AI in Safety- and Mission-Critical Aerospace Systems

Enabling the future of intelligent aerospace systems

AI is changing the aerospace landscape—and fundamentally changing how aerospace systems are designed, built, validated, and operated.

The challenge is ensuring these systems remain safe, effective, reliable, and ready for the mission while taking advantage of the new capabilities they enable.

Aero Intelligence works with aerospace and defense leaders to resolve the development, technical, and validation challenges that arise when integrating AI.

Our mission is to accelerate aerospace innovation through clear decisions and disciplined execution, delivering business outcomes, fulfilling customer needs, and satisfying regulatory and operational approval requirements on shorter, more predictable schedules.

Schedule a 30-Minute Program Review See Our Approach
Proven track record: five AI systems taken from zero to fielded operation in ten years, including FAA Part 135 certification.
The Problem

AI doesn’t simply add a new capability to an aerospace system. It changes the system itself.

Integrating machine intelligence into safety-critical systems can provide significant benefits, but it fundamentally changes how those systems must be designed, developed and validated. The technology and development model are changing at the same time.

As systems become increasingly intelligent and autonomous in nature, the challenges evolve as well:

  • How do humans maintain appropriate authority?
  • How can system behavior be validated with sufficient confidence?
  • How does the technology, architecture and organization evolve to support safe, effective, and reliable operation?
What we hear from aerospace leaders:
“We know AI has to be part of the system. We’re not sure how it changes our business, our architecture, our team, or how we’ll prove it’s safe, and nobody here has done it before.”

We work with aerospace and defense leaders facing consequential technical and program decisions where the existing approach is no longer sufficient.

Where We Focus

Fielding aerospace systems that sense, reason and act.

From aircraft and air traffic management to defense mission systems and orbital operations, and across the full range of AI technologies from automation to fully autonomous learning systems, we focus on three areas.

Organizational Readiness

Business strategy, product roadmap and development process aligned, with the right people in the right roles and clear expectations.

Machine Intelligence Strategy & Architecture

Where AI belongs in the system and how much authority it holds, with architectures that expand human capability while preserving situational awareness and responsibility.

Mission Assurance

Evidence the system is safe and ready to operate, with traceability, explainability and the right balance of deterministic and probabilistic validation.

Explore our reference architecture

Portrait of Michael Zyskowski
Demonstrated Leadership

Over thirty years building aircraft, software, and autonomous systems.

Aero Intelligence is led by Michael “Z” Zyskowski, an aerospace engineer who has taken AI-driven systems from research to certified, fielded operation, including FAA Part 135 drone delivery, on-orbit autonomous space defense systems, and fully driverless passenger-carrying automobile fleets.

Proven track record: five AI systems taken from zero to fielded operation in ten years, including FAA Part 135 certification.
  • Wing / Google X: Autonomous drone delivery, from research to FAA Part 135 certification and 100,000+ delivery flights.
  • Archer Aviation: ZEE aviation AI foundation model platform and Archer Aviator AI pilot assistant.
  • True Anomaly: Autonomous orbital operations software platform Mosaic successfully used on orbit.
  • Cruise: Simulation-first validation for the Origin driverless vehicle, and 10M+ simulated miles supporting expansion to 20+ US cities for the GM Cruise autonomous Chevrolet Bolt EV.
  • Microsoft / Studio Alpha: AI powered defense simulation platform delivered to USMC, USSF, NASA and USINDOPACOM.

Across these programs: $100M+ in contract awards. Before that: Microsoft Flight Simulator and Lockheed Martin Prepar3D high-fidelity, real-time simulation; 747/767 aerodynamics, stability and control, flight test and accident investigation at Boeing; and NASA Langley research on small aircraft transportation systems.

Read the full background
Proven Experience
What We Do

Three interconnected challenges. One system.

Our work addresses three interconnected challenges these programs must solve:

  • Product Development is usually where issues surface first: missed deadlines, unexpected consequences and lack of operational confidence.
  • Design of the system, where the human-machine interface and component modularity are not adequately addressed.
  • Validation of the system, where insufficient attention is given to quality assurance and authorization strategy.

These areas must iterate together around shared features, evidence and risk backlogs, allowing the program to reach operational readiness sooner and better matched to what customers expect.

What business, organization and development strategy are required?

Product Development

Business · People · Process

We align business and product-development strategy, engineering capabilities, decision structures, organizational practices, and machine-learning workflows with what the technology and mission require, with clear roles, ownership and accountability.

Why it matters

Integrating machine intelligence into mission-critical systems changes more than the technology. Organizations may need different engineering capabilities, decision structures, development practices, data and simulation capabilities, and cross-functional relationships to successfully develop, approve, field, and sustain increasingly intelligent systems. When goals are misaligned, ownership has gaps or the process no longer fits, the result is unclear accountability, missing capabilities and missed deadlines.

Typical deliverables
  • Organizational and capability assessment
  • Decision rights and accountability matrix
  • Product strategy, PRD and roadmap
Return on investmentClear ownership and one plan the whole team can work to. Fewer missed deadlines, less rework and faster decisions.
How should the system be designed, and where should authority sit?

Design

Technical & System Architecture

We assess mission and operational needs, define consequential system-level decisions, and develop practical approaches to AI integration, including where authority sits between people and AI, human-machine teaming, versioned interfaces, graceful degradation, and modular architecture.

Why it matters

AI and autonomy create consequential architecture decisions. The system must account for how AI participates in perception, reasoning, recommendation, decision-making, and action; how humans maintain appropriate situational awareness, authority, and responsibility; and how legacy and emerging technologies work together in concert. With the technology changing quickly, teams also need a shared terminology, a clear definition of the problem they are solving and a plan they can trust.

Typical deliverables
  • AI and human authority map
  • Machine-intelligence integration strategy
  • System and software modularity plan
Return on investmentIntentional architecture decisions for modularity and explainability. Avoids late redesign, enables robust evaluation and allows software change without rebuilding the system.
How do we establish confidence that it will operate safely and effectively, and when is it ready to deploy?

Validation

Evaluation & Quality Assurance

We establish how the system will be evaluated, validated and authorized for mission use, including the evidence that shows when it is ready to deploy, then translate the resulting strategy and architecture into an executable program with clear priorities, risks, dependencies, decisions, and accountability.

Why it matters

As systems become more intelligent and autonomous, establishing confidence in their behavior becomes a central program challenge. Legacy qualification approaches were not built for learned behavior, and the edge cases that matter are often hidden. Validation must address not only component performance, but system behavior, human-machine interaction, operational scenarios, edge cases, and the evidence needed to gain authority to operate. Operational readiness must also account for how the people who use, supervise, and interact with these systems are trained to understand their capabilities, limitations, and expected behaviors.

Typical deliverables
  • Compliance and authorization strategy
  • System validation strategy
  • Operational confidence and sustainability plan
Return on investmentA credible path to release and authorization. Define the metrics, surface issues early, build a culture of safety.
Outcome: an AI-Powered Aerospace System that achieves mission readiness on time, with confidence at lower cost.

See how we invert the System V

How We Work

Engagement options.

Every engagement starts with a 30-minute program review. From there, choose the level of support that fits the program.

01

Program Review

30 minutes · no cost

A conversation about your program, what’s at risk and whether there is a fit.

02

Readiness Assessment

Fixed scope · fixed price

Specific focus questions agreed up front, targeted team interviews and a review of existing artifacts, resulting in a report with a scorecard, trade-offs and a recommended course of action.

03

Defined Deliverables

Fixed scope

Fixed scope and price for a specific, near-term need, such as a product roadmap, AI authority map or a compliance strategy.

04
Core engagement

Monthly Advisory

Typically 3–6 months

A standing seat with the leadership team plus hands-on working sessions with your engineers. The most flexible option and the best value.

05

Fractional Head of Product

Embedded

An embedded leader who sees it through to operational and mission readiness.

Inverting the System V

Design and validation run together, not in sequence.

Our approach adapts the classic systems engineering V model to better support software- and AI-driven development.

The Classic System VRequirements are defined at the top left and decomposed down into system and detailed design, components are built at the bottom, then integrated, verified and validated up the right side. Validation arrives last.RequirementsSystem designDetailed designBuild componentsIntegrationVerificationValidationDefine and decomposeIntegrate, verify, validateEvidence arrives last
Fig. 1 · The Classic System V

Traditional aerospace programs rely on the systems engineering V model: define requirements at the top, decompose them into a detailed design down the left side, build components at the bottom, and then integrate, verify, and validate up the right side. This structured model works well when system behavior can be fully specified up front and verified against explicit requirements, forming the foundation of current safety-critical software certification standards.

AI and autonomous capabilities challenge these fundamental assumptions
  • Behavior is learned from data: System behavior cannot be fully specified in requirements alone; it must be demonstrated empirically across the entire operational domain rather than traced line by line.
  • Validation occurs too late: Because validation sits at the end of the V, issues related to machine-learning performance, training data, or human-machine interaction surface late in integration when corrections are most costly.
  • Systems evolve continuously: Models, data pipelines, and software continue to adapt after deployment, whereas the classic V assumes a static, frozen design.
  • Ownership gaps create risk: Unclear boundaries and responsibilities lead to unaligned assumptions, missed dependencies, and costly rework.

To address these challenges, we invert the V.

Product development leads

Organizational alignment, engineering processes, and team capability sit at the top, enabling teams to identify risks early and collaborate effectively.

Parallel design and validation

Design and validation run concurrently in continuous iterative loops, allowing teams to evaluate results in real time and adapt architecture as goals evolve.

Faster time to market

Addressing architectural and organizational decisions early reduces downstream friction and significantly accelerates delivery.

The Inverted System VProduct Development sits at the top, with Design on the left and Validation on the right. Each starts with the problem and its impact, then shows the approach: align people, processes and priorities; architect adaptable, reliable intelligent systems; and prove system safety, performance and readiness. Design traces back to the decisions and assumptions creating risk; Validation proves forward with the evidence needed for safety and mission readiness. Design and Validation iterate around a shared feature, evidence and risk backlog using deterministic and probabilistic methods, leading to Operational Readiness and Mission Readiness through continuous release cycles, and a mission-ready intelligent aerospace system.Product DevelopmentBUSINESS · PEOPLE · PROCESSDesignTECHNICAL & SYSTEM ARCHITECTUREValidationEVALUATION & QUALITY ASSURANCEProblem: Organizations aren’t structuredto deliver intelligent systems.Impact: Missed deadlines, increased costs,and unpredictable outcomes.Problem: Traditional architecturesaren’t designed for machineintelligence.Impact: Integration challenges, safetyrisks, and costly redesign.Problem: Existing methods can’t adequatelyvalidate intelligent behavior.Impact: Delayed approvals, increased costs,and postponed deployment.Approach: Align people, processes, andpriorities. Accelerate decisions,reduce rework, and deliver predictably.Approach: Architect adaptable, reliableintelligent systems. Reduceintegration risk, avoid redesign,and accelerate development.Approach: Prove system safety, performance,and readiness. Identify risks earlyand accelerate operational approval.TRACE BACKWhich decisions and assumptionsare creating risk and delayingprogress?PROVE FORWARDWhat evidence is needed todemonstrate safety and missionreadiness?FEATURE, EVIDENCE& RISK BACKLOGOperational ReadinessCONOPS & PRODUCT-MARKET FITRegulatory authority acceptanceConOps integration at scaleProduct-market fit establishedMission ReadinessMISSION & SAFETY ASSURANCESafety assurance evidence acceptedField deployed and tested; shadow opsOperators trained; go-live criteria metDETERMINISTICClassic validation methodsPROBABILISTICTrain, evaluate, monitor, updateContinuous Integration,Development and ValidationContinuous Release CyclesMission-Ready Intelligent Aerospace SystemService offeringOutcome
Fig. 2 · The Inverted System V
Reference Architecture

Augmented human intelligence, with safety isolated by design.

This is how we frame an intelligent aerospace system. Machine intelligence extracts knowledge, assesses, and proposes. People keep authority. The certified control loop stays deterministic and never depends on AI for safety.

Fig. 1 · Augmented Human Intelligence system loopMachine intelligenceHuman authorityAuthorizationCertified control
Mission-critical loop

AI proposes. It does not command.

Perception and decision support turn sensor data into knowledge, then deliver insight and recommendations to the operator and proposals to the authority gate.

Human authority

People approve, modify, or reject.

The operator holds decision authority at the gate and can monitor and override the control loop directly.

Safety-critical loop

Control stays deterministic.

Command guard, control laws, and safety-critical sensors remain isolated and certifiable. AI sees vehicle state read-only.

Only human-authorized instructions cross into the control loop. The control loop never depends on AI for safety.

Portrait of Michael Zyskowski
Founder & Principal

Michael “Z” Zyskowski

Aerospace engineer and product leader with a proven track record: five AI systems taken from zero to fielded operation in ten years, including FAA Part 135 certification, across autonomous aviation, autonomous vehicles, robotics, and space defense. His career started in the wind tunnel and flight test at Boeing and NASA.

100,000+autonomous delivery flights leading to FAA Part 135 certification
10M+simulated miles validating driverless autonomy, supporting expansion to 20+ US cities
$100M+contract awards across USMC, USINDOPACOM, USSF, and NASA
30+ yrsin aerospace and software, from 747 stability and control to AI copilots
Background

An engineer’s view of the whole program.

Z has worked every layer of an intelligent aerospace system. At Boeing he did stability and control work on the 747 and 767, ran an upset-recovery flight test program, and supported major accident investigations. At Microsoft he built the flight model and physics engine for Flight Simulator, then launched the ESP simulation platform for military and training customers, which became Lockheed Martin’s Prepar3D.

Over the last decade he has led the programs where AI meets certification and operations: Wing’s autonomous drone delivery at Google X, simulation-based validation of the Origin driverless vehicle at Cruise, Mosaic autonomous orbital operations software at True Anomaly, and the ZEE aviation foundation model and Archer Aviator pilot software at Archer Aviation.

That range is the reason Aero Intelligence works at the system level. Architecture, validation, organization, and program execution are problems he has had to solve together, on schedules that mattered.

Career

Selected programs

2026

Archer Aviation

Senior Product Director

Established ZEE, Archer’s AI foundation model purpose-built for aviation: a unified aviation intelligence platform built on ADS-B, ATC communications, maps and charts, aircraft state, terrain, and weather data. Built Archer Aviator, the first product on ZEE: AI software for pilots and eVTOL operators supporting navigation, flight planning, aeronautical decision-making, flight operations, and copilot assistance. Led a cross-domain team of AI researchers, engineers, and operational test staff.

2025 – 2026

True Anomaly

Senior Product Director, Mosaic

Led Mosaic, space-defense software for autonomous orbital operations, used in non-cooperative rendezvous and proximity operations supporting the Victus Haze program. Shaped the architecture and winning proposal for the Golden Dome Space-Based Interceptor program award.

2024 – 2025

Scaled Foundations / General Robotics

Director of Partner Development

Took the GRID robotics intelligence development platform to market through OEM partnerships, including live defense-industry demonstrations integrating robotic hardware and fielded sensor systems.

2023 – 2024

Cruise

Lead Technical Product Manager, Simulation

Owned simulation products that evaluated driverless-vehicle safety, reliability, and performance, with simulation as a primary verification and validation method for Origin, a purpose-built driverless vehicle, and 10M+ simulated miles supporting expansion to 20+ US cities for the GM Cruise autonomous Chevrolet Bolt EV.

2020 – 2022

Microsoft, Studio Alpha

Technical Director

Took an internal startup from concept to product-market fit, with contracts across the US Marine Corps, US Space Force, NASA, and USINDOPACOM. Owned architecture for secure data integration, distributed simulation, GIS, and real-time 3D.

2016 – 2020

Google X · Wing

Lead Technical Product Manager

Led Wing’s autonomous drone delivery and airspace management system from research incubation to FAA Part 135 certification and operations in multiple countries. Supported NASA UTM flight tests that informed BVLOS rulemaking.

2000 – 2016

Microsoft Flight Simulator & Microsoft Research

Engineer, Lead TPM, Engineering Director

Built the core flight model and physics engine, launched the ESP government simulation platform for military and training customers, and led the Lockheed Martin technology license that created Prepar3D. At Microsoft Research, led applied research programs with industry and academic partners.

1995 – 2000

Boeing & NASA

Aerodynamics Engineer · Aerospace Engineer

Stability and control for the 747-500X/600X, flap and winglet aerodynamics for the 767-300ER, and upset-recovery flight test. Accident investigation for TWA 800 and USAir 427. NASA research on the SATS and AGATE general aviation programs.

Experience Behind the Work

Where this background shows up in an engagement.

Design

Autonomy and human-machine architectures fielded in real operations.

  • Wing autonomous delivery and airspace system
  • Mosaic autonomous orbital operations
  • Archer ZEE foundation model and Archer Aviator
  • Flight dynamics and simulation engines

Product Development

Cross-domain teams organized to ship, from startup to enterprise scale.

  • Wing spin-out to an Alphabet company
  • Cross-domain AI and engineering teams
  • OEM partner and SDK ecosystems
  • Regular, milestone-driven release cycles

Validation

Evidence that regulators, customers, and boards accepted.

  • FAA Part 135 certification
  • Simulation-first V&V for Cruise Origin
  • Flight test and accident investigation
  • Payment-linked government milestones
Credentials

Education

  • M.S. Aerospace Engineering, University of Kansas, 1996 (GPA 4.0)
  • B.S. Aerospace Engineering, University of Kansas, 1994

Patents

  • Integrated Aircraft Flight Dynamics Prediction and Simulation (2002)
  • Automatic Longitudinal Pitch Trim in Air Combat Simulation (2003)
  • Multi-level Image Viewing (2010)

Selected publications

  • Convergence: Commodity Flight Simulation and the Future, The Aeronautical Journal (2008)
  • Extensible Vehicle Physics in the Microsoft ESP Visual Simulation Platform, RAeS (2008)
  • ChronoZoom, ACM SIGITE (2013), plus five AIAA/NASA/TRB papers
Start a Conversation

From demo to fielded and proven.

If your AI-enabled program needs to get from a promising demo to a fielded system that your team, customer and regulator will trust, we identify the business, organizational, technical and validation gaps, deliver a plan to close them, and work alongside your team to execute it.

Start with a 30-minute program review.

Bring us the problem. We’ll discuss the program, what’s at risk and where the challenges appear to be. The goal is simple: determine whether Aero Intelligence is the right fit and the next step to getting started.

Schedule a 30-Minute Program ReviewEmail z@aero-intelligence.ai