Modern software development moves fast. Updates roll out seamlessly, with new features and fixes integrated through continuous testing and delivery. Even something as trivial as an emoji goes through a rigorous validation process before appearing on your phone. Meanwhile, the hardware that powers cars, rockets, and industrial systems remains trapped in a slow, outdated development cycle.
This isn't just an inconvenience—it’s a fundamental flaw in how we build and test mission-critical machines. The result? Projects that drag on for years, budgets that balloon into billions, and failures that could have been avoided.
Hardware development is still stuck in outdated testing cycles, while software moves at an agile pace.
Testing Like You Fly
Traditional hardware development follows a rigid, linear process. Engineers design, test, and review in long cycles—sometimes delaying real-world validation until it's too late. This approach is like designing a car entirely on paper, building it in full, and only then taking it for a test drive.
The “testing like you fly” philosophy changes this. Instead of waiting until late in development to validate assumptions, engineers use the same test infrastructure across every stage—simulation, hardware testing, and live operations. Each phase feeds into the next, creating a continuous cycle of improvement.
This method ensures that:
- Data review is consistent across all phases, reducing blind spots between testing environments.
- Operational knowledge is integrated early, preventing costly redesigns after deployment.
- Failures happen in controlled conditions, not in the field, minimizing risk and improving safety.
Testing like you fly creates a continuous feedback loop, making every test more relevant and effective.
The Payoff of Continuous Testing
A fast, iterative testing model offers clear benefits: faster development cycles and fewer late-stage surprises. But the real impact runs deeper. When testing becomes routine and automated, teams test more often. When they test more often, they collect better data. When they collect better data, their simulations become more accurate. And when simulations improve, so does every subsequent test.
This feedback loop compounds over time, enabling:
- Faster iteration cycles through automated data review.
- Earlier anomaly detection before issues escalate.
- More advanced testing frameworks, where machine learning can surface hidden risks instead of relying on static thresholds.
Sift: Infrastructure and Apps Powering Continuous Testing
Shifting to continuous testing at scale requires more than a new version of an old tool—it demands a platform that eliminates fragmentation and ensures data remains structured, queryable, and actionable across the entire lifecycle.
Sift isn’t just an application for data review. It is a full-stack observability platform built for hardware, integrating ingestion, storage, visualization, and validation into a single system. Unlike traditional tools that bolt on analytics after the fact, Sift ensures data is structured, enriched, and searchable from the moment it is collected.
With Sift, engineering teams gain:
- A scalable infrastructure optimized for high-frequency, high-cardinality telemetry—removing bottlenecks that slow down iteration.
- A single source of truth that connects simulation, ground tests, and live data.
- Automated anomaly detection that surfaces issues before they become failures.
- Tooling for root cause analysis, enabling teams to diagnose and resolve anomalies rapidly—minimizing downtime and accelerating validation cycles.
- An evolving knowledge base, where institutional expertise is captured in-line with the data—ensuring insights remain current, actionable, and directly tied to real-world testing.
Sift doesn’t just accelerate testing—it fundamentally changes how hardware teams build, validate, and scale complex machines.
The companies that merge software’s agility with hardware’s complexity will define the future.
“Test Like You Fly” Is the Foundation of Continuous Validation
Testing isn’t a phase—it’s an ongoing, integrated process that spans simulation, ground testing, and live operations. But the shift to continuous validation doesn’t make “test like you fly” obsolete—it makes it more critical than ever.
“Test continuously” only works if the data pipeline is structured, actionable, and aligned with real-world performance. The real danger isn’t sticking to outdated test models—it’s rushing to continuous testing without a unified observability layer. Testing without structure isn’t progress—it’s noise.
Sift enables continuous validation without sacrificing mission fidelity. Every piece of data—whether from simulation, testbed, or deployed operations—feeds into a single, structured source of truth. This ensures that:
- Every test has context. Testing without an operational benchmark is meaningless. “Test like you fly” provides that benchmark, making sure that early-stage tests align with real-world conditions.
- Failures happen in controlled conditions, not in the field. Testing isn’t just about speed—it’s about surfacing issues before they become operational failures.
- Scaling test requires structured data, not just more test points. A satellite constellation doesn’t benefit from endless unstructured telemetry—it needs a system that makes sense of it.
Infrastructure, Not More Tools, Is the Answer
The implications of this shift extend far beyond aerospace. Imagine cars receiving software updates as easily as your phone. Imagine energy infrastructure adapting in real time to maximize efficiency. Imagine hardware development cycles measured in months, not years.
Bringing software’s agility to hardware requires more than new tools—it demands a mindset shift.
The future of test isn’t about bolting new dashboards onto old infrastructure. It’s about replacing fragmented, outdated tooling with a modern infrastructure that can scale. Companies don’t need another visualization or anomaly detection tool—they need a platform that unifies data from ingestion to insight.
Sift’s infrastructure is purpose-built for this challenge. Unlike legacy monitoring solutions that collect data without structure, Sift ingests, normalizes, and organizes telemetry at scale, ensuring that engineers have the right data at the right time.
- Tools come and go, but infrastructure is what enables rapid iteration at scale.
- More test data doesn’t mean better test data—Sift ensures every test contributes to mission success.
- The companies that win will be the ones that invest in infrastructure, not just tools.
Sift doesn’t just enable more testing—it ensures that every test is actionable, high-fidelity, and mission-driven.
