Understand what is driving business performance.
Know why a number moved and what to do about it, the same day. KPI drivers, variance analysis and root cause take you from the number that moved to the reason it moved.
What makes this hard today.
The dashboard shows the KPI is down. Finding out why takes three days, four analysts and a spreadsheet nobody else can reproduce.
KPIs reported without their contributing drivers
Variance explained differently by every department
Root-cause analysis performed manually, after the fact
Underperformance identified but never attributed
The cost of deciding blind.
A KPI without its drivers is a symptom without a diagnosis. Management time is spent establishing what happened instead of deciding what to do — and by the time the explanation is agreed, the window to act has usually closed.
Performance Intelligence
Veraius decomposes each KPI into the factors that move it, quantifies each contribution, and traces the movement back to the operational events and records behind it.
KPI decomposition
Each metric broken into the drivers that compose it.
Variance attribution
Plan-versus-actual gaps attributed to specific contributing factors.
Root-cause tracing
From the aggregate movement down to the underlying records.
Location comparison
Stores, plants or regions compared on a like-for-like basis.
Anomaly detection
Movements that fall outside expected behaviour, flagged as they occur.
Narrative explanation
The reasoning written out, with its sources attached.
Connect. Understand. Predict. Decide. Act.
The same pipeline runs behind every solution: your systems are connected, the data is put in business context, the outcome is predicted, the decision is reasoned with its sources attached, and the result is carried into workflow.
ERP, POS, MES, WMS, CRM and IoT — into one place.
Operational data related and put in business context.
Demand, stock, production and performance, ahead of time.
Decision models and AI weigh the options with you.
Decisions executed through workflow and automation.
What changes in the way you work.
Review meetings start from an explanation rather than build one
Variance is attributed consistently across departments
Underperformance is linked to an operational cause that can be acted on
Less analyst time spent reconstructing what already happened
Where it is used in practice.
Monthly performance review
Each KPI presented with its drivers and their quantified contribution.
Store or plant benchmarking
Why one location diverges from comparable ones.
Budget variance analysis
The gap between plan and actual, decomposed by cause.
Executive question answering
A direct question about performance, answered with sourced reasoning.
What your teams gain from Veraius agents.
Cash you can see ahead
Liquidity and working capital are projected on a rolling 30-day horizon, so a shortfall shows up before it happens.
See the Finance agents ManufacturingAnswers from the real query
Every figure comes from a query against your production data and links back to the records behind it.
See the Manufacturing agents FinanceBudget against reality
Planned and actual spend are compared by cost centre, and variances above your threshold are escalated with their cause.
See the Finance agentsIn every department
- Agents prepare the work on your own data; your people approve what happens next
- Plain-language questions answered from live ERP data, with tables and interactive charts
- Grounding check: numbers that cannot be traced to a source are not shown
- Explainability, confidence and risk scores with decision rationale
- Slack, Teams, WhatsApp and Signal channels with an approval inbox
- Department-based access policy with personal data masking
Frequently asked questions
Straight answers about integrations, deployment, security and cost — including where the honest answer is "it depends".
Ask your own questionWhat is performance intelligence for?
Finding the decisions behind a number that moved. A margin drop reported at month end is a fact; which product, which store and which pricing or replenishment decision produced it is what someone can act on, and that chain is what this connects.
How is this different from a BI dashboard?
A dashboard shows the metric and leaves the attribution to the reader. That works when there is one obvious cause and fails when there are four interacting ones, which is the normal case. The difference is not visualisation; it is that the decisions are recorded, so the chain from cause to number exists to be traced.
Does it need the other solutions to be in place?
It gets substantially better with them, because a recorded decision is far easier to attribute than an inferred one. But it works against historical data alone, and that is often how organisations start — the first thing it surfaces is usually a pattern nobody had the time to look for.
Who uses performance intelligence day to day?
The people accountable for the numbers rather than the analysts who prepare them. If a category manager needs a request to an analytics team to find out why margin moved, the answer arrives after the decision window has closed.