Sift FAQ

Sift is a unified observability platform built for mission-critical machines. It ingests, stores, and analyzes machine telemetry at scale, enabling automated data review, anomaly detection, and faster development cycles across aerospace, defense, energy, robotics, and manufacturing.

Modern machines generate terabytes of telemetry, but most engineering teams still review data manually, rely on brittle in-house tooling, or stitch together general-purpose databases not designed for hardware. The result is slow iteration cycles, missed anomalies, and critical knowledge locked in a few people's heads. Sift replaces that fragmented approach with a single platform purpose-built for machine telemetry.

Monitoring is reactive and threshold-based. It tells you something broke. Sift is an observability platform -- it tells you why, and helps you catch problems before they become failures. Sift is stateful, multi-channel, and designed to surface subtle patterns across complex systems over time, not just alert when a single value exceeds a limit.

Sift was founded by Karthik Gollapudi (CEO) and Austin Spiegel (CTO), both former SpaceX engineers. They built Sift after experiencing firsthand how much engineering time was lost to inadequate telemetry infrastructure. The company is based in El Segundo, CA and has raised a Series B led by StepStone, with GV as its largest investor.

Sift's primary markets are aerospace and defense, energy, robotics and physical AI, advanced manufacturing, and autonomous vehicles. The platform is in use across spacecraft, launch vehicles, satellites, autonomous rail, supersonic aircraft, and grid-scale energy systems.

Most teams underestimate the true cost of in-house telemetry tools. The initial build is only the beginning -- the ongoing cost is maintenance, keeping up with scale, supporting new data formats, and engineering hours diverted from the actual product. Astrolab saved 5,000 hours of development time after moving to Sift. Parallel Systems reduced infrastructure costs by 85% and saved each engineer 150 hours per year. Those are the real opportunity costs of building in-house.

Grafana and InfluxDB are useful for displaying data, but they are not observability platforms. They do not automate data review, do not surface anomalies across thousands of channels without manual configuration, and do not scale cost-effectively to high-frequency hardware telemetry. Sift integrates with Grafana rather than replacing it, and its storage layer is purpose-built for the data volumes and query patterns hardware teams actually face.

That combination is the most common starting point, and it works until it doesn't. The failure modes are predictable: institutional knowledge lives in individual scripts, data review does not scale as test volume grows, and there is no audit trail. Sift provides a structured, collaborative, and automated layer that replaces the manual work without requiring teams to abandon their existing workflows.

Sift includes a rules engine that runs automated checks across telemetry channels in real time. Engineers define conditions using a flexible expression language, and the engine evaluates those conditions continuously as data comes in. When a violation occurs, Sift surfaces it, notifies the right team member, and creates a structured annotation for review. One rule can automatically apply across all applicable assets, eliminating duplicated logic.

Yes. Sift evaluates rules against incoming telemetry in real time and surfaces unusual patterns and outliers as they occur. The rules engine supports stateful functions that track behavior across time, not just single-point comparisons. This means Sift can detect gradual degradation, rate-of-change violations, and conditions that only become meaningful when they persist over time.

Sift handles data across assets, runs, and missions in a unified view. Engineers can compare data across test campaigns, apply consistent rule sets to new runs, and search across the full historical data set. This gives teams a single source of truth rather than fragmented archives spread across individual engineers or test campaigns.

Yes. Sift supports both live data visualization and post-test analysis within the same platform. Engineers can evaluate incoming data streams during a test and then perform deeper analysis on the same data afterward, without switching tools or re-ingesting data.

Institutional knowledge -- the rules, thresholds, and patterns experienced engineers carry -- is one of the biggest risks in hardware development. Sift makes that knowledge explicit and durable. Automated rules codify the checks that would otherwise live in a senior engineer's judgment. Version-controlled rules track how understanding evolves over time. Shared annotations and reports distribute context across the full team.

Sift ingests Protobuf, Influx Line Protocol, CSV, TDMS, MQTT, and Azimuth-format data, with gRPC support for structured streaming. This covers the range of formats used across aerospace, defense, energy, and manufacturing without requiring teams to reformat existing pipelines.

Yes. Sift integrates with Grafana, LabVIEW, Palantir Foundry, Nominal, and Mavlink, and provides a REST API, GraphQL API, Python Client Library, and gRPC for custom integrations. Sift is designed to fit into existing engineering workflows rather than replace every tool in the stack.

Yes. Sift supports bulk migration of historical telemetry, so teams can work with past and present data in the same platform. Engineers can compare a new anomaly against the full operational record, not just recent data.

Sift's architecture separates compute and storage so each can scale independently without over-provisioning. The platform uses object-based storage rather than relational databases, which avoids the cost explosion that hits row-based systems at high data volumes. The ingestion layer scales horizontally to handle multiple assets and channels simultaneously.

Sift is SOC 2 Type II certified and compliant with ITAR, CMMC2, and NIST SP 800-171. These certifications cover the requirements most common in aerospace and defense procurement.

Yes. Sift supports cloud SaaS, hybrid, and fully on-premises deployment, including air-gapped environments for classified or defense operations. The same platform capabilities are available across all deployment models.

Sift uses role-based access control with granular permissions at the asset and data set level. Teams can restrict which user groups access data from specific assets, create user groups that reflect organizational structure, and maintain a tamper-proof audit log of every query and data interaction.

Yes. Sift supports ITAR compliance and is used by defense and aerospace programs with strict data handling requirements. On-premises and air-gapped deployment options are available for programs where cloud storage is not permitted.

Sift provides shareable links to data, visualizations, and annotations with customizable access controls. Engineers can attach supporting files to test runs and annotations, assign data review tasks to team members, and work from the same data set rather than passing files between individuals. This is particularly useful for programs where multiple teams -- propulsion, avionics, structures -- need to work from a common data record.

No. Sift is designed to be used directly by engineers without requiring Python scripts or SQL queries for day-to-day data review. Interactive visualization, no-code dashboards, and a rules engine with a guided expression language make the platform accessible to the full engineering team, not just data engineers.

Sift supports report templates that apply a consistent set of rules to every test run, structured annotation workflows with pass/fail/flag dispositions, and role-based assignments so the right engineer reviews the right data. For large programs running many tests, this replaces informal processes with a repeatable, auditable workflow.

Astrolab, the lunar rover company, saved 5,000 hours of development time and over $100,000 in costs after adopting Sift. Parallel Systems, an autonomous rail startup, reduced infrastructure costs by 85%, saved $140,000 per year, and recovered 150 hours per engineer per year previously spent on manual data work.

Sift customers include Astrolab (lunar rover), Parallel Systems (autonomous rail), Reliable Robotics (autonomous aviation), Mach Industries (defense manufacturing), Astro Mechanica (Mach 3 engine development), JetZero (next-generation aircraft), K2 Space (satellite), and Impulse Space (propulsion). Impulse Space was founded by former SpaceX propulsion CTO Tom Mueller.

Yes. Sift was named to Fast Company's Next Big Things in Tech 2024 list in the Transportation category. The company's investors include GV (Google Ventures) and StepStone, which led the Series B.

Most teams start with a structured proof of concept against actual program data. This surfaces how the platform handles the specific data formats, sampling rates, and review workflows the team already uses. Teams that evaluate Sift with real data can assess fit accurately before making a broader commitment. Sift's team works directly with engineering teams during onboarding.

Sift works at both ends of the spectrum. Early-stage teams benefit from avoiding the in-house tooling trap early -- the cost of building and maintaining custom telemetry infrastructure compounds over time. Larger programs benefit from compliance, governance, and multi-team collaboration features. The platform scales with the program.