YOUR AI ANALYTICS PARTNER
Aerlytix was built from within the aviation finance ecosystem and continues to shape it, and be shaped by it. This makes us strongly positioned to guide you at every stage of your AI journey.
We’ve been building the aviation finance intelligence foundation that makes your AI trustworthy, ensuring AI outputs that are grounded in context, accuracy, and business relevance.
This latest capability enables Aerlytix users to interact with our financial models in a conversational format from inside their own AI agents, producing precise answers using your real aviation finance data.
Our models are built with a deep understanding of the aviation finance industry, continuously enhanced by our active userbase.
Our AI will connect directly into your existing AI assistant allowing it to query live records and cross-reference relationships across Aerlytix in real time.
Every output comes with an audit trail back to the underlying data and model logic so you can show your work when it matters.
Users maintain full control while role-based permissions mean you don't compromise on data governance.
This opens up a new way to interact with your data. Our infrastructure will allow you to converse with our trusted models from inside your AI agents, creating expansive new possibilities for your organization.
Not only that, but it will produce answers you can trust. Every response will be traceable back to your real, structured data and our battle-tested financial models that come with judgement you can trust already built-in.
Surface insights across your portfolio that would take hours to find manually.
Faster answers to routine questions, freeing your team for higher-value work.
Every output is traceable to a formula and a data source, auditable and defensible.
Retrieve/summarize information leveraging the computational strength and calculation engine within Aerlytix.
Create, modify or stress-test existing scenarios or information within Aerlytix.
Trigger actions within Aerlytix – push analyses into the platform, interact and commit scenarios, and more.
Adjust flight-hour and cycle assumptions to model high and low burn periods.
View impact of utilization shock to cashflows and maintenance events.
Set core engine management assumptions throughout the forecast, including RDC management, soft limit approach, short build on FSV, items that drive the biggest cost line in any forecast.
Shape the maintenance workscope applied at each event so forecasted shop-visit costs reflect the real work planned.
Review impact of workscope parameters for SV optimization.
Toggle redelivery conditions on or off to compare an RDC-enforced position against a waiver scenario - directly relevant to end-of-lease analysis.
Apply inflation assumptions across the maintenance cost base.
Understand the inflection point of market inflation which may generate an exposure on your investment.
Run full sale and valuation assumptions against an asset to frame disposition and residual value outcomes.
Set the core acquisition inputs to calculate IRR, Net Purchase Price or Gross Purchase price.
Choose the LLP limit methodology used in the maintenance forecast to control planning v.s regulatory limits.
Get in touch with our team to arrange a first look demo of our platform and AI capabilities.
We focus AI enhancements on addressing real challenges today while creating a pathway for long-term transformation. Here are some of the platform’s AI use cases already delivering tangible value for our clients.
Financial statement spreading is one of the most time-consuming parts of counterparty analysis.
The system automates the spreading of financials including income statements, balance sheets, and cash flow statements, automatically mapping line items into the client’s credit model instead of this task taking hours to be manually input into Excel.
Automated Technical Specification processing removes the burden of manual data entry by extracting and mapping information directly from document source.
Users can automatically extract and populate key information from seller packs, including component serial numbers, utilization metrics, part descriptions, and other static asset attributes, significantly reducing administrative effort and accelerating analysis workflows.