Reporting nobody has to double-check.
Two dashboards disagreeing about revenue is not a dashboard problem. It is a modelling problem, and it will not be fixed by a third dashboard.
JANNEX builds data platforms and analytics: ingestion pipelines, warehouse and lakehouse modelling, transformation layers, business intelligence and dashboards, data quality and lineage, and the data infrastructure required before AI systems can be built on top.
Why the numbers do not agree
Because the definition of the number lives in several places, and each place is slightly out of date.
- The same metric calculated differently in three tools
- Pipelines that fail silently and are noticed a week later
- No lineage, so nobody can trace a figure back to its source
- History overwritten, so last quarter cannot be reproduced
- Personal data spread across environments with no classification
How we build data platforms
One definition of each metric, in one place, that everything else reads from.
Model deliberately
A warehouse structured around business processes, with a semantic layer where metric definitions live once.
Test the data
Freshness, volume, uniqueness and referential checks running as part of the pipeline, alerting before a report is wrong.
Keep history
Slowly changing dimensions and immutable raw storage, so any past state can be reconstructed.
Govern from the start
Classification, access control and lineage applied as the platform is built, not after an audit.
What this covers
Platform
- Warehouse and lakehouse design
- Batch and streaming ingestion
- Transformation layers
- Orchestration and scheduling
- Cost-aware storage design
Quality & governance
- Data testing and validation
- Lineage and cataloguing
- Access control and classification
- Retention policy
- Reconciliation with source systems
Consumption
- Semantic and metric layer
- Business intelligence and dashboards
- Self-service modelling
- Operational reporting
- Embedded analytics in products
AI readiness
- Document and unstructured pipelines
- Feature and embedding stores
- Permission-aware retrieval sources
- Evaluation datasets
- PII handling for AI workloads
Technology
The working set for this capability. Choices are made per engagement, against your constraints and your team's skills.
Where it is typically applied
Patterns we see repeatedly, described generically. Your version will differ in the details, and the details are the work.
One version of the numbers
A semantic layer so finance, operations and product read the same definition of the same metric.
Operational reporting off the production database
Moved to a warehouse, so analysis stops competing with customers for the same connections.
Preparing for AI
Documents inventoried, cleaned, permissioned and indexed — the work that determines whether an AI project is weeks or quarters.
Regulatory and audit reporting
Reproducible figures with lineage back to source records and a retained history.
How the engagement runs
The same seven stages, scoped to the size of the problem.
Understand
We start with the constraint, not the feature list. What breaks today, who it affects, what it costs.
Define
A written scope with the trade-offs made explicit — what is in, what is deferred, what we will measure.
Design
Interfaces, data models and system boundaries designed together, because they constrain each other.
Build
Short cycles against a working environment. Reviewed code, tests where they earn their keep.
Launch
Staged rollout with monitoring in place before traffic, not after the first incident.
Learn
Instrumented usage read against the thing we said we would measure at Define.
Scale
Performance, cost and operations tuned once real load has told us where the pressure is.
Questions we are asked
Do we need a warehouse, or is a database enough?
If reporting runs against the production database and either side is slowing the other, or if you need to combine sources, it is time. Below that, a well-indexed read replica is often the honest answer.
How does this connect to AI?
Directly. Retrieval systems are only as good as the content behind them. Inventory, cleaning, permissions and indexing are the same groundwork, which is why we treat them as one programme.
Can you work with our BI tool?
Yes. The modelling and semantic layer matter more than the visualisation tool, and we would rather improve what your team already knows than add another licence.
Have something worth building?
Tell us the constraint you are working against. If we are not the right people for it, we will say so.
Or write to connect@jannex.in