Aviation · NASDAQ: FTAI

An AI executive's read of your business, and the operator who can run it.

I'm Rene Charbonneau. I spent eight years inside airline operations and fifteen rebuilding the systems that run them. I build AI agents by hand. Here is how I read FTAI's model, and how I would digitize the shop floor and add margin.

Scroll. The business, then the operator.

01 · Understanding the business

FTAI sells time on wing, not shop visits.

FTAI pre-builds serviceable modules and swaps an airline's engine in weeks, not a 120 to 270 day shop visit. Green time, used serviceable material (USM), and Parts Manufacturer Approval (PMA) parts, priced 30 to 70% below the original-equipment-manufacturer (OEM) pathway, take cost out of the most expensive line in the engine: parts, which run about 80% of a traditional shop visit. That swap-not-repair model is FTAI's Maintenance, Repair & Exchange (MRE) business.

Scale compounds. More engines lower the cost per module, which wins more airlines, which fills more managed aircraft, which buys more engines. PMA parts pull the unit cost down at every turn.

  1. Own the iron: 1,000+ CFM56 and V2500 engines.
  2. Manufacture green time in the Module Factory.
  3. Exchange rather than repair, airlines swap in weeks.
  4. Captive demand: the managed fleet feeds MRE contracts.
  5. PMA parts run 30 to 70% below OEM, lowering unit cost and looping back to step one.
The core of the P&L

Aerospace Products (MRE)

The Module Factory. Module-level swaps measured in weeks. An owned, managed shop network across Montreal, Miami and Rome.

MRE · swap in weeks, not months
Asset ownership

Aviation Leasing

A portfolio of owned and managed assets that feeds the shops and the exchange pool with a steady supply of engines.

290 assets · 47 aircraft / 243 engines
Third-party capital

Asset Management (SCI)

FTAI's Strategic Capital Initiative (SCI) brings in institutional capital to buy aircraft. FTAI earns management fees, co-invests, and captures captive MRE demand from the fleet it services.

Fees, co-invest, and captive maintenance demand
$830.7M
Q1 2026 revenue +65% YoY
$325.6M
Total adjusted EBITDA
$1.625B
2026 segment EBITDA guidance
Aerospace $1.05B + Leasing $575M
12%→25%
MRE market share, current to target
1,050
2026 module target +39%

02 · Two engines of growth

Two engines, both aimed at the AI economy.

The AI buildout needs two things FTAI already has: electricity it can deliver fast, and a maintenance machine efficient enough to fund the company while it scales. The same CFM56 platform and the same shop network point at both.

~945 TWh
Global data-center electricity demand is on track to more than double by 2030, driven by AI. New power can wait years for the grid. Source: IEA, Energy & AI (2025).
Selling power

FTAI Power

FTAI converts CFM56 cores into 25 megawatt (MW) turbines for data centers that cannot get on the grid for years. It can move faster than a new-build turbine because it already owns the supply. More than 22,000 CFM56 engines have been built, FTAI owns over 1,000, and it runs the shops that remanufacture them. First units are targeted for 2026, scaling toward about 100 Mod-1 units a year through 2027. Jereh Group packages and distributes, FTAI supplies the turbine.

Physical power into the AI buildout, from inventory it already holds.

Running the shop smarter

Palantir partnership

Palantir's Artificial Intelligence Platform (AIP), in a partnership signed November 17, 2025, puts scheduling, inventory and parts procurement on one operating picture. That efficiency is how FTAI targets 1,050 modules in 2026 without adding 1,050 modules' worth of headcount, and how it presses its 12% share of the roughly $25B annual CFM56 market toward 25%.

Operating leverage. More output from the same bench.

One company sits on both sides of the AI economy: selling generation into the buildout, and using AI to widen the margin that funds it. Both ride the same operating data underneath.

03 · The digital shop floor

Digitize the floor first. Then add agents as digital employees.

FTAI's shop floor is still part physical, part spreadsheet. The opportunity is a digital shop floor, where every engine, part and work scope is a live record and the engine has a digital twin. Build that foundation first, then put AI agents on top as digital employees. This is not a software project. It is a transformation project measured in turnaround time, parts cost and margin. Palantir provides the platform; this is the work that feeds it.

Layer 7 to 8Experience

What the floor uses every day: copilots, embedded agents, decision tools.

L8AI Applications: approval inbox, exception dashboardL7Domain agents: work-scope, procurement, asset lifecycle
Layer 5 to 6Intelligence

The working brain: where models reason, retrieve, and stay safe.

L6Guardian & compliance (airworthiness, audit)L5Agentic runtime & orchestration
Layer 1 to 4Foundation

The structural base. Get this wrong and nothing above scales.

L4Knowledge layer: engine ontology + digital twinL3Model foundationL2Data foundation: engine, parts, borescopeL1Infrastructure & security

Reference architecture adapted from an end-to-end AI landing stack (Levels 1 to 8). Own the foundation, own the outcome. Agents at the top are only as good as the data and digital twin beneath them.

Move inputs earlier

Most margin leaks because customer and work-scope data arrives after the engine is already off-wing. The fix is sequencing: rework the intake process, digitize the existing paperwork, and run predictive analytics on parts so config, utilization and findings are known before induction.

Foundational, builds the base others depend on Leverage, more output without new heads Reusable, redeploys across shops and assets
Work scope is set too late
Scope is fixed at induction, so parts are bought after the engine is off-wing, driving turnaround time (TAT) and aircraft-on-ground (AOG) cost.
Foundation & workflow

Stream config, utilization and borescope data into the engine ontology before off-wing. The work-scope planner moves from reacting at induction to planning ahead of arrival.

L2 DataL4 KnowledgeDigital twin
Agents, working together
Data Ingestion AgentStreams config, utilization and borescope feeds pre-arrival
Foundational
Borescope Vision AgentReads inspection imagery for damage and wear signals
Reusable
Work-Scope OrchestratorDrafts the scope and parts kit before the engine lands
Leverage

The orchestrator directs the two specialists, then hands a draft scope to the planner for sign-off.

Life-limited parts live in spreadsheets
Each life-limited part (LLP) needs back-to-birth traceability and cycles-remaining. Scattered systems cost hours and risk findings.
Foundation & workflow

Make every serial number a live record: history, location, cycles, next event. The records and traceability workflow stops being a manual hunt across systems.

L2 DataL4 Knowledge
Agents, working together
Serialized-Parts TrackerTurns each serial into a live back-to-birth object
Foundational
Traceability ValidatorFlags missing paperwork and cycle gaps before they become findings
Leverage
Three shops, three systems
Montreal, Miami and Rome each came in by acquisition with their own work-order conventions, so capacity and TAT are not comparable.
Foundation & workflow

One standard for how a shop visit is recorded across all three sites. The multi-site operations workflow gets a single, comparable picture of capacity and turnaround.

L1 InfraL2 DataL4 Knowledge
Agents, working together
Work-Order NormalizerMaps each site's conventions to one standard schema
Foundational
Cross-Site Capacity AgentCompares TAT and capacity across sites in real time
Leverage
Reconciliation OrchestratorResolves entity mismatches across the three systems
Reusable
Parts buying is reactive
Without pre-condition signals, USM gets bought late and dear, and scope creep adds labor.
Foundation & workflow

Forecast findings from engine history and source material ahead of induction. The procurement workflow shifts from reactive to pre-positioned.

L2 DataL3 Model
Agents, working together
Findings Forecast AgentPredicts likely findings from engine history and imagery
Leverage
USM Sourcing AgentPre-positions used serviceable material before arrival
Reusable
FTAI Power has no aftermarket yet
A turbine sold once, with no recurring service, leaves margin on the table.
Foundation & workflow

Stand up the service-and-parts and uptime model alongside first delivery. The aftermarket workflow applies the module-maintenance model to power assets from day one.

L4 Digital twinL7 Domain agents
Agents, working together
Turbine Uptime MonitorTracks fielded-unit telemetry against a health model
Reusable
Service-Trigger AgentTriggers module maintenance the way it would for an engine
Leverage

Digitize the floor, then conduct the agents. It needs an owner who has run both the shop-floor systems and the profit and loss (P&L).

I am that operator.

Rene Charbonneau · Senior AI & Digital Transformation Executive · Montreal

04 · How I fit

The rare operator for this exact work.

I combine deep airline operations, Fortune 50 systems transformation, FAA-regulated delivery, and hands-on AI architecture. I can turn FTAI's shop floor digital faster and more completely than a conventional hire. I am in Montreal, where the shops are.

Airline & regulated systems
  • 10+ years in the airline industry
  • Built maintenance and in-flight aircraft applications at Air Canada that required FAA sign-off before use
  • Fluent in on-time performance, turnaround margins and MRO pressure
Enterprise transformation
  • Fortune 50 ERP (enterprise resource planning) rewrite plus full change management
  • Digital transformations for Fortune 500 companies; cross-vendor data migration
  • $18M+ P&L, 8 units, ~150 people, 6 countries; multi-site scale across acquired shops
  • Air Canada: migrated 60+ applications to Amadeus across 10+ vendors. Built teams and systems across all areas of operations: maintenance, in-air, ramp and airport. Grew the team 6 to 75 in five months
AI builder
  • Anthropic Claude Platform Architect Certification, one of the first in the world
  • Delivered AI workshops to Microsoft and multiple client business units
  • Stood up data foundations for production AI systems and enterprise data platforms consolidated from multiple acquisitions
  • Audit-grade delivery: CISA certified; SOC 2 and regulatory reporting at a broker-dealer; 25% timeline compression
Each thing FTAI needs, what I bring, and where I have already done it.
What FTAI needsWhat I bringTrack record
Audit-grade discipline. Federal Aviation Administration (FAA)-regulated shops, investor reporting on SCI Regulated delivery where the books get inspected Certified Information Systems Auditor (CISA); built SOC 2 and regulatory reporting at a broker-dealer
Run multi-site scale to a P&L Operator accountability, not advice $18M+ P&L, 8 units, ~150 people, 6 countries
One standard across acquired shops; move data between vendors Large-scale aviation systems work Air Canada: migrated 60+ applications to Amadeus across 10+ vendors. Built teams and systems across all areas of operations: maintenance, in-air, ramp and airport
Take cost and time out of the shop, without adding heads Delivery that compounds, priced and measured Cut delivery timelines 25%; pricing models with auditable assumptions
Digitize the floor and stand up the data foundation I build it, so it runs rather than stays theoretical Data foundations for production AI; Databricks, Snowflake, Fabric, Purview, FinOps
Someone on the ground Already here Montreal-based
Proof I build, not just advise

Code is conducted, not written.

I run a personal multi-agent build system at home that ships real, tested software. An orchestrator directs specialist agents, an independent agent does QA, and a governed harness handles tests, parallel work and deployment. I do not talk about this work from the outside. I operate it daily.

How my multi-agent build system is wired, and what each part does
One orchestrator, a fleet of specialist agents, and a governed harness underneath.
Human operator Sets goals, approves plans, breaks ties
Orchestrator Routes every request and dispatches all agents, then sequences QA after the architects Claude Code · Sonnet
spawns specialist agents
Strategy

Cross-project strategy and the scope-to-retro delivery pipeline

Claude Code · Opus
Advisors

Technical, product, security, knowledge and growth briefs

Claude Code · Opus
Project architects

End-to-end delivery in-fork: plan, build, verify, open the PR

Claude Code
Independent QA

A fresh fork per PR that reviews the diff with no inherited context

Claude Code · Sonnet
Infrastructure

Home network, container media stack, DNS and device onboarding

Claude Code + MCP
Release engineering

Deploy pipeline, service supervision and the runtime substrate

Claude Code + launchd
Always-on ops

Read-only fleet health, log tails and ticketing from the host

OpenClaw gateway
Session analyzer

Classifies sessions and tracks cost and process compliance

Python CLI
Governed harnessHook guardrails · parallel git worktrees · tests · deploy · audit trail
Connector planeGitHub · Jira · Playwright · CI/CD · MCP servers
Home infrastructureAlways-on host · OrbStack containers · AI runtime (Hermes)
First 90 days · 01

Walk the floor

Sit with Montreal, Miami and Rome. Find where turnaround and parts cost actually leak.

First 90 days · 02

Digitize one shop visit

Take a single work-scope or procurement step, put data ahead of induction, and measure turnaround and parts cost against baseline.

First 90 days · 03

Make it repeatable

Turn the win into a standard play with the math that shows it pays for itself, then sequence it against the ramps.

Self-funding: hit 1,050 modules without 1,050 modules' worth of headcount.

Let's talk

Bring the operator in before the ramp, not after.

Rene Charbonneau Let's talk