What is Sift?
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.
Platform
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.
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.
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).
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.