Process Airworthiness & Maintenance Data faster for Commercial Insights

KeepFlying's Generative & Predictive AI gets your datasets LLM ready to generate ROI across your business needs.

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Airlines spend over USD 2 Million per Narrow Body Redelivery

Engine MROs miss out on over USD 250,000 in bottom line per Engine Overhaul

Lessors spend over USD 50,000 per Aircraft or Engine Records Inspection

AI Agents driven Use cases powered by

SkyBot, MROBot & LeaseBot

Engines
Airframe
Scrap Rate Predictor

Scrap Rate Predictor

What?
Predicting which components will be scrapped in the Next Maintenance Visit necessitates a clear understanding of the varying complications in an Engine's history, spanning multiple operators, environments, thrust ratings, and maintenance visits, and the respective weightage assigned to each technical, operational, and environmental variable.
Why?
Accurate prediction of scrap rates enables the implementation of procurement plans to ensure part availability during repair, mitigating delays in procurement timelines and turn-around times, given that often, during repair and inspection, the determination of parts needing repair versus those needing scrap and replacement only becomes apparent.
How?
Our algorithms analyze the behavioral profile of each Engine Serial Number (ESN) by integrating its operational, technical, and environmental parameters to predict scrap rates for each component, incorporating weightage assigned to each parameter, relationships between parameters, and a confidence interval to support prediction accuracy.
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Shop Visit Profiler

Shop Visit Profiler

What?
For Operators or Lessors managing a fleet of Engines at different ages and stages, strategic planning and profiling of Engine Shop Visits are crucial to maximize modular lifespans, conserve cash, and design optimal build goals aligned with overall cash flow objectives.
Why?
Effectively managing a diverse fleet of engines at various lifecycle stages and statuses (In Operation, Shop Visit, Storage) necessitates Lessors or Operators to optimize the residual lives of modules and LLPs as the fleet matures, involving profiling Shop Visits against individual Engines or fleets to analyze statuses, potential module swaps or exchanges within the fleet for cost conservation, and facilitating immediate lease placements to enhance the value and utilization of each Engine.
How?
KeepFlying® algorithms evaluate fleet statuses, predicted scrap rates, work scope levels, and residual lives across modules to compute the commercial impact of build goals, swaps, and exchanges, facilitating the profiling of shop visit costs and placements to optimize profitability and minimize expenses.
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Work Scope Predictor

Work Scope Predictor

What?
An Engine's diverse operational history, spanning different operators, environments, thrust ratings, and maintenance visits, presents varying challenges in predicting repair work scope levels for its modules during the upcoming Maintenance Visit to enable Engine Operators and Lessors to effectively plan and budget for associated costs.
Why?
The capability to forecast repair work scope levels aligned with Engine Build Goals empowers Operators and Lessors to opt for cash conservation and simulate maintenance economics, or in the case of an engine undergoing lease transition, enables them to budget and strategize maintenance to ensure swift turn-around time and smooth transition to the new Operator.
How?
Our algorithms, trained on datasets integrating an Engine's Maintenance and Operational history, analyze and correlate the impact to predict repair work scope levels for each module prior to a visit, often preempting necessary upgrades during module split and strip inspections, facilitating proactive commercial planning and minimizing turn-around time.
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Trade Off Maintenance Vs Fuel Consumption

Trade Off Maintenance Vs Fuel Consumption

What?
The decision to delay maintenance on a mid-life or aging Engine results in increased fuel consumption and expensive shop visit costs, posing challenges in justifying Engine Shop Visits without residual values or clear revenue visibility from usage or lease placements, necessitating a careful trade-off between timely maintenance visits and ongoing fuel expenses.
Why?
Operators must weigh the increasing fuel consumption of mid-life and aging engines against the cost of sending them for a Shop Visit, considering the significantly improved fuel efficiency of next-generation engines, thus necessitating a trade-off decision.
How?
By incorporating the Engine's past shop visit records, removal causes, and predicted scrap rates, our algorithms compute EGTM deterioration profiles and ongoing fuel costs, juxtaposing them with potential revenue streams; the resultant savings from scheduling a Shop Visit not only enhance customer satisfaction but also ensure prolonged engine availability.
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Slot Profitability Forecaster

Slot Profitability Forecaster

What?
Maintaining the commercial viability of slot sales in fixed-price Engine Maintenance contracts necessitates tracking and predicting Engine Cost Build up before and during Engine Induction, ensuring adherence to slot profitability targets.
Why?
Predicting variances in Engine Cost Build up early is crucial for mitigating risks associated with the escalation of work scope levels, upgrades of repairs to replacements, and scrapping of components not covered as part of fixed-price elements, thereby safeguarding profitability of an Engine Shop Visit and preventing adverse impacts on the shop floor.
How?
Our algorithms activate work scope escalation and scrap prediction functions in coordination with capacity constraints to ensure Engine slots meet contracted turnaround times and target profitability, facilitated by dynamic capacity balancing models that assess the impact of scraps, replacements, swaps, exchanges, and repairs on timely Engine turnaround and associated costs to predict slot profitability.
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Gate 0 Profiler

Gate 0 Profiler

What?
Upon Engine MROs receiving Engines into the shop, digital documents arrive in diverse unstructured formats, with the Engineering team typically requiring three weeks to process these incoming documents and devise a shop floor plan.
Why?
Engineering teams must examine the incoming Engine documents from the Lessor or Operator to evaluate any fluctuations in work scope levels and scrap rates against components, thereby ensuring preparedness of the Shop Floor and Procurement teams with the necessary labor and material capacity to meet contractual obligations within the turnaround time.
How?
By combining smart asset type driven OCR with ATA Spec2500-driven standards, our algorithms interpret incoming Engine documents, potentially reducing processing time for Engineering teams from three weeks to a few days.
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Contract Stress Tester

Contract Stress Tester

What?
Engine MROs engage in negotiating fixed-price contracts with Operators and Lessors to enhance their bid-winning prospects, though they face the risk associated with varying levels of repair work scopes required for engines and the MRO's capacity to meet demand within contracted turnaround times.
Why?
Engine MROs strive to optimize their capacity utilization across fixed-price LTAs with Operators, Lessors, OEMs, while also accommodating capacity for time and material-driven hospital visits to maximize profitability, necessitating thorough stress testing of fixed-price contract slots before committing to terms and conditions related to repairs, materials, scraps, and labor.
How?
Our algorithms conduct Shop Visit lifecycle simulations, ranging from slot prospecting to induction, utilizing either a limited or exhaustive set of parameters against an Engine Serial Number (ESN) to rigorously evaluate fixed price elements concerning work scope levels, scrap limits, material, and labor caps, allowing for multiple simulations to be conducted prior to finalizing contract terms and conditions.
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Gates 1-4 Tracker

Gates 1-4 Tracker

What?
Tracking planned versus projected Shop Visit progress once the Engine is in shop is critical to manage aspects of disposition, kitting, and dependencies on external repair vendors and suppliers that may impact TAT, while predicting repairs that may upgrade to a replacement or material risks owing to vendor performance poses a challenging task.
Why?
Tracking and mitigating gate-wise risks is crucial once a fleet of engines is in the shop to ensure all engines are returned within contracted Turnaround Time(TAT), and in case of delays, providing risk mitigation avenues is essential to uphold overall profitability.
How?
Our algorithms assess engine status, monitor cost accumulation, and forecast deviations to assess risk mitigation strategies, leveraging vendor performance ratings to recommend potential repairs, swaps, and exchanges to optimize TAT savings and prioritize ESNs for scheduling algorithms aimed at achieving the optimal balance between TAT and profitability.
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Records Copilot

Records Copilot

What?
Aircraft and maintenance records, which can exist across various systems including Records Management Systems, Content Management Systems, and MRO Software Systems, are typically unstructured and can be found in PDF, Excel, or CSV formats.
Why?
Interpreting Aircraft and Maintenance Records from diverse unstructured sources involves context-based analysis beyond OCR, enabling faster decision-making, while resolving duplicate or conflicting data sources for coherence. 
How?
Our records interpretation platform provides a ChatGPT "like" interface that offers users to efficiently query and retrieve relevant information from back-to-birth records through natural language conversations.
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Aircraft Transition Economics Monitor

Aircraft Transition Economics Monitor

What?
During a lease transition, obtaining access to all aircraft technical records is essential, especially for older aircraft with multiple previous operators, as assembling complete records spanning the aircraft's entire history can be difficult.
Why?
Identifying critical data, such as for back-to-birth tracing of life-limited components or modifications that need to be carried out in concurrence with new lessee's requirements, amidst this vast repository can be particularly challenging. Arriving at cost estimates for this transition can become a time-consuming process.
How?
Enabled by the Records Co Pilot feature, this function facilitates Lessors and Airlines in appraising and estimating transition efforts and expenses during the transfer of an Aircraft between lessees, assessing costs associated with bridging programs and modifications necessary for transitioning the aircraft from its current state to a "to-be-delivered" status by examining records and conditions.
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Automatic Redelivery Binder Generator

Automatic Redelivery Binder Generator

What?
During aircraft redeliveries, the return conditions outline how the previous lessee is expected to redeliver the aircraft, while the delivery conditions specify how the aircraft should be presented to the next lessee. Ideally, these conditions would align perfectly, but in reality, such alignment is rare.
Why?
The timeframe between transitions and the potential consequences of redelivery violations can impact both lessees and lessors significantly. Initiating this process 12-18 months prior to redelivery is crucial to facilitate a seamless aircraft transition and to identify avenues for mitigating risks, ensuring a trouble-free redelivery experience.
How?
The function streamlines risk mitigation and cost escalation calculations for Aircraft Redeliveries, with the Records Co Pilot tool aiding in rapid interpretation of records and redelivery conditions, while algorithms forecast statuses and identify avenues for risk reduction to facilitate cost savings, binder generation, and ensure a smooth transition.
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ATA SpXchange

ATA SpXchange

What?
Aircraft technical and maintenance records, generated from various systems and formats, undergo exchange in diverse formats when aircraft transition between lessees, redelivered to a lessor and delivered to another lessee, or return from maintenance visits.
Why?
The Air Transport Association (ATA) has established standards for data exchange within the aviation ecosystem, facilitating seamless transfer of records by defining specifications for aircraft records, configuration, ARCs, eTech logs, maintenance summaries, reliability, and supply chain data.
How?
The KeepFlying® SpXchange ATA Specification Platform is a modularized approach to each ATA Specification. It provides the necessary tools to wrangle data that is not in an ATA Specification and output that data in the appropriate ATA Specification. This can then be further integrated in to or interrogated by down-stream systems removing the need for expensive or time-consuming human data-capture processes.
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For MRO's

For Airlines & Lessors

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Our Platform.

FinTwin®

Allows Airlines, MROs, Lessors & Financiers to run "what-if" scenarios to simulate commercial impact of decisions against Assets and Maintenance Visits

ATA SpXchange

Extracts & converts 100% of technical PDF content into a Machine-Readable data-structure. This is proven to enable faster data-consumption.

Data Wrangler

Ensures airworthiness and maintenance data integrity by integrating diverse sources into a unified, accurate view, enhancing decision-making and operational efficiencies.

Skybot™

Aviation-specific LLM-powered chatbot (integrated with FinTwin® platform), designed to effortlessly query large datasets and extract vital information.

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