Admin Dashboards & Data Management
Build an AI-powered data analysis and visualization app
Kliv is an AI app builder. Describe your data analysis and visualization workflow, and it builds the database, ingestion jobs, dashboards, access rules, alerts, digests, and data-aware agent.
Just enter your idea into the text box and AI will build it for you
What is Kliv?
Kliv isn’t a BI suite with generic dashboards — it’s an AI that builds software for you.
You describe the app you need in plain words, and Kliv builds it: the database, ingestion jobs, validation rules, dashboards, access rules, alerts, scheduled emails, and an agent that answers from your data. What you get is a real app on your own domain, and you can change it later by asking. It isn’t a template.
On this page, that app is a data analysis and visualization app. Kliv builds all kinds of web apps; this page focuses on dashboards built on rows you control.
Why the dashboard should live where the data does
Many teams do not start with clean data in one warehouse. They start with exports, spreadsheets, and schedules from several tools, then a meeting based on whoever combined the numbers.
Kliv builds the missing app first: a database that owns the numbers, ingestion that runs on schedule, validation before writing, dashboards shaped to your meeting, and access rules that decide who can see which rows.
What the system knows
The records and rules that carry the operation.
Locations and day records
One row per store per day can hold sales by category, waste by cause, and staff hours. Charts aggregate those rows instead of separate spreadsheet copies.
Ingestion
POS exports can arrive by upload or scheduled job. A typed server-side function validates the file and rejects malformed rows with reasons.
Dashboards
Margin by store by week, waste against target, and labor ratio can be pages in your app, shaped to your Monday meeting.
The manager’s access
A store manager sees their store, a regional lead sees the region, and the CFO sees everything. Row-level security enforces the scope.
The question agent
A question such as “Which stores missed waste target in June?” is answered from the rows the signed-in user may see, and the conversation is stored as a transcript.
The Monday digest
A scheduled job can render last week’s numbers into an email on your template before the meeting starts.
An example: Sofia’s bakery chain
Here’s how one operator might use it. It’s only an example — you would describe your own data sources, metrics, access rules, alerts, and reports.
Daily files become validated rows
Sofia’s app ingests POS sales, waste sheets, and labor schedules. Each store gets one day record, and bad rows are rejected with readable reasons.
Managers see only their stores
A store manager signs in and sees the store they run. Regional leads see their regions, and the CFO sees the whole chain.
The Monday dashboard matches the meeting
The dashboard shows sales by category, waste against target, and labor ratio on the same page the team reviews each Monday.
Questions use the same access rules
A manager asks which days missed the waste target in June. The agent answers from that store’s rows, not from the whole chain.
Alerts go where the team works
A nightly job posts out-of-range stores to Slack, and the Monday digest lands before 8:00.
Changes do not lose history
When a new waste category appears or location 14 opens, Sofia describes the change and the app updates while old rows keep their meaning.
Describe the data app you want
Kliv builds from your description, so include data sources, validation rules, metrics, dashboards, access scopes, alerts, and scheduled reports. Here are three examples:
Multi-site ops dashboard
Sales, waste, and labor per store.
“Build an operations dashboard for my 12-location bakery chain. Ingest daily POS sales, waste sheets, and labor schedules; store one row per store per day with sales by category, waste by cause, and staff hours; reject malformed uploads with reasons; let managers see only their own store; give regional leads region dashboards; and send a Monday email digest before 8:00.”
Client reporting portal
Each client sees only their numbers.
“Build a reporting portal for my marketing agency. Each client should sign in to dashboards over their own campaign data, a nightly import job should update spend, clicks, leads, and conversions, and clients should never see another client’s rows.”
Quality metrics tracker
Line measurements, defect rates, and alerts.
“Build a production quality app. Ingest daily line measurements, validate ranges before writing, chart defect rates by shift and line, alert Slack when values are out of range, and keep a weekly supervisor digest.”
How it comes together
The schema changes in conversation. When a new location opens or a new waste category appears, you describe the change and the app updates while history stays intact.
Alerts go where the team already looks. A server-side job can check thresholds nightly and post out-of-range stores to Slack. The connection secret stays out of your code.
Experiments are reversible. Before a larger change, you can take a snapshot of the whole app and restore it if the new logic is wrong. Kliv also writes unit tests as it builds, so broken calculations can fail before publishing.
A dashboard is usually the first internal tool a company builds and never the last — internal tools on Kliv shows what tends to grow up around it.
Common questions
What exactly is Kliv?
Kliv is an AI app builder. You describe the data app you need, and Kliv builds ingestion, validation, dashboards, access rules, alerts, digests, and an agent that answers from your rows.
Do I get a real app, or a template?
A real app. It is built around your data sources, metrics, dashboards, access scopes, and reporting workflow.
Can I change it after it’s built?
Yes. You can add metrics, data sources, dashboards, alerts, or access rules by asking.
Our data lives in three different tools — how does it get in?
Uploads and scheduled jobs can land through typed server-side functions that validate before writing. Bad rows are rejected with reasons you can read.
Can managers see only their own numbers?
Yes. The rule lives on the rows, so dashboards, exports, and agent answers all read through the same row-level access.
Is the agent doing analysis or inventing it?
It queries the rows the signed-in user may see through capabilities you grant. If the data does not answer, the honest answer is that it does not.
What happens when we add locations or metrics?
You describe the change and the schema updates with history intact. Old rows keep meaning what they meant.
Can we trust a change not to break the numbers?
Kliv writes unit tests as it builds, and passing tests can gate publishing. Snapshots add a restore point for larger changes.
Can alerts post to Slack?
Yes. Scheduled jobs can check thresholds and post out-of-range values to Slack, with the integration secret held by the platform.
Can I use my own domain?
Yes. The dashboard app can run on your own domain.
Can I move off Kliv later?
Yes. The app’s code syncs to your own Git repository, so leaving is a git pull rather than a rebuild.
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Build one version of the numbers
Describe the data sources, validation rules, metrics, dashboards, access scopes, alerts, and digest schedule. Kliv builds the data app around them.
