Governed AI BI. For IT and .
Every Excel uploaded, every AI call, every chart, tracked, encrypted, auditable. No SQL, no DAX, no IT ticket. The semantic-layer-first BI that ends shadow Excels and shadow ChatGPT.
Revenue by month
Every mid-sized company is stuck in the same loop.
IT can't keep up.
Power BI requests pile up. Each one takes weeks because requirements are vague and the data needs reshaping. Two engineers, fourteen tickets, no time to improve the warehouse.
Management can't wait.
So they export a CSV. They build it in Excel. They paste confidential data into ChatGPT. There are now two versions of every chart, and IT has no idea what data is leaking.
The result: data dispersion, unclear requirements, no feedback loop, zero governance. AI spend is invisible. Excels live on personal laptops. Nobody is happy. This is the problem zenital selfBI solves.
A regional director needs revenue by region this quarter.
- Files a request to IT
- Waits for a clarification meeting
- Pushes back on the metric definition
- Gets a final dashboard, already stale
- In parallel: builds an Excel, mails it around. Two versions, two numbers.
- Opens selfBI
- Types "Revenue by region this quarter vs last"
- Chart appears in 4 seconds
- Adds a filter: "exclude internal sales"
- Pins it. Shares the link with her team.
selfBI vs the workarounds you have today.
Most companies juggle two or three of these. zenital selfBI replaces that mix with one governed surface.
SUMMARIZE(
Sales,
Sales[Region],
"Revenue",
CALCULATE(SUM(Sales[Amount]))
)
| A | B | |
|---|---|---|
| 1 | Region | Revenue |
| 2 | EMEA | 412,300 |
| 3 | NA | 298,500 |
| 4 | =SUM(B2:B… | #REF! |
What we don't do.
Most BI tools over-promise. We'd rather tell you up front.
We don't replace Power BI.
We sit above it as the self-service layer. Some clients replace, others run both.
We don't generate "insights".
We generate charts. Insights are still the human's job.
No predictive analytics.
Not the product. We do BI well, not forecasting, not ML.
No real-time streaming.
Cube caches. Freshness depends on your source, and we tell you when.
No "auto-magic" data modelling.
The semantic model still needs care. zenital builds and maintains it as a service. We don't pretend otherwise.
The honest answers.
Power BI is a tool for IT. Self-service in PBI means handing a SQL-less user a DAX editor, most managers won't. selfBI is built for the SQL-less user. The IT side is the bonus, not the core.
ChatGPT doesn't connect to your warehouse with row-level security. It also won't show IT what employees pasted into it. zenital selfBI is the governed alternative.
The org admin chooses the privacy mode in /admin/settings: full telemetry, or "metadata only" (which hides prompt content while keeping cost and usage signals). Either way, every employee knows what is logged.
Every chart has a "this looks wrong" button that triggers a correction flow in plain language. The AI never generates raw SQL, it goes through a validated semantic layer that prevents hallucinations.
Encryption at rest (AES-256-GCM). Per-org data isolation via row-level security. A unified event log captures every AI call, file upload, dashboard action, and admin change, queryable from /admin. BYOK and SOC 2 path are on the roadmap; everything else is live today.
Yes. zenital selfBI runs in Docker. We offer SaaS, managed deployment, and self-hosted with a license. Many clients start managed and migrate to self-host once they trust the product.
A typical pilot is two weeks. Week 1: IT connects 1–2 sources and we build the semantic model together. Week 2: management runs the wizard against real questions. We help with the model in week 1 so you see real value in week 2.
Two weeks. One of your data sources. Your real workload.
We do a pilot: week 1 we connect a source and build the semantic model. Week 2 your team uses it for real questions. You see, on your data, exactly what we just described.
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