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Overview

Sift is a unified observability platform built for teams developing and operating complex hardware systems. It provides end-to-end infrastructure and tooling to ingest, store, analyze, and collaborate on high-frequency telemetry and event data across the full machine lifecycle. Where traditional observability tools are built for IT metrics or software logs, Sift is designed from first principles for modern machines: rockets, aircraft, autonomous vehicles, robotics platforms, and large-scale manufacturing systems. It supports diverse, high-cardinality data types, aligns telemetry across subsystems, and structures information for fast, reliable search, review, and traceability.

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Platform

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Overview

Sift is composed of three architectural layers: infrastructure, governance, and applications—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 (for example, Protobuf, Influx) and unstructured (for example, logs, video) 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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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).

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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.