Redpanda Data’s cover photo
Redpanda Data

Redpanda Data

Software Development

San Francisco, CA 25,276 followers

Agentic Data Plane powered by multi-modal data streaming. Connect, query, and govern for AI & real-time applications.

About us

Redpanda is pioneering the Agentic Data Plane (ADP) - a new category in AI infrastructure that makes it simple and secure to connect AI agents with enterprise data and systems. Built on a multi-modal data streaming engine, Redpanda empowers agentic applications that reason and act in real-time with speed, autonomy, and precision. Global leaders including Activision Blizzard, Cisco, Moody's, Texas Instruments, Vodafone and 2 of the top 5 banks in the U.S. rely on Redpanda to process hundreds of terabytes of data a day. Backed by premier venture investors Lightspeed, GV and Haystack VC, Redpanda is a diverse, people-first organization with teams distributed around the globe.

Website
https://redpanda.com
Industry
Software Development
Company size
51-200 employees
Headquarters
San Francisco, CA
Type
Privately Held
Founded
2019

Products

Locations

Employees at Redpanda Data

Updates

  • Redpanda Data reposted this

    Wrapping up an incredible 3 months as a Hack the Planet Scholar with Redpanda Data. What started as an idea evolved into CogniPulse a real time AI powered system for machine health monitoring that does not just detect anomalies but actually decides actions and explains them. Over the course of this journey, I focused on building an end to end system from ingestion to decision making, grounded in a simple principle: you should never be left wondering why a system acted. One of the most meaningful aspects of CogniPulse was designing a full traceability chain, where every event leads to an alert and ultimately to an action — with each step being transparent, explainable, and auditable . Building this required thinking deeply about real-time systems, moving toward an event-driven architecture where services react instead of poll, and designing AI components that don’t just output decisions but clearly articulate their reasoning . With Redpanda as the streaming backbone, I was able to bring together low-latency data processing, intelligent decision-making, and system observability into a cohesive, production-ready workflow I was able to bring together low latency data processing, intelligent decision making, and system observability into a cohesive, production ready workflow. More than anything, this experience pushed me to think like a systems engineer and go beyond just building features by focusing on designing systems that are reliable, observable, and built for real world constraints. I am incredibly grateful to the people who made this journey so impactful: , Tyson Hamilton, Santiago Jimenez, 🚀 Alexander Gallego and Evan Atkinson for their mentorship, guidance, and constant encouragement throughout the program. Github Link: https://lnkd.in/gNfaBkAb Grateful for the learning, the challenges, and the opportunity to build something meaningful. On to what is next 🚀 #DistributedSystems #Streaming #ExplainableAI #EventDriven #Redpanda #SystemsEngineering #BuilderMindset

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  • Redpanda Data reposted this

    AI agents are quickly shifting from AI systems that generate answers to AI systems that take action. In my latest Forbes piece, I explore why governance can’t stay focused on outputs. When agents are coordinating workflows, calling tools, and modifying production systems, the real question becomes: what did the system actually do, and can you prove it? Here’s how I think about it: ➡️ Governance has to move from filtering outputs to controlling actions. Once agents can execute across systems, guardrails need to live in the execution path – not just at the interface. ➡️ The right abstraction is an agentic data plane: a layer that enforces identity, policy, and observability across models, tools, and systems in real time. ➡️ If you can’t reconstruct behavior, you don’t have governance. Full, ordered transcripts of every interaction aren’t optional – they’re the system of record. ➡️ Enterprises will not standardize on one model or vendor anytime soon. Governance has to be consistent across a heterogeneous, multi-cloud, multi-model world. ➡️ Agents should be treated like any other production system: measured not just on uptime, but on correctness, safety, and policy adherence. We’re entering a new phase where AI systems not only assist but also operate. The organizations that succeed won’t just build smarter agents. They’ll build the infrastructure to govern exactly what those agents do. Give the article a read: https://lnkd.in/gjEzDHDi

  • 🎵 Me and my shadow, never apart... 🎵 Disaster recovery for #streamingdata is one of those things that everyone needs but nobody likes to set up. External replication tools like #MirrorMaker mean extra hardware and no guarantee of data fidelity. (Not to mention more grey hairs.) Shadow Linking is Redpanda's built-in, enterprise-grade disaster recovery feature. 🎉 No external service or no JVM tuning. Just continuous, high-fidelity replication built straight into the broker. ✌️ Paul Wilkinson walks through the full architecture, switchover and failover flows, and even throws in a demo so you can try it yourself. Read the full post 👇 https://lnkd.in/gMEgrfmG #datastreaming #Kafka #developers

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  • Agents promise autonomy, but #autonomy without alignment creates risk. 🤔 In episode 2 of "Hello, Agent!", Dominik Tornow (Resonate HQ) explains why building reliable #agent systems isn’t just about #infrastructure or execution frameworks. Here's the thing ☝️: durable execution can help systems run reliably, but it doesn’t solve the deeper problem of making sure agents actually behave as intended. Click the link in the comments to learn how teams can resolve the tension between autonomy and alignment👇

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  • View organization page for Redpanda Data

    25,276 followers

    Our pandas don't just build tools for #agenticAI. We run them ourselves. 🤖 Exhibit A: Redleader, a single prompt-driven agent handling customer interactions across Slack. It proved AI could draft meaningful responses, automate triage, and generate real ROI. But as adoption grew, one agent doing everything stopped being enough. So we rebuilt it. ✌️ Redleader is now a bounded, multi-agent system running on Redpanda Agentic Data Plane, with five specialist agents working together: 🤝 Concierge handles ingestion 🗂️ Records Custodian manages durable state 🧠 Orchestrator coordinates reasoning 🎯 Sentiment Discriminator classifies risk 📊 Activity Monitor handles observability and learning Rob Siwicki and Rachel Z. walk through the full architecture and what it took to go from a capable agent to a coordinated, governed system. Read the full post 👇 https://lnkd.in/gk2aBfjf #agenticdataplane #AI #datastreaming #developers

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  • AI agents are only as good as the data feeding them. That's why we're heading to #GoogleCloudNext with two things your stack is probably missing: ⚡ Redpanda Streaming: blazingly fast, #Kafka-compatible event streaming without the operational headaches. 🧠 Redpanda Agentic Data Plane: the backbone your #agentic workforce needs to run safely and act responsibly at scale. Come find us at booth 2716 and let's talk about what it looks like when your data infrastructure can finally keep up. Find us at Google Cloud Next 👇 https://lnkd.in/e2cSpBCA

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Funding

Redpanda Data 5 total rounds

Last Round

Series D

US$ 100.0M

Investors

Image GV
See more info on crunchbase