Run all your R&D tools on one platform

The approaches that seem obvious tend to fail for the same reasons

You might be one of the few software engineers embedded in an R&D team. You know the infrastructure needs to change. But every path forward has the same problem: it takes months of setup before you can deliver anything the business actually asked for.

Building it yourself takes months you don't have

You can build a streaming pipeline on Kafka and Kubernetes. You can containerise Python applications and wire up observability. But that's months of infrastructure work before you solve a single business problem. When stakeholders are already asking "where are the results?", months of invisible plumbing work puts your credibility at risk.

Your last internal build probably didn't survive contact with users

Your team built something that worked in development. Then users needed to upload their own scripts, manage dependencies, and deploy changes without your help. That's where it fell apart. Reliability issues, messy dependency management, no standardised deployment. At some point you start looking for something else.

Stakeholders lose faith faster than you can build

As soon as someone sees a glimpse of value, they want it everywhere, immediately. A small team facing that kind of demand can't spend months on infrastructure before delivering the next thing. And when you've been promising a platform for a year with limited visible progress, the business starts wondering if it will ever arrive.

"We're developers, we can build anything"

Yes, you can. The question is whether you should. Building and maintaining Kafka clusters, container orchestration, observability dashboards, access controls, and deployment pipelines is a full-time job. If that's consuming your capacity, the actual business problems (the analytics, the integrations, the tools engineers need) sit in a backlog that never gets shorter.

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What developers at similar teams report after switching

Solving business problems in week one

A developer at one F1 team contacted Quix before he'd been in the role a week. The timescales he'd been given were incompatible with months of infrastructure setup. Within his first week he was building the analysis tools his engineers actually needed. The infrastructure was already there, he just wrote the Python applications.

One interface for your engineers

Your users already have too many applications to learn and disparate systems they need to log into. Adding three more is a non-starter. Quix gives your engineers a single point of entry: one place to deploy their Python applications, see what's running, and access the data they need. You reduce their cognitive load instead of adding to it.

Deliver on the request that's been waiting for years

Some teams we work with have been trying to deliver a self-service analytics platform for two or three years. Internal builds stalled, vendor projects failed, and the business lost patience. Quix lets you ship a working version in weeks and rebuild credibility with stakeholders who had stopped expecting results.

Keep ownership without the maintenance burden

You own the platform configuration: topics, resource limits, access controls, deployment policies. Engineers own their applications. Everything deploys the same way with the same observability and logging. You have full visibility into what's running, but you're not in the critical path for every new tool someone wants to ship.

What the platform looks like day-to-day

Managed infrastructure, your configuration

Quix runs Kafka and Kubernetes underneath, but you don't manage the cluster. You define topics, access controls, and resource limits through the Quix UI or CLI. Every application (yours or an engineer's) deploys as a containerised service with the same observability, logging, resource governance, and monitoring. One deployment process for everything.

A Python API that engineers already understand

Quix's streaming library exposes a familiar interface: data frame in, data frame out. Engineers who write Pandas for batch analysis can write Quix applications for live analysis without learning Kafka consumers, message serialisation, offset management, or container orchestration. The streaming complexity is abstracted away.

Self-service with guardrails

Engineers deploy their own applications through the Quix UI or CLI. You set the guardrails: resource limits per application, topic permissions, deployment policies, and access controls. You have full visibility into what's running and how much resource it's consuming. But you're not in the critical path for every new analysis tool someone wants to ship.

Works with what you've already built

Pre-built connectors for MQTT, OPC-UA, InfluxDB, PostgreSQL, and common industrial protocols. Custom connectors via the Python SDK in hours.

Trusted by data-intensive R&D teams at:

Low risk, fast time to value

Expert consulting included

Many engineering teams don't have software expertise in-house, which is why Quix includes hands-on technical consulting to get you up and running. We can also run workshops to show your engineers how to build their own data tools.

Get a working pilot in days

You don't need a 6-month business case. Get a pilot running with a small representative dataset. Show stakeholders a live demo with real query times instead of a slide deck.

Runs on your infrastructure

Quix deploys on-premise or in your own VPC. No data ever leaves your network. Once deployed, Quix operates without any connection to the outside world, which is why teams in defence, aerospace, and other regulated industries trust it.

No vendor lock-in

Quix runs on open-source technologies: Kafka, Kubernetes, standard time-series databases. If you ever need to walk away, you keep the blueprint, the code, and the skills.