Definitions that live in people’s heads
Revenue means different things in different departments, so management reviews contain several totals.
Data & AI
EBTIKAR builds data platforms and governed reporting, and applies artificial intelligence where it measurably improves a business process.
- Data management Sources, quality and ownership
- Analytics Reporting and dashboards
- Applied AI Assistants and automation
Our approach
Decisions built on data you can defend
Most organisations do not lack data. They lack one agreed definition of a customer, a sale or a cost, and a reliable route from source systems to the report on the screen.
We begin with the questions the business needs answered, then trace each one back to its sources, definitions and owner. The first delivery is usually one governed metric rather than a platform, because credibility is built one number at a time.
AI work follows the same discipline. A model is introduced only where a measurable decision can improve, is evaluated against the current process, and runs with human review wherever the consequence of an error is significant.
A metric is a promise, not a chart.
Scroll to see how a definition becomes dependable.
Our standard
One definition, one owner, one number
Every metric carries its definition, source tables, owner and refresh schedule in a catalogue. When two reports disagree, the discussion is about the definition rather than about who exported what.
Ask for a data review Capabilities
Data and AI services we provide
Built in layers, so each one is useful before the next begins.
Common problems
Why the numbers disagree
None of these is solved by adding another dashboard.
Manual extracts
A monthly spreadsheet export, edited by hand, quietly becomes the source of truth for a year.
No ownership of quality
Nobody is accountable for a field being correct, so bad records persist and spread into every report.
AI bought before the data is ready
A model trained on contradictory records produces confident answers that cannot be trusted or explained.
Delivery
How a data project progresses
The order matters: questions, then sources, then models.
The track
The track
Questions
The decisions the business needs to make and the metrics they depend on.
Questions
The decisions the business needs to make and the metrics they depend on.
Platform areas
The layers of a data platform
Each tab describes what we build and how it is verified.
Sources under control
Ingestion, schema contracts and retention rules, so the platform knows what it holds and for how long.
- Schema contracts for each source
- Retention and access rules
- Lineage from report to record
Method
How we run a data engagement
One question answered properly is worth more than a broad programme that stalls.
Frame
Agree the decisions worth improving and the metrics they depend on.
Prove
Deliver one governed data product end to end, with a named owner.
Scale
Add sources and products on the same pattern, with tests and lineage.
Extend
Introduce AI only where a baseline exists to beat and a review step is in place.
Results
What changes when the numbers agree
The first improvement is usually a meeting that gets shorter.
Reporting time falls
Scheduled refreshes replace manual extracts, and the monthly pack is assembled from governed data.
Discussions move to the decision
With one agreed definition, meetings spend their time on what to do rather than which number is right.
AI with evidence
Any model in production has a measured comparison against the process it supports.
Next step
Talk to our data team
Name the question your reports keep failing to answer.
That question is a better starting point than a platform choice, and it usually shows which two systems need attention first.
