Data Platform & Apps

The layer underneath, when reporting alone will not hold it

At some point the answer stops being another dashboard. When data lives in six systems that disagree, or the process you need does not exist in any tool you can buy, you need something built underneath.

A warehouse table list: 42 modelled tables with row counts, refresh times and freshness status.
Rows modelled 18.4m 42 tables, one refresh

Sample data, fictional company

How it fits together

Many sources in. One place they agree.

Reporting sits on top of this. When two dashboards disagree, it is almost always because there is no layer like this underneath them.

How a Datavoris data platform fits togetherFour scattered sources — CRM, finance, spreadsheets and operational systems — flow into a single warehouse on Microsoft Fabric or Snowflake, which in turn feeds dashboards, internal apps and scheduled exports.One warehouseMicrosoft Fabric or SnowflakeSchema & modellingETL / ELT orchestrationAccess control & versioningYour sourcesWhat reads itCRMFinance & XeroSpreadsheetsOps systems & APIsDashboards & reportsInternal apps & portalsScheduled exports
Every report, app and export downstream reads the same modelled data — so they cannot drift apart.
What we build

Two ways in

Data Architecture, Warehouse & Pipelines

One place your data actually lives, so every report and app downstream is reading the same thing.

  • Cloud warehouse on Microsoft Fabric or Snowflake
  • Data architecture and dimensional modelling — star schemas, fact and dimension design
  • Schema design and ETL/ELT orchestration
  • Multiple sources unified, deduplicated and reconciled
  • Access controls and versioned, reviewable transformations
SnowflakeMicrosoft FabricSQLPython

Full-Stack Web Apps

The internal tool, portal or product that does not exist off the shelf — designed and shipped fast, AI-accelerated.

  • React and Next.js front ends in TypeScript
  • Python or Node back ends, with database and auth wired in
  • Built on your warehouse, so it agrees with your reporting
  • Proven in production — Revora, our own SaaS, runs on this stack
Next.jsTypeScriptPythonSupabase
What it replaces

The before and the after

Every one of these is something a real client was doing by hand before we started.

How it works today
With a Datavoris portal
Six systems each hold part of the answer, and reconciling them is a person’s week.
One warehouse they all feed. Reconciliation happens on a schedule, not in someone’s head.
Every new client means standing up a reporting setup from scratch.
A shared model with per-client partitioning. New clients go live in days.
The process you need does not exist in any tool you can buy, so it lives in email.
An internal app your team logs into, built on the same data as everything else.
Something we built

Multi-Client Data Warehouse

Every new client meant standing up a data setup from scratch. We built one warehouse with per-client partitioning, so each new client’s dashboards run on the same shared, versioned model. New-client dashboards now go live in days rather than weeks, with each client’s data properly isolated.

Is this the one you need?

Describe what is happening today in a few lines. If a different family fits better, we will say so — and if it is not a fit at all, we will point you somewhere that is.