; Solaris operates as a direct-service company with no franchise network
Inferred
Agent_Inference
customer_acquisition_metric
At 10x scale, CAC likely rises as Permian Basin market saturates; must expand geographies, increasing logistics costs and reducing unit economics
Inferred
Agent_Inference
network_effect_present
No meaningful network effects; equipment rental is a linear service business — additional customers do not enhance value for existing customers
Inferred
Agent_Inference
asset_efficiency_ratio
3.4% Return on Assets (Excellent)
High
SEC-XBRL
recession_resistance_tier
Tier 3 (cyclical); revenue highly correlated with E&P capex spending; 2020-type oil price crash demonstrated ~30-40% revenue decline vulnerability
Inferred
Agent_Inference
customer_segment_primary
Large independent and major oil & gas operators (E&P companies) in Permian Basin; top-5 customers likely represent 50-60% of revenue
Inferred
Agent_Inference
customer_segment_secondary
Midstream pipeline and infrastructure operators requiring temporary power; smaller concentration but growing segment for grid-independence use cases
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", "19-0000 Life, Physical, and Social Science Occupations", "23-0000 Legal 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", "53-0000 Transportation and Material Moving Occupations"]
High
SOC-2018/GICS-overlay
agent_automatable_labor_share
0.33 (HIL — ~33% of characteristic roles agent-automatable)
Medium
SOC-2018 + agentic-exposure-v1
capital_expenditure_profile
103.9% CapEx / Revenue (High-CapEx Infrastructure)
High
SEC-XBRL
sec_cik
0001697500
High
SEC-EDGAR
ticker
SEI
High
SEC-EDGAR