A4 — Applied Intelligence
Intelligent Analytics
Data that explains what matters
Applied domain A4
More dashboards don’t mean better decisions. We design analytical systems that turn complex, fragmented data into information people can actually use — combining data engineering, analytics, visualisation and AI.
An insight that arrives after the decision is only a report.The answer has to arrive while there is still time to act.
- 01What happened?
- 02Why did it happen?
- 03What is happening now?
- 04What happens next?
- 05And most importantly — what should we do about it?
The real problems
The real problems → Our approach
- 01
Many dashboards, many truths
The same KPI is calculated differently in finance, sales and operations. Meetings are spent reconciling numbers instead of acting on them.
Our approachOne definition per metric
A semantic metrics layer defines each KPI once — formula, filters, grain and owner — and every dashboard, report and question uses that definition.
- 02
Signals noticed too late
A drop in conversion or a rise in returns is spotted weeks later in a monthly review, when the chance to respond has already passed.
Our approachFrom what to why
Metrics are monitored continuously. When one moves outside its expected range, the system breaks the change down by region, product, channel and other dimensions, and shows which segments drove it.
- 03
Explaining is harder than reporting
Dashboards show that a number moved, not why it moved. Root-cause analysis depends on a handful of analysts slicing data by hand, so most questions are never answered.
Our approachForecasts with honest ranges
Forecasts are published with ranges rather than single numbers and back-tested against history every cycle, so planners know how far to trust them.
- 04
Insight that never becomes action
Even a correct insight changes nothing unless it reaches the person who can act, in the system where they work, with a clear recommendation.
Our approachAction where the work happens
Alerts and recommended actions reach the owner of each metric in the tools they already use, with the expected impact and the evidence behind them. Questions asked in plain language are answered from governed metrics, with the query shown.
The product for this domain
VARDA Lumen
Decision intelligence
Not just what happened. What to do next.
Decision intelligence on governed metrics: one definition per KPI, anomaly and root-cause analysis, forecasts and recommended actions with expected impact.
- Semantic metrics layer
- Anomaly detection and alerting
- Root-cause decomposition
- Forecasts with uncertainty
Illustrative scenarios
Illustrative scenarios
Scenarios are illustrative; they do not describe specific clients.
- 01
Retail margin under pressure
ContextA regional retail chain sees its gross margin slipping but cannot tell whether pricing, promotions, supplier costs or product mix is responsible. Each department brings its own numbers to the meeting.
OutcomeMargin is defined once in a shared metrics layer. Automated decomposition shows which categories and stores are driving the change, and category managers receive the findings with suggested actions.
- 02
Demand planning with ranges
ContextA consumer-goods distributor plans stock from spreadsheet forecasts that are rarely checked against actual sales, and ends up with both shortages and excess inventory.
OutcomeForecasts are produced per product and region with ranges and back-tested every cycle. Planners concentrate on the items where uncertainty is highest.
- 03
Answers without a ticket queue
ContextIn a mid-sized insurer, managers wait days for the analytics team to answer routine questions about claims and renewals.
OutcomeManagers ask in plain language and receive answers drawn from governed metrics, with the underlying query visible. Analysts spend their time on the questions that genuinely need them.
Compliance & governance
Compliance & governance
- Designed to support KVKK requirements through role-based access, row-level security and masking of personal data in every view.
- Designed to support auditability: every metric has a documented definition, an owner and lineage back to its source.
- Designed to support the controlled use of language models: questions and generated queries are logged, and answers are limited to data the user is entitled to see.
- Designed to support GDPR requirements where analyses include data about people in the EU.
Frequently asked questions
Frequently asked questions
01We already have a BI tool. Do we need to replace it?
No. The metrics layer and analytics services can feed the BI tools you already use, such as Power BI, Tableau or Superset. What changes is that every tool reads the same definitions, with anomaly detection, forecasting and alerting running behind them.
02How reliable are answers to natural-language questions?
The language model does not calculate anything itself. It translates the question into a query against governed metrics, and that query is shown with the answer. If a question falls outside the defined metrics, the system says so instead of guessing.
03How are recommendations decided?
They are designed with the people who own each metric: which actions are possible, what they cost and how their effect has been measured before. Every recommendation shows its expected impact and the evidence behind it, and the decision stays with a person.
04What do we need in place before we start?
Access to the source systems and a small set of metrics that matter to a real decision. Data-quality problems will surface along the way; the platform is designed to expose and track them rather than hide them inside a chart.
Let’s design the right architecture for intelligent analytics.
Analytical systems that explain what happened, why, and what to do next, combining data engineering, governed metrics, forecasting and AI.