Business Tools
Build a data integration and analytics platform
Kliv is an AI app builder. Describe the data platform you need — feeds, schedules, join keys, rejects, transforms, dashboards, and access rules — and it builds it around your data.
Just enter your idea into the text box and AI will build it for you
What is Kliv?
Kliv isn’t a packaged BI tool with fixed connectors — it’s an AI that builds software for you.
You describe the app you need in plain words, and Kliv builds it: the database, import jobs, transforms, dashboards, roles, and reports. What you get is a real app for your team. You can use it, change it later by asking, and keep the records in your own system. It isn’t a template.
On this page, that app is a data integration and analytics platform. Kliv builds all kinds of web apps; this is one example.
Why build your own data platform?
Many teams have plenty of data and little agreement. Tills, webshop orders, supplier invoices, and loyalty scans may all describe the same business in different formats. Reports then depend on one person’s copy-paste routine.
Kliv builds the receiving database and the rules around it. Feeds arrive on a schedule, join keys settle differences, rejects are held for review, and published tables come from applied rows.
Feeds, keys, and loads as tables
Integration is easier to trust when each part is a record.
The feeds
Each source is a row: till export, webshop orders, supplier invoices, or loyalty scans, with format, owner, and expected arrival window.
Join keys
SKU maps and store maps decide when different systems mean the same product or location.
The load log
Every run records rows read, rows applied, rows set aside, and why. Bad numbers become a lookup instead of a long investigation.
The rejects table
Rows that do not match a key are set aside with a reason. When the key is fixed, they can apply on the next run.
Published tables
Sales by store, margin by category, and slow-movers lists are computed from applied rows only, not from feeds that partly arrived.
An example: Verner’s garden-center chain
Here’s how one retailer might use it. It’s only an example — you would describe your own feeds, keys, transforms, rejects, and dashboards.
The same sale appears twice
Month-end shows a suspicious quarter for garden furniture. The webshop and tills both counted the same 41 click-and-collect orders.
The load log shows the duplicate
Verner opens the load log and sees order 7203 in both feeds: same customer, same teak table, counted once at the warehouse and once at the till.
The rule is written once
The fix becomes a transform: collect-in-store orders count where the money was taken, and the twin row is dropped. The rule has author and date.
The quarter is replayed under the rule
The pipeline reruns the quarter. Published tables update, and the load log keeps the before and after so January can still be explained in July.
The trading meeting uses one number
Fourteen stores read one sales figure. The argument moved from the meeting into the join keys and transform history.
Describe the first data flow
Kliv builds from your description, so the more detail you give, the closer the first version. Include sources, schedules, file formats, keys, rejects, transforms, and who can see which tables. Here are three to build on:
Retail sales warehouse
Tills, webshop, returns, click-and-collect, and sales tables.
“Build a warehouse for our 14-store garden-center chain. Load till CSV exports and webshop orders nightly, map SKUs and stores, apply our returns and click-and-collect rules, hold rows with missing keys in a rejects table, and publish agreed sales tables by store and category.”
Supplier invoice matching
Invoices, receiving records, and mismatch reasons.
“Build a tool that loads supplier invoices and receiving records every morning, matches them by purchase order and SKU, lists mismatches with reasons such as quantity difference or missing receipt, and sends the buying team a daily exception report.”
Loyalty insight board
Loyalty scans joined to sales and store trends.
“Build a dashboard over our loyalty-card scans. Join scans to till sales by customer and receipt, show repeat-visit rates and category preferences by store, and restrict store managers to their own store’s numbers.”
Where the rules live
Transforms are typed TypeScript functions. Click-and-collect, returns, and VAT split rules are readable code with author, date, and diff.
Runs are scheduled server-side. Nightly tills, hourly webshop orders, or any cadence your feeds keep can run as jobs. A failed run or missing feed leaves a red row in the log.
Access can be scoped by store or role. A store manager can read their own store’s tables, while buying or finance can read the views they need across stores. Row-level security enforces that boundary.
The best interface for this kind of tool is a meeting where nobody rechecks the number by hand. More examples are at internal tools that answer to a meeting.
Common questions
What exactly is Kliv?
Kliv is an AI app builder for custom web apps. You describe your data platform, it builds the app, and you own the result.
Do I get a real app, or a template?
A real app. Kliv builds the database, imports, transforms, rejects table, dashboards, roles, and reports around your data flow.
Can I change it after it’s built?
Yes. You can add feeds, change transforms, add keys, change dashboards, or adjust access rules later.
Does Kliv connect to our till system?
If the till can export a file or call a URL, yes. Feeds can arrive as scheduled pulls, uploads, or webhooks handled by typed functions.
What happens to rows that don’t match?
They wait in the rejects table with a reason, such as unknown SKU or missing store code. Fixing the key can apply the rows on the next run.
Can history change under us?
Only with a record. Reruns are written to the load log with what changed and why, so restated numbers have an author and date.
Who sees chain-wide numbers?
The roles you name. Store managers can see one store, buying can see categories, and finance can see chain-wide tables.
Can it send alerts when a feed is late?
Yes. Each feed can have an expected arrival window, and late feeds can trigger a message before the morning report is missed.
Can rejects be fixed and reapplied?
Yes. Rows can sit with a reason until the key is fixed, then apply on a later run without being dropped.
Can I move off Kliv later?
Yes. The code syncs to your own Git repository, so leaving is a git pull rather than a rebuild.
Do I need a separate dashboard tool?
No. The app can include its own dashboards, reports, admin screens, and scheduled emails.
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Make the source of each number visible
Describe feeds, schedules, keys, transforms, rejects, dashboards, and access rules in detail. Kliv builds the platform that holds the rules and the numbers.