AI & Data

One trusted version ofthe numbers, for everyteam

Pipelines, warehouses and data models that bring your systems together — so reports agree, analysts stop reconciling, leaders decide faster, and AI has clean history to learn from.

Assess your data See the results

Single source of truth: One modelled warehouse, shared definitions, no competing exports.
Governed: Lineage, access control and quality checks on every pipeline.
Near real-time: Freshness matched to the decision — minutes where it matters.
4–8 weeks: To a first trusted dashboard.

70%: Less time reconciling reports
15 min: Data freshness
40+: Sources unified

Overview

Most dashboards disagree becausethe data underneath does

Finance, sales and operations each pull numbers from their own exports, so every leadership meeting starts with an argument about which figure is right. We build pipelines that bring those sources together with agreed definitions and visible lineage.

Once the foundations are trusted, reporting becomes fast, self-service becomes safe, and AI projects finally have clean, well-understood history to work with — instead of a data-cleaning project disguised as an AI project.

Who this is for

CFOs and COOs whose teams reconcile before every meeting
Groups with multiple entities, branches or ERPs and no consolidated view
Organisations preparing data foundations before an AI initiative

25+: Data platforms built and running
4–8 weeks: To a first trusted dashboard

What's included

What you get from thisengagement

Working software and a documented reason for each decision — not a slide deck of possibilities.

Data audit
An inventory of sources, owners, definitions, quality issues and the metrics that actually drive decisions.
Pipelines & ELT
Scheduled and streaming pipelines that load data reliably from every system you run, with tests on every load.
Data warehouse & modelling
A modelled warehouse with clear, documented definitions, so every team calculates metrics the same way.
Reporting & BI
Dashboards built on the warehouse, answering the questions leaders actually ask — and self-service for analysts.
Governance & access
Permissions, lineage, data quality checks and retention rules that keep sensitive information safe and auditable.
Monitoring
Alerts when a pipeline fails or data looks wrong, before anyone reads a bad report.

Our approach

From audit to trustedreporting, in threephases

Each phase ends with something you can review and a decision point where you can stop.

Start with an audit

- Inventory sources, owners and current reports
- Agree the metrics and definitions that matter
- Design the target data model and access rules
- Fixed-scope plan and estimate
You end up with: A data map, agreed definitions and a number — before any pipeline is built.
- Warehouse, pipelines and tests for the first domain (e.g. finance)
- Data quality checks and lineage in place from the first load
- First dashboards reconciled against source systems and signed off
- Documentation of every model and definition
You end up with: A trusted warehouse and the first dashboards leadership can rely on.
- New domains and sources added against the same model
- Monitoring, cost management and performance tuning
- Access governance and periodic quality reviews
- Handover to your data team
You end up with: A data platform the whole business trusts — and AI-ready history.

Not sure which service you actually need?

Describe the problem in thirty minutes and a solution architect will tell you — including when the answer is that you do not need us for this.

Book a call

Tools we use

Data tools chosen for reliability

Open standards and widely adopted tools your analysts and engineers already know.

Warehouse
- PostgreSQL
- Cloud warehouse(BigQuery / Redshift / Synapse)
Pipelines
- Python
- dbt / orchestration tooling
BI
- Power BI
- Metabase
- Looker Studio
Cloud
- AWS
- Microsoft Azure
- Docker

Why us

Why partner with Kaidova on data engineering

Our own platforms generate operational data for real businesses; we build the reporting they run on. That experience shapes how we build yours.

Definitions before dashboards
We agree what 'revenue' and 'margin' mean across teams before building anything — that is where most data projects fail.
Reconciled to source
Every first dashboard is checked against the system of record and signed off by finance.
Governed from day one
Lineage, access and quality checks are built in, not added after the first audit finding.
A number before you commit
The audit ends with a fixed estimate and a measurable first deliverable.
Built to be handed over
Models, pipelines and definitions documented so your analysts can extend them.

Our work

Results from systems in production

How disciplined engineering translates into operational outcomes. Every figure shown is measured on a live system and approved by the client.

View all case studies

01Lieferfair
Marketplaces & Logistics
Rebuilding a delivery marketplace to survive its own growth
A fast-growing delivery platform was hitting the limits of its original architecture. We re-engineered the core system for scale, reliability and real-time operations.
Read case study
3.2×
Increase in order throughput
02Construction contractor (on Oplyra)
Construction & Trading
One ledger for a contractor running six disconnected systems
Read case study
40%
Less time closing the month
03FastestVPN
Consumer Software
Scaling a consumer VPN to millions of sessions a day
Read case study
99.9%
Uptime across regions