Today we're launching Arvix AI: the autonomous optimization engine inside Unravel that closes the loop between knowing what's broken and fixing it. Not an advisor. An operator.
Most "agentic AI" in our category sees a problem, writes you a recommendation, and leaves you to do the actual work. That's not an agent. That's a slightly faster Jira ticket.
Today, Unravel is launching Arvix AI, the autonomous optimization engine that lives inside the Unravel platform. Arvix doesn't just tell you what's wrong with your Databricks, Snowflake, and BigQuery workloads. It fixes them. It rewrites the inefficient code. It rightsizes the compute. It cleans up the storage. Then it validates every change against real workload behavior before applying it, and rolls it back automatically if anything degrades.
The three problems Arvix AI actually solves
If you run data infrastructure for an enterprise today, you're living with three problems at once.
The bill keeps going up, and nobody can fully explain why. Pipelines miss SLAs while your best engineers spend their week firefighting instead of building. And every platform you run on, Databricks, Snowflake, or BigQuery, gives you a list of recommendations that piles up faster than your team can implement them.
None of those are tooling problems. They're capacity problems. There aren't enough hours in your engineers' week to manually optimize thousands of queries across multiple platforms while also delivering the data products the business is asking for.
The current generation of observability and FinOps dashboards, as well as native platform tools, all do the same thing. They show you the problem. Then they hand it back to you. LLMs might give you an answer, but they don’t understand your environment and can’t validate their recommendations. So you have to live with the risk and cost of failure.
The dynamic underneath the dashboards
Here's the part most people don't know. Fewer than 10% of platform-native recommendations ever get implemented.
Not because the recommendations are wrong. They're often quite good. But, acting on them requires a senior engineer to read the suggestion, understand the surrounding context, write the fix, test it, deploy it, and watch it in production. Multiply that by thousands of workloads. The math doesn't work.
So the recommendations sit. The bill keeps growing. The team keeps firefighting. Visibility without action is just expensive watching.
The missing layer isn't more monitoring. It's a system that can do the work itself, safely, at scale, without your team having to sign off on every line.
A decade of telemetry is the bridge
This is where Arvix is fundamentally different from anything else you can put on top of your data platform.
A generic LLM starts from zero on every workload. It sees one query, one moment, no history. It can suggest something plausible. It cannot know whether that suggestion will hold up in your environment and at your scale, because it has never seen your environment or scale.
Arvix AI has. Unravel has spent the last decade building the most complete view of how data platforms actually run. We've analyzed more than ten billion workloads across 100+ enterprises. Every failure pattern. Every cost spiral. Every regression that broke a dashboard and every fix that brought it back.
That telemetry is the substrate on which Arvix runs. It's why Arvix can act with confidence in production, and it's why other tools can't prompt their way to the same capability in six months.
You can't synthesize ten years of operational data. You either have it or you don't.
The Context Graph is why automation can be reliable
Telemetry alone isn't enough to act. To safely change code in production, the system has to understand what depends on what.
That's the job of the Context Graph, the intelligence layer at the core of Arvix AI. The Context Graph continuously maps your entire data environment across six dimensions: compute, workload, data, code, platform, and business.
It doesn't just read the SQL. It knows which cluster the query runs on. It knows which downstream pipelines depend on it. It knows which team owns it, what the SLA is, and how the specific platform configuration affects code execution.
That cross-dimensional awareness is what allows Arvix AI to make changes without breaking anything downstream. It's the difference between an LLM rewriting your query because it looks cleaner and Arvix rewriting it because it knows the rewrite is faster, cheaper, and won't disrupt the three jobs that consume its output.
This is also the part that's fundamentally lacking in generic AI.
Operator, not advisor
The other thing that makes autonomous action possible is the safety net.
Every change Arvix proposes gets tested against real workload behavior before it goes anywhere near production. After deployment, Arvix monitors the change and automatically reverts if performance degrades. You set the autonomy level for each workload and team. AutoApply for the changes you trust, Human-in-the-Loop for the ones you want eyes on.
The result is a system that behaves as a great senior engineer would if they could clone themselves a thousand times and never sleep. Conservative where it should be. Aggressive where it can afford to be. Always validating. Always reversible.
That is the definition of an operator. And it's what separates Arvix from every "AI agent" in this category.
What this looks like in production
A top 3 global airline ran into resiliency week and turned Arvix AI loose on their Databricks environment. In three days, Arvix applied 1,500 optimizations autonomously and delivered $340,000 in savings. Zero production incidents.
A global pharmaceutical company using Snowflake realized $725,000 in optimization across 1,471 jobs, with a 5.95x return in year one and a two-month payback.
Across deployments, the average customer sees a 40% reduction in data platform spend with simultaneous performance gains. SLAs hold. The bill goes down. Engineers stop spending their week firefighting and go back to building things that matter.
The test of an agent
The test of an agent isn't whether it can answer your question. It's whether you needed to ask in the first place.
The companies that win the next decade in data aren't going to be the ones with the prettiest dashboards. They're going to be the ones whose engineers no longer have to look at dashboards at all. The work happens in the background. The platform pays for itself.
Arvix AI is the engine that gets you there. And it's available today, inside the Unravel platform. Learn more about Arvix AI →