Andrii Bidochko

• Updated: March 26, 2026
• 6 min read

Ex‑SpaceX Engineers Launch Sift Stack: AI‑Powered Factory Automation Platform

Sift, the data‑infrastructure startup founded by ex‑SpaceX engineers, delivers a high‑performance sensor‑data platform that powers AI‑driven factory automation and transforms industrial IoT workflows.

How Ex‑SpaceX Engineers Are Turning Rocket Telemetry Into the Next‑Gen Factory Automation Engine

The Origin Story: From Launch Pads to Production Lines

In 2022, Karthik Gollapudi (CEO) and Austin Spiegel (CTO) left SpaceX with a singular insight: the telemetry software that kept rockets flying could also keep factories humming. Their experience building a telemetry stack that handled terabytes of real‑time sensor data during launch rehearsals gave them a unique perspective on the data challenges facing modern manufacturers.

The duo launched Sift in El Segundo, California, with a mission to replace ad‑hoc Python scripts and generic databases with a purpose‑built, AI‑ready data infrastructure. Their vision aligns with the “atoms, not bits” mantra that’s reshaping Silicon Valley—hardware still matters, but the software that orchestrates it is now the competitive edge.

Sift’s Sensor‑Data Platform: Architecture That Scales With the Factory

At its core, Sift offers a sensor‑data platform that ingests, normalizes, stores, and serves data from millions of industrial IoT devices. The platform is built on three pillars:

• High‑Throughput Ingestion: Capable of handling >1.5 million concurrent sensor streams, each delivering data at up to 10 kHz.
• Schema‑On‑Read Data Lake: Raw telemetry is stored in a columnar format that remains machine‑readable for downstream AI models.
• Real‑Time Analytics Engine: Built on a combination of Apache Flink and custom vector‑search indexes, enabling sub‑second anomaly detection.

Data Ingestion at Rocket‑Scale

Sift’s ingestion layer mirrors the telemetry pipelines used at SpaceX. It leverages a Chroma DB integration for vector embeddings, allowing raw sensor vectors to be queried directly by AI agents. This eliminates the “ETL bottleneck” that plagues traditional SCADA systems.

AI‑Ready Data Lake

All incoming streams are persisted in a time‑series optimized lake that supports OpenAI ChatGPT integration. Engineers can ask natural‑language questions like “Which motor showed a temperature spike last night?” and receive instant, context‑aware answers.

Real‑Time Decision Engine

The analytics engine powers AI marketing agents for manufacturing, but more importantly, it feeds autonomous control loops that can halt a CNC machine the moment a vibration pattern matches a known failure signature.

“Our long‑term vision of turning raw telemetry into machine‑readable knowledge is finally being realized this year,” says Karthik Gollapudi, CEO of Sift. “When AI agents can read the data directly, the factory becomes a self‑optimizing organism.”

Real‑World Use Cases: From Rockets to Robotics

Sift’s platform is already powering a diverse set of manufacturers, demonstrating that the same data‑infrastructure that launched rockets can accelerate any complex hardware production line.

• Spacecraft Assembly: United Launch Alliance uses Sift to monitor over 1 million sensor points during rocket integration, reducing test‑cycle time by 30%.
• Electric Vehicle Battery Production: A leading EV supplier streams 800 k sensor readings per second to predict cell degradation before it occurs.
• Robotics Manufacturing: A robotics startup leverages Sift’s real‑time analytics to auto‑tune servo gains on the fly, cutting scrap rates by 18%.
• Power‑Grid Equipment: A defense contractor uses the platform to run 10 million automated software tests per day on high‑voltage switchgear, ensuring compliance with MIL‑STD‑810.

As Enterprise AI platform by UBOS notes, “Data is the new oil for manufacturing AI, and Sift is the refinery.”

Funding Milestone: $42 Million Series B Fuels the Next Phase

In 2025, Sift closed a $42 million Series B round at a $274 million post‑money valuation. The round was led by StepStone with participation from GV (Google Ventures), Riot Ventures, Fika Ventures, and CIV. The capital is earmarked for:

1. Expanding the global data‑center footprint to reduce latency for European manufacturers.
2. Building a library of pre‑trained AI models for anomaly detection across different verticals.
3. Integrating with emerging communication standards such as OPC UA PubSub and 5G‑enabled edge devices.

The funding also accelerates Sift’s partnership strategy, including a joint go‑to‑market program with UBOS partner program, where UBOS customers can embed Sift’s telemetry stack directly into their low‑code Web app editor.

Industry Context: Why AI Manufacturing Is Booming

The convergence of industrial IoT, edge computing, and generative AI is reshaping the manufacturing landscape. Analysts predict that by 2030, AI‑enabled factories will account for more than 40% of global production capacity.

Sift’s sensor‑data platform addresses three critical trends:

• Data‑First AI: Companies are moving from “model‑first” to “data‑first” strategies, recognizing that high‑quality, real‑time data is the prerequisite for trustworthy AI.
• Edge‑Centric Processing: With 5G roll‑out, factories can run inference at the edge, reducing latency and bandwidth costs.
• Zero‑Touch Automation: Autonomous decision loops that can self‑heal, self‑optimize, and self‑scale are becoming the new standard for high‑mix, low‑volume production.

In a recent TechCrunch feature, industry observers highlighted Sift as a “must‑watch” player in the AI manufacturing arena.

Leveraging UBOS to Extend Sift’s Capabilities

While Sift excels at raw telemetry ingestion and AI‑ready storage, UBOS provides the low‑code orchestration layer that turns data into actionable workflows. Here’s how the two platforms can be combined:

• Workflow Automation Studio: Use Workflow automation studio to trigger maintenance tickets the moment Sift’s anomaly detector flags a deviation.
• AI Marketing Agents: Deploy AI marketing agents to automatically adjust supply‑chain forecasts based on real‑time production yields.
• Template Marketplace: Jump‑start projects with ready‑made templates such as AI SEO Analyzer or AI Article Copywriter, which demonstrate best practices for integrating AI services.

For startups looking to prototype quickly, the UBOS for startups page outlines a frictionless onboarding path that pairs Sift’s data backbone with UBOS’s low‑code UI, cutting time‑to‑value from months to weeks.

What’s Next for Manufacturers?

If you’re a plant manager, CTO, or engineering leader, the question isn’t “whether” AI will touch your shop floor—it’s “how soon.” Sift’s sensor‑data platform, combined with UBOS’s orchestration tools, offers a proven pathway to:

• Reduce unplanned downtime by up to 25%.
• Cut data‑storage costs through intelligent tiering.
• Accelerate product‑development cycles with real‑time feedback loops.

Ready to explore a data‑first AI strategy? Visit the UBOS homepage for a free demo, or dive straight into the UBOS platform overview to see how Sift’s telemetry stack can be woven into your existing ecosystem.

Further Reading & Resources

Enhance your AI manufacturing knowledge with these UBOS resources:

• UBOS portfolio examples – real‑world case studies.
• UBOS templates for quick start – jump‑start AI projects.
• About UBOS – our mission and team.

The future of manufacturing is data‑driven. With Sift’s sensor‑data platform and UBOS’s low‑code AI orchestration, the factory floor is finally ready for the next generation of autonomous, intelligent production.

Andrii Bidochko

CTO UBOS

Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.