Orama Zetta · how it works
From intent to a running system, in four stages.
Orama Zetta turns a stated problem — or an artifact you already have — into a correct, deployed, running data and AI system. Every stage is visible, every step open to inspection, and nothing advances until it is correct.
The arc
Connect, model, deploy, and learn — as one flow.
The four stages
Every stage is visible and open to inspection.
Plain language, or what already exists
A project begins with intent expressed in words, or with an artifact already in hand — an existing workflow, a SQL script, a notebook, a schema. Orama Zetta reads both the stated goal and the logic already encoded, and reconciles them into a single problem definition.
- Natural-language intent
- Existing workflows & scripts
- Notebooks & schemas
- Source profiles
A correct graph, validated before it runs
The problem is compiled into an execution graph — connect, clean, model, index, serve — laid out to professional standard. Schemas are type-checked against the real sources; modeling steps are checked for leakage and point-in-time discipline. The graph is visible and inspectable; gaps are surfaced as gaps, not papered over.
- Connect → clean → model → index → serve
- Schema type-checking
- Leakage & point-in-time checks
- Visible, editable graph
One action, onto infrastructure you run
A validated graph is provisioned as Orama Zetta nodes and workflows onto the chosen target: a local environment, a Kubernetes cluster, your own cloud account, or an on-prem host. The work deploys into that environment and runs there.
- Local → cluster → cloud → on-prem
- Node provisioning & reconciliation
- Runs in the environment you choose
- Self-healing runtime
Answers with lineage — and a system that sharpens
The graph executes on the nodes: workflows run, indexes build, models score. Results return with full lineage — the sources used, the steps taken, how fresh and how confident. Each run is grounded experience the next architecture can learn from.
- Live execution & monitoring
- Source-level lineage
- Freshness & confidence signals
- Grounded improvement loop
05 · The correctness gate
Generating a workflow is easy. Generating a correct one is the product.
A graph that ships confidently-wrong results is worse than a queue. Before deployment, every graph passes a correctness gate — and is blocked, with a reason, if it does not.
Schema & type checks
Every step is validated against the real source profiles. A reference to data that isn't there is flagged, not silently generated.
Leakage & point-in-time
Modeling steps are checked so models never train on information they wouldn't have had at prediction time.
Entity resolution
Results resolve to the right entity — a single subsidiary, not several blended into an averaged, flattering blur.
Talk to sales
Bring us the problem
Tell us what you want your data to do and where it has to run. We will scope the deployment with you on a call.