Intelligent Crew & Aircraft Scheduling Optimization
Airlines lose billions annually from operational inefficiencies, flight disruptions, and outdated systems that impair customer experience and revenue
OptiCrew continuously optimizes crew pairings, aircraft routing, and maintenance schedules using constraint programming and real-time disruption data. It replaces fragmented legacy scheduling tools with a unified platform that maximizes asset utilization while maintaining regulatory and contractual compliance.
Airline operations executives and revenue managers at major U.S. carriers handling 50M+ passengers/year
Holistic optimization engine that simultaneously solves for crew, aircraft, and maintenance constraints — current systems solve these in silos leading to suboptimal outcomes.
precise and efficient
Simultaneously optimizes crew, aircraft, and maintenance schedules using advanced algorithms
Automatically adjusts schedules when disruptions occur
Ensures all generated schedules meet FAA, union, and airline-specific rules
Visualizes aircraft and crew utilization with improvement opportunities
Bi-directional integration with existing crew management and flight planning systems
Scenario modeling for different operational parameters
Creates equitable and efficient monthly crew schedules
Incorporates latest scientific fatigue models into scheduling
Notifications and schedule changes delivered to crew members
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| name | text | No |
| created_at | timestamp | No |
| fleet_size | int | Yes |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| organization_id | uuid | No |
| text | No | |
| role | text | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| organization_id | uuid | No |
| schedule_type | text | No |
| start_date | timestamp | No |
| utilization_rate | int | Yes |
| status | text | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| schedule_id | uuid | No |
| crew_id | text | Yes |
| aircraft_id | text | Yes |
| route | text | No |
| compliance_score | int | No |
Relationships:
/api/optimizeTrigger schedule optimization with given constraints
/api/schedulesRetrieve current and upcoming optimized schedules
/api/disruptions/reoptimizeTrigger immediate re-optimization after disruption
/api/utilizationGet current aircraft and crew utilization metrics
/api/integrations/syncSynchronize with legacy crew management system
Max 50 crew members
Up to 250 crew members
Unlimited scale
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 18 | 22% | $124 | $1,488 |
| Month 6 | 145 | 33% | $1,510 | $18,120 |
OptiCrew replaces fragmented scheduling systems with intelligent, unified optimization of crew, aircraft, and maintenance — delivering 12-18% better utilization.
Target crew planning directors at medium and large US carriers (Allegiant, Hawaiian, Breeze) through industry contacts and LinkedIn. Offer a 60-day optimization pilot comparing current schedules against OptiCrew recommendations with guaranteed utilization improvement targets. Convert pilots to paid contracts by demonstrating hard ROI.
Long history in aviation
Siloed tools rather than holistic optimization
Unified solver for crew + aircraft + maintenance
Very sophisticated algorithms
Extremely expensive with long implementation
Affordable, quick to deploy SaaS model with real-time capabilities
Optimization models improve with more airline data (anonymized). High complexity of constraint programming creates technical moat and high switching costs once integrated.
Computational advances in constraint programming and open-source solvers like OR-Tools have made sophisticated real-time joint optimization practical for mid-size carriers for the first time.
Optimization problems can be computationally expensive
Use progressive optimization with early feasible solutions and cloud function scaling
Airlines have significant inertia around scheduling systems
Start with shadow mode (parallel running) to prove value without operational risk
Complex union and regulatory rules vary by carrier
Build highly configurable rule engine and engage aviation labor experts
Success: Map current pain points and secure 8 letters of intent for pilot participation
Success: Demonstrate minimum 10% utilization improvement vs baseline schedules
Success: Document measurable improvement and convert minimum 2 to paying customers
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