Beyond Kubernetes
We started with Kubernetes, but Astro Platform is built to be the control plane for whatever compute looks like next — serverless, GPU clusters, edge inference, and agent runtimes.
About AstroPulse
Every team deserves a platform engineer.
Production isn't one thing. It's applications sitting on services, sitting on compute, and sitting on cloud infrastructure. Every layer breaks differently. Every change ripples in ways nobody predicted. Most teams are running this without really understanding what's underneath them.
Now add AI to that: code nobody fully reviewed, systems nobody completely understands, PRs moving too fast to audit, and generated logic that is too difficult to trace. We're heading toward something like the internet — infrastructure so layered that most people who run it have no idea what's actually underneath. That's fine until something fails. When AI-generated systems fail, the people who shipped them often can't explain why. The operational response needs to be smarter than the systems being operated.
The problem
AI is running in production without visibility into what it's doing, policy enforcement, or an audit trail. When it fails, nobody can explain why. The model was never the bottleneck. The control layer was.
The insight
You don't train AI on every layer of complexity. You build APIs that abstract it. AI expresses intent. The runtime decides what's safe to run. Operational knowledge accumulates in the platform, not in the model.
What we're building
Astro Platform is that runtime. Nova is the AI platform engineer operating through it. We started with infrastructure because we've lived the problem. Our direction is clear: to build the control plane for every AI workflow in production.
Our approach
AI expresses intent. The runtime decides what runs.
Most teams building in this space follow the same pattern: they connect every tool, pull context from each one, feed it into a model, and hope it reasons correctly. The problems compound quickly: token costs rise at scale, data leaves the customer environment, and fragmented context produces inconsistent results. Yet none of it controls what happens next. It only describes what is happening.
Read and write are not the same problem. Reading metrics, correlating logs, and surfacing what's wrong: every tool does that. Writing to production is different. A restart, a rollback, or a config change — the blast radius is real, and the person executing often has to call someone who actually understands the system. That coordination has always been how production changes safely. Astro Platform formalizes it: blast radius assessment, policy enforcement, approval routing, and an audit trail. Nova reads freely. Nova writes only through the runtime. Production receives only what the runtime approves.
Astro Platform
The runtime for AI operations.
Bring your own compute — Kubernetes today, whatever comes next. Astro Platform wraps it with a runtime layer: intent-based APIs, audit trails, policy enforcement, and human approval gates built in. Every operation is traced. Nothing executes unless the runtime approves it.
Nova
The AI that works through the runtime.
Nova connects to your stack through purpose-built skills — Datadog, Prometheus, GitHub, Kubernetes, and more. It investigates, plans, and calls the runtime with intent. This is not generic AI running directly against production. It is an agent operating through the right execution layer.
Where we're going
Read is solved. Write is where we're going.
The deeper challenge isn't capability. It's trust. There is something important about why we trust humans in production that most people miss: human slowness is an accidental safety mechanism. Reading a runbook step, thinking before executing, and pausing to verify — that cognitive friction gives people time to notice something wrong. AI doesn't have that. It can execute thousands of operations before anyone realizes a mistake was made. The very thing that makes AI powerful in production is what makes it dangerous.
The runtime replaces accidental friction with intentional control: blast radius assessment before any write executes, policy enforcement at machine speed, approval gates that pause the right operations at the right moment, and audit trails that make every action explainable. As trust is established and patterns repeat, the runtime learns what can safely proceed automatically and what still needs a human. That's how autonomous operations are earned, not assumed.
From reactive to proactive
Today, Nova investigates after incidents happen. Next: an AI that understands your production state deeply enough to prevent them.
AI SRE for every team
The institutional knowledge of an experienced SRE is available 24/7 and gets smarter with every deployment you run.
The problem
Operational knowledge lives in a handful of engineers.
In most companies, a few senior engineers or long-timers know how production actually works — which service breaks under load, which deployment causes drift, and which alert is noise. When they leave, everything becomes fragile.
That knowledge should live in the platform. Nova is how it becomes available to everyone — the junior engineer who just joined, the team that just inherited a system they don't understand yet, and the on-call engineer at 3 a.m.
Join the journey.
We're a small team building something that doesn't fully exist yet. The AI-native operational platform is still being defined — and we're defining it.
If you've felt the pain of a Friday deploy gone wrong, rebuilt runbooks that nobody reads, or believe AI can do real operational work — we'd love to talk.
For teams
Get the operational depth your team deserves, without hiring a dedicated SRE team.
Open NovaWhat we believe
Principles that guide every decision.
We're early. The market is figuring out what AI operations actually mean. These beliefs are our compass.
01
The platform engineer for everyone
World-class platform engineering expertise shouldn't be reserved for companies that can hire a team of SREs. We're building Nova to give every engineering team — three people or three hundred — the operational depth that production demands.
02
Depth over surface
AI that reads from external APIs and guesses is a chatbot. AI that runs on the actual platform — knowing the rollout history, the cluster state, and the governance policies — is an engineer. We chose depth. That's why we built Astro Platform first.
03
You stay in control
Nova investigates, hypothesizes, and proposes. You approve what runs. Guardrails aren't a limitation we added later — they're core to how Nova is designed. AI that acts without human confirmation has no place in production.
04
Build for the inflection point
The AI-native operational platform doesn't exist yet. The tools being built today will define what platform engineering looks like for the next decade. We're building for that moment — not for where the market is, but for where it's going.
Team
Built by engineers who lived the problem
AstroPulse was not built by people who saw a market opportunity, but by engineers who spent years inside the problem and know exactly what's broken.
Rajesh RC
Founder & CEORajesh built PaaS, data platforms, and SaaS at scale from scratch. He watched brilliant engineers spend half their time on operational complexity that shouldn't exist. He is now building AstroPulse to fix that: a runtime between AI and production, so every team has the operational depth that used to require the right people in the room.
Careers
Join our mission
We're building something early and important. If you've lived in production operations, built infrastructure at scale, or believe AI can do real operational work, we want to talk.
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