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Sift helps teams working on complex machines move from raw telemetry to confident decisions faster. Sift is designed for users who need to make sense of high-frequency data from rockets, aircraft, robots, manufacturing systems, and other hardware platforms. Instead of starting from architecture, Sift starts from the work you need to do:
  • Connect your test or mission data
  • Explore what happened
  • Validate the results
  • Share findings with the people who need them.
For teams working on high-value hardware, the hardest part is often not that the data exists but that the data is scattered, noisy, and hard to connect. Sift is built to help you:
  • Get the right telemetry in front of you
  • Follow the story inside your data
  • Make and share decisions with confidence
  • Move from disorder to traceable insight
This is the user workflow that defines how Sift supports your work every day.

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Common workflows

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1. Understand a test run or mission quickly

When you load a run, Sift presents the data in ways that match your workflow:
  • View the same channels and signals that matter to your system
  • Overlay sensor traces and events to see correlations
  • Jump from summary views into the exact time window where something changed
This makes it easier to answer questions like:
  • “Why did this channel spike at this point?”
  • “Did the anomaly happen before or after the subsystem reboot?”
  • “How did this run compare to the previous one?”

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2. Investigate issues across assets and subsystems

Sift helps you trace problems through multiple sources of truth:
  • Compare runs side by side
  • Search for the same failure patterns across tests
  • Link alarms, anomalies, and rule results back to the data that caused them
The goal is to keep your focus on the investigation, not on managing separate tools.

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3. Review and validate results with stakeholders

Your review workflow can include:
  • Automated checks and rule-based review for common issues
  • Human review for exceptions, anomalies, and unexpected behavior
  • Shared views so engineers, analysts, and managers see the same timeline
This is especially useful when you need to confirm that a flight test, production campaign, or system checkout is ready to move forward.

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4. Share findings and keep work repeatable

Once you’ve found the answer, Sift makes it easier to:
  • Share a link to the same data view
  • Preserve the context and filters used during the investigation
  • Reuse the same analysis patterns on future tests
That means your team can work consistently and avoid repeating the same setup steps.

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Platform

Sift is composed of three architectural layers: infrastructure, applications, and governance (designed to work independently or together, depending on your needs). These layers are modular and support flexible deployment models, including managed SaaS, hybrid (on-prem compute with cloud storage), or fully on-premise for classified environments.

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Infrastructure layer

Purpose-built ingestion and storage system for structured hardware telemetry.
  • Ingestion: Real-time ingestion of structured (Protobuf, Influx, etc.) and unstructured (logs, video, etc.) data from test stands, flight systems, or CI pipelines.
  • Streaming stateful analysis: Processes data on the fly using configurable rules and statistical operators, enabling anomaly detection, filtering, and derived signal computation.
  • Managed storage: High-throughput, object-based storage optimized for high-cardinality datasets. Supports schema evolution and time-aligned access.

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Application layer

User-facing tools for analysis, review, and collaboration.
  • Root cause analysis: Compare test runs, inspect anomalies, correlate data across subsystems, no code required.
  • Data review: Run automated data checks using rule-based review pipelines, customizable by subsystem or mission phase.
  • Visualization and dashboards: Build plots and timelines across channels, overlay data, and share via links, integrated or using tools like Grafana.
  • Data-driven manufacturing: Capture lineage and test context at the part or component level, supporting traceability and regulatory compliance.

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Governance layer

Controls system-level behavior, access, and performance.
  • Role-based access control (RBAC): Granular user and group-based permissions across assets, data, and features.
  • Query optimization and load balancing: Manages query workloads to ensure stability during peak usage or live operations.
  • Agentic interfaces (planned): Adds support for future LLM-based interfaces that operate on versioned, explainable metadata (for example, genealogy, dimensions).