KeepFlying provides a data-to-dollar platform that utilizes generative and predictive AI to analyze airworthiness and maintenance data, enabling airlines and MROs to optimize engine maintenance and reduce operational costs. By accurately predicting scrap rates and work scope levels, the platform enhances decision-making and improves profitability across the aviation supply chain.
Funding
$1.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Airlines, lessors, and MROs face challenges in efficiently processing and analyzing complex airworthiness and maintenance data, leading to suboptimal decision-making in engine maintenance and increased operational costs. Predicting component scrap rates and accurately profiling shop visits remains difficult due to the intricate interplay of operational, technical, and environmental variables. This lack of foresight hinders effective planning, budgeting, and resource allocation across the aviation supply chain.
Solution
KeepFlying offers a data-to-dollar platform leveraging generative and predictive AI to streamline the analysis of airworthiness and maintenance data, enabling stakeholders to optimize engine maintenance strategies and reduce operational expenditures. The platform's AI agents, including SkyBot, MROBot, and LeaseBot, provide use-case-driven insights for engines and airframes. By accurately forecasting scrap rates, profiling shop visits, and predicting work scope levels, KeepFlying empowers users to make informed decisions regarding procurement, maintenance planning, and lease transitions. The FinTwin module allows users to run "what-if" scenarios to simulate the commercial impact of decisions against assets and maintenance visits. The platform facilitates proactive commercial planning, minimizes turnaround times, and enhances profitability across the aviation ecosystem.
Target Audience
KeepFlying primarily targets airlines, lessors, and MROs seeking to optimize their engine maintenance strategies, reduce operational costs, and improve decision-making through AI-powered data analysis.
Features
- **Scrap Rate Predictor:** Predicts component scrap rates by analyzing operational, technical, and environmental parameters, enabling proactive procurement planning.
- **Shop Visit Profiler:** Profiles engine shop visits to optimize modular lifespans, conserve cash, and design optimal build goals.
- **Work Scope Predictor:** Forecasts repair work scope levels for engine modules, facilitating effective planning and budgeting for associated costs.
- **Trade Off Maintenance Vs Fuel Consumption:** Computes EGTM deterioration profiles and ongoing fuel costs to determine the optimal timing for engine shop visits.
- **Slot Profitability Forecaster:** Predicts variances in engine cost buildup to ensure adherence to slot profitability targets in fixed-price maintenance contracts.
- **Gate 0 Profiler:** Interprets incoming engine documents using smart asset type driven OCR with ATA Spec2500-driven standards, reducing processing time for engineering teams.
- **Contract Stress Tester:** Simulates shop visit lifecycles to rigorously evaluate fixed-price contract elements concerning work scope levels, scrap limits, material, and labor caps.
- **Records Copilot:** Provides a ChatGPT-like interface for querying and retrieving relevant information from back-to-birth records through natural language conversations.
- **Automatic Redelivery Binder Generator:** Streamlines risk mitigation and cost escalation calculations for aircraft redeliveries.
- **ATA SpXchange:** Extracts and converts technical PDF content into a machine-readable data structure, enabling faster data consumption.