Orama Studio · architecture
Inside the engine: an adaptive database
Orama Studio is built on an adaptive database that combines the data store, AI compute, model training, and orchestration. The store, training loop, model execution, and agent runtime are one product architecture, designed to run as a distributed system.
01 · Spine & substrate
One engine, both control plane and substrate.
The engine manages platform state, deployments, storage, workflows, models, and the agent runtime. Data and compute share the same distributed substrate, so teams operate one product architecture instead of assembling the AI system from separate frameworks.
02 · Why it's fast
The work comes to the data.
Compute next to the data
Rather than pulling data out to a warehouse, processing it, and pushing it back, the work runs on the node where the data sits. Generated and hand-written logic executes next to the bytes, for lower latency and no warehouse round trip.
Storage that manages its own cost
Frequently used data stays on fast storage and colder data moves to cheaper storage automatically, so repeated questions stop carrying warehouse-scale cost every time they are asked.
A distributed cluster
Nodes form a replicated, self-healing, distributed cluster, where a laptop, a server, or a cloud instance are the same kind of node. Adding capacity is joining a node, and the topology spans from a single machine to a private cloud.
Native compute spans CPUs, every major GPU, and ML ASICs, across operating systems and architectures, from a laptop to a data center, the same kind of node.
One cluster, every vendor: see deployment →03 · Learning, not inference
Train models on your outcomes
A general-purpose model can provide an interface, but a call per row does not train a model on your business outcomes. Orama Studio trains the models and pipelines the problem needs on your data. Each model has measured accuracy and explained drivers, and several models can work together when the use case requires an ensemble.
Provides an interface
Useful for language tasks and interaction, without learning a business-specific outcome from each request.
Learns from outcomes
Trained on your signals and evaluated against what happened, with measured accuracy and explained drivers.
04 · Personalized intelligence
It knows you and your data.
Training on your data is the hard part. It has always needed a scarce expert in the loop, and it is where most AI projects quietly stall. Orama Studio closes that gap with automation: it studies you and your data, proposes what is worth building, and trains the agent to do it, so you don't have to already know the answer.
Auto-suggest
You don't have to know what to do with your data. Orama Studio studies it, and how you work, and hands back ranked, high-value use cases, ready to build.
Automated training
It trains an agent on your data automatically: the 'trained on your data' hard part done by the platform rather than by hand. The agent arrives already fluent in your systems.
Personalized interface
Reach Orama Studio over a standard agent interface, and it adapts to you. The same endpoint that serves your agents shapes its tools and responses to your context, so every surface is personalized, not one-size-fits-all.
05 · The name
Zetta: the platform Metis is built on.
Zetta is the platform Metis is built on and Orama Studio runs on: dashboards, pipelines, warehouses, applications, agents, models and model training. The name is a statement about the ceiling, not your data. Scale runs one way: going bigger is the hard direction, and going smaller is free. An architecture built for the extreme, with data where it lives, work next to it, and storage managed for cost, is exactly the architecture that stays fast and economical at the scale you actually run. Solve the largest data problems, and the everyday ones come for free.
Talk to sales
Start with the outcome
Tell us what you want to predict or automate and where the system has to run. We will scope the first production use case with you.