KeepFlying Onboards Play Airlines as a customer


Residual Life & Cash Flow Forecasting

Engine Owners and Lessors will be able to use the KeepFlying® Digital FinTwin® to support forecasting Risks and Revenue Potential over the course of an Asset’s Remaining Useful Life (RUL) and manage seamless Engine transitions & Redeliveries.

Maximizing Remaining Useful Life (RUL)
Enhanced Redelivery Management
Machine Learning Driven Cash Flow Forecasting


You will have your very own Digital FinTwin® mirroring your Owned, Leased & Financed Assets on our cutting-edge platform between 8 and 12 weeks from commencing a data implementation exercise.

KeepFlying® has specialized staff for delivering our services. Each customer will have their own dedicated implementation manager. They handle the entire lifecycle of the implementation from data profiling, data preparation & quality control to tailoring the models towards your specific needs.

Data Profiling &

1 - 2 Weeks


2 - 4 Weeks

Model Tuning & 

1 - 2 Weeks

Evaluation &
User Training

2 Weeks

Acceptance &

2 Weeks


Design Asset Driven Data Models to simulate impact of decisions on Engine & LLPs Remaining Useful Lives, Residual Values and Redelivery risks.
Maximizing Remaining Useful Life
Augment historic and dynamic data to visualize cost forecasts and  revenue maximization using “what-if-simulations” based RUL models.
Enhanced Redelivery Management
Design Engine utilization scenarios to simulate redelivery risks including non-compliance to return conditions including LLP Over-runs. Visualize the commercial impact of delayed redeliveries.
ML Driven Cash Flow Forecasting
Forecast cashflows against an Engine or a portfolio of Engines to visualize maintenance costs, reserves, rentals as a factor of scenario driven models.

Maximizing Remaining Useful Life

Simulate the impact of projected utilization of your Engine against scenario driven forecasts to gauge commercial viability of trades, leases and placements to maximize revenue potential over the course of its Remaining Useful Life.
Engine Data Wrangling
Apply customized ML algorithms to augment historic & dynamic data feeds with operational data for building accurate Engine Profiles.
RUL Scenario Forecasting
Configure Engine type Data models (IAE V2500, RR Trent 700, GEnx) based on Engine Profiles to visualize projected Revenue Forecasts over RUL.

Enhanced Redelivery Management

Apply the power of advanced AI models & smart context based OCR to assimilate redelivery binders and  run cost analytics that highlight redelivery risks and over-runs as a factor of current and projected Technical, Operational parameters.
Smart Redelivery Manager
Drive redelivery simulations using smart OCR on records in conjunction with return conditions to predict costs as a factor of rentals, reserves, LLP and phase-outs.
Redelivery Risks
Predict redelivery risks with respect to return conditions as factors of running costs, non-compliance using historic & projected technical, environment & operational parameters.

ML Driven Cash Flow Forecasting

Model Asset finance and loan / lease payoffs against your portfolios to assess risks, rewards as a factor of lessee credit risks, jurisdictional risks to build a cash in – cash out funnel as a factor of payments, maintenance costs, reserves and rentals.
Mine Finance Data
Use pre-configured smart models to map finance data against Engine / Portfolio to model cash flows against loans / leases terms & parameters
(interest rates, inflation, volatility cycles).
Assess Trade Viability
Simulate the impact of Engine / Portfolio configured finance models in conjunction with projected costs and revenues across scenarios to gauge commercial viability of existing and future trades / leases.

Need a demo?

Understand how your current & future portfolio of Engines can forecast cash flows by configuring the Engine FinTwin® Asset Owner Edition to build your FinTwin®.

Drop a note and our team will get back to you with a data and powerplant expert to help setup an environment for you.
We never share you details with third parties.
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