High-quality decisions
A sound process: the right frame, real alternatives, reliable information, and clear trade-offs, so the choice it produces is a sound one.
Limited by whether the data is true, complete, and understood.
The Enterprise Intelligence OS
Investable, fiscally responsible AI and intelligence needs the right data.The Metis enterprise intelligence operating system: applications that create the right data, the pipelines and warehouses that make it available, and the models, agents, and dashboards that turn it into decisions, outcomes, and returns. Built by Orama on Zetta.
OramaJS on GitHub · 10.6k stars · 5.8M monthly downloadsorama studio · the surface of metis
Which accounts could expand next quarter, what signals support the forecast, and what should their success team do next?
Concept preview
Score the data first: adoption history, engagement signals, and renewal outcomes exist and are current, so the decision is admitted. Train an expansion model on them, rank eligible accounts, explain the signals behind each score, and give customer success a next-action queue tied to those signals.
grounded inproduct adoption historyaccount engagement signalsrenewal outcomes
- Expansion propensity model
- Account-level score explanations
- Customer success action queue
What is intelligence?
Intelligence is high-quality decisions, made fast, made many
For an organization, intelligence is not a model or a tool it has licensed. It is the capacity to know what is happening, why, and what will happen next, to decide what to do, and then to do it. The Greeks called the practical kind mētis: wisdom applied to the situation in front of you. Metis is named for it.
Fast decisions
Made at the speed the world changes. A weekly report cannot run an intra-day pricing decision.
Limited by whether the data is current when the decision is made.
Many decisions
Made at scale and repeatably, up to and including acting without a person in the loop.
Limited by whether the data can be reached, is permitted for the use, and is in a form software can use.
A company that owns a forecasting platform but does not trust its forecasts has no predictive capability. Intelligence is what an organization can actually act on.
The big idea
AI is only as good as the data it can reach
No model, agent, or platform can see past the data underneath it. That makes data availability the place to start, and how far your data can carry each decision can be measured before anything is built.
The argument you have been sitting in
- Which model is smarter
- Which agent framework is newer
- Which platform has more features
- Whose AI strategy is thicker
Every one of them argues about what to build on top of the data. Every one of them assumes the data is there.
The question that has a number
What can your data actually carry, for the decisions you have to make?
Scored per decision on ten dimensions, combined as a weakest link, and summarized as a unit's Corporate Intelligence Capability (CIC) score from 0 to 10. Nothing above the data raises it, so it is measured first.
Built around the data you already own, and the data you do not have yet
- Application data
- Warehouses and lakes
- Documents and records
- Decisions and outcomes
- Sensors, partners, and the web
Grounded in reality
Data availability first, then decisions, outcomes, and returns
Every report, model, and agent is downstream of the data it can reach: you cannot have AI, or intelligence, without data availability, and nothing built above the data raises that ceiling. Metis measures availability first, before anything is built, and scores it per decision, because relevance is a relation between data and a decision: the decision says which data has to be available. That is decision-driven, not data-driven, which starts from what exists and answers only the questions the data happens to be able to answer. Metis raises availability where it is short and logs every decision so outcomes and returns are measured, not assumed. Intelligence and AI investments, grounded in return on investment (ROI).
Outcomes and returns: every decision is logged with what was expected, outcomes are measured at the horizon, and returns are stated as what was invested, how far availability rose, and what the business metric did: return on investment (ROI), in the unit's own metric.
Grounded in data you actually have
Your dataSource
scoreAvailability, scoredScored
admitDecisionUnit
actInstrumentRuns
measureOutcomeMeasured
stateReturns, measuredStated
Nothing above the data can add information the data does not contain. That is why Metis starts with availability, not with a model.
What the OS spans
Application development, data engineering, and AI, as one system
Applications that create data
The one thing nothing downstream can repair is data that was never captured. Metis builds the applications that create it: capture at the point of decision, workflow applications in place of spreadsheets, extractors, experiments, and collectors.
Pipelines and warehouses that make it available
Zetta compiles intent into verified pipelines and holds the data on one distributed platform, whether it is the store, runs over your warehouse, or becomes the store over time.
Models, agents, and dashboards that use it
Models trained on your data with calibrated confidence and explained drivers, grounded answers, agents under per-action permission, and dashboards over one definition per metric. AI is the engine of these, and a small part of the whole.
Landed by forward-deployed engineers
Metis is landed inside your organization: inventory the decisions, score the data, raise what is short, run what the data can carry, and measure the outcome on a metric you already report.
In plain numbers
Built in the open, sold in plain words
10.6k
GitHub stars on OramaJS, our open-source search library
5.8M
Monthly downloads of OramaJS on npm
30 days to 11 Sep 2026
One belief
The right data driving measurable outcomes
Open source
Read OramaJS before you commit to anything
Your cloud, on-prem, or dedicated
Metis runs in your cloud, on-prem, or dedicated
What Metis is made of
One operating system, three ways in
Understand the platform, bring enterprise decisions to Orama Studio, or build directly with the open-source OramaJS library.
The Enterprise Intelligence OS
Metis
The operating system itself: what Zetta makes possible, how the OS is grounded in reality, and how it lands.
The surface of Metis
Orama Studio
Turn business questions into verified production models, pipelines, and agents on your infrastructure.
Open-source search
OramaJS
A free search library for applications that want to own and operate their index.
How it lands
Forward-deployed, on infrastructure you choose
Metis runs in your cloud, on-prem, or dedicated, and it meets your data where it is. All three are supported, chosen with your platform team.
- Zetta as the store, your warehouses as sources
- Zetta over the warehouse you already have
- Zetta becoming the store over time
Proof you can inspect
Start with the platform
See what Zetta makes possible, dashboards, pipelines, warehouses, applications, agents, models and model training, and how the OS is grounded in data availability, decisions, outcomes, and returns.
Product truth is more useful than an unsupported customer quote.
Start with your decisions
Bring the decisions that matter and the data you have. The first thing we do is score them; the second is show you what moves the score. That is how AI spending stays fiscally responsible.