In hardware development, the phrase "test like you fly" has long been a guiding principle—and for good reason. Born from decades of hard-won lessons in aerospace and space system development, "Test Like You Fly" (TLYF) emerged not from theory, but from failure. Post-mortem analyses of high-profile mission losses repeatedly traced root causes to insufficiently realistic testing: machines encountering flight conditions for the first time in flight.
In response, the industry codified TLYF into a formal, risk-informed discipline. It became a cornerstone of mission assurance, built on operational realism, end-to-end integration, and the belief that complex hardware must be tested under the same conditions in which they are expected to operate. The message was clear: If you want to fly with confidence, you must test accordingly.
But realism alone is not enough. The demands on hardware teams are shifting because the machines they build are fundamentally changing. Systems today are increasingly software-defined, autonomous, and continuously evolving. It's no longer enough to validate against static scenarios—teams must continuously assess performance in real time as software, environments, and operational conditions shift.
The question isn’t just how accurately you replicate real-world conditions—but how often. How quickly. How traceably. That's where CI/CD enters the equation. And that's where Sift makes it real.
Dashboards Don’t Deliver Trust
The industry has embraced the language of CI/CD. "Test continuously." "Push to deploy." "Treat anomalies like exceptions." These phrases resonate with engineers working to modernize their development cycles. But beneath that shared ambition, teams often find themselves constrained by fragmented telemetry stacks, inconsistent data access, and tools that struggle to keep up with the scale and speed of modern machines.
To make confident decisions, engineers need more than visibility—they need a foundation of structured, trustworthy data.
You can’t debug with dashboards alone. You can’t trace causality across a fleet using checklists and screenshot annotations. What’s needed is infrastructure that treats validation as a first-class operation—one that begins at ingestion, reasons in real time, and supports the full hardware lifecycle.
CI/CD for hardware isn’t a metaphor. It’s a system.
What "Test Like You Fly" Actually Means Today
At Sift, "test like you fly" isn’t nostalgia—it’s forward-compatible. It's a foundation that today's CI/CD strategies should build upon, not replace.
CI begins at design, where engineers encode validation rules that define how subsystems should behave. Those rules are evaluated continuously as data streams in from lab, line, or flight. And when something breaks, the same system provides real-time diagnosis, historical comparison, and anomaly context in one unified environment.
Test like you fly means:
- Defining rules at design-time
- Validating telemetry at run-time
- Compounding on insights during post-processing
This is continuous integration for hardware—not just automated testing, but full-lifecycle validation. And what about deployment? In hardware, CD isn’t about binaries. It’s about feedback. Every result from manufacturing or field ops flows back into development, closing the loop without waiting for a quarterly review or manually correlating logs.
In practice, test realism and test cadence are complementary. One ensures your hardware is validated in operational conditions. The other ensures you're learning fast enough to keep up with complexity. The most capable teams do both.
How Manufacturo and Sift Enable Continuous Validation Across the Lifecycle
Manufacturo plays a critical role in the modern hardware lifecycle by enabling execution speed and operational agility—especially at the manufacturing and deployment stages. As a partner, their cloud-native manufacturing management system complements Sift’s observability layer by maintaining continuity between what gets validated during development and what ultimately gets built on the factory floor - through integrated traceability, version-controlled execution, and connected quality workflows.
Teams start by defining rules and expectations in early hardware development—assertions about system behavior, tolerances, and failure conditions. With Sift, those validations are continuously applied to real-time telemetry and test data. With Manufacturo, those insights inform execution ensuring the hardware built matches the hardware tested.
The result:
- Ship faster by streamlining the path from validated design to manufactured product
- Reduce rework by catching misalignments and deviations earlier in the lifecycle
- Increase quality by maintaining validation context through to execution
This isn’t just “continuous testing.” It’s coordinated, continuous validation—spanning development, test, manufacturing, and field operations. When observability and execution work together, CI/CD becomes more than a software metaphor. It becomes real.
Read more on their blog.
When observability and execution work together, CI/CD becomes more than a software metaphor. It becomes real.
The Real Differentiator: Continuous Reasoning
Visibility alone isn’t enough. Understanding comes from context, from correlation, and from reasoning over time. That’s what makes Sift different.
- Streaming rules that hold on to state—like unit tests for the physical world—so you can validate behavior over time, not just detect one-off spikes.
- Run-over-run comparisons that surface regressions without manual diffing.
- Calculated channels that let engineers derive context-rich signals (e.g., thrust margin, power offset) from raw telemetry—all in-browser, without needing Python or SQL.
- Asset-agnostic logic, so a rule written for one engine model can validate behavior across the entire fleet.
CI/CD for hardware isn’t about running more tests. It’s about understanding what’s happening—as it happens.
CI/CD That’s Built for Real-World Machines
Modern machines are not static. They are evolving, networked, and constantly generating data. Validation is no longer a phase. It’s a loop.
At Sift, we enable confidence by turning telemetry into a continuously validated source of truth. For teams working on hypersonic engines, autonomous vehicles, or orbital systems, speed is only valuable when paired with trust in the results. That’s why Sift treats every datapoint as part of a larger chain of validation. From ingest to insight, the infrastructure is designed to reflect how machines really behave—no black boxes, no post-hoc alignment, no annotation that lives in a silo.
The legacy of "Test Like You Fly" is one of rigor, realism, and responsibility. The future of testing builds on that legacy—with telemetry-native tools designed for machines without limits.
If you test like you fly, you need infrastructure that flies like you test.
