Every company has data. But data without context is just numbers.
“Active users” means one thing to the product team. Something else to finance. Something else again to the exec dashboard.
When an AI agent picks the wrong definition, it gives you a confident wrong answer. And you’d never know.
Genie Ontology tries to fix this.
It builds a living map of how your business works — learned from your tables, dashboards, pipelines, Slack, Salesforce, Google Drive, and 50+ other tools.
Then it ranks definitions. Think Google’s PageRank, but for business knowledge.
Which definition of a metric is most trusted? Most used? Most tied to certified data? That one goes to the top.
Databricks calls this OntoRank.
The early results? Genie answered 84.5% of real-world data questions correctly on the first try. The best general-purpose AI agent scored 52.4%.
That’s a massive gap — and it shows just how much context matters.
The interesting question isn’t whether this is impressive (it is).
It’s: what happens in the other 15.5%?
The agent doesn’t say “I’m not sure.” It answers — confidently — and cites a trusted source.
Context tells an agent what something means.
It doesn’t check whether the math it does with that meaning is actually right.
- For all of your apps and data. Genie One connects insights from across your entire data estate through a rich ecosystem of native connectors. Through Lakehouse federation, Lakeflow Connect, and new two-way integrations to everyday business tools (e.g., Gmail, Slack, Teams), Genie One can extract insights and orchestrate actions with any system you may have.
- Full co-work capabilities. Genie One provides a full suite of agentic-cowork capabilities, including schedules and alerts, monitoring, document creation, custom skills, and custom MCP support. Sales leaders can ask Genie to prepare a daily brief for all their customer meetings, combining the context from their calendar, email, and hard data from their Lakehouse and other connected systems. General managers can simply paste in last month’s business review document, and ask Genie to update it with the latest information from their inventory systems, along with the transcripts from their team discussions. The possibilities are endless.
Databricks published two things that are really the same argument: the dashboard era is ending.
Genie Agents now take multi-step actions across your business tools without a human clicking through each step. Genie Ontology builds the context layer automatically, pulling from tables, queries, and dashboards to figure out which sources are trustworthy. That’s the part most AI projects skip: teaching the system what a customer, order, or subscription actually means before you ask it to act.
The second post makes a related case. Dashboards were never the finish line, they tell you what happened, they don’t move you forward. A decision execution platform detects the signal, recommends the action, executes it, and logs the outcome. One loop instead of a meeting to debate a chart.
We’ve been arguing a version of this for a while. Most companies don’t have a dashboard problem. They have a data problem dressed up as one. The biggest platforms in the industry are now finally building for the loop, not just the chart.
It’s the work we do for clients: structure the data first, then let agents act on it instead of just reporting on it.
Is your team still waiting on a dashboard to tell you what to do next, or is something already doing it for you?
