Operate intelligently
Business intelligence, executive dashboards, and semantic models people actually trust — with the data quality and governance behind them.
Because each report defines the measure differently. The fix is a semantic model: one certified definition of revenue, margin, or utilization that every report inherits. Once the definition lives in one governed place instead of in each analyst's query, reconciliation meetings stop and the argument moves on to what the number means.
Problems we solve
What we build
Each of these is a definition rather than a label — if a term below is not what you thought it meant, that is worth a conversation before a proposal.
How we work
The same four steps on every engagement. Nothing is scoped until the current state has been checked against the running system rather than taken from a report.
We check the running systems and the live data ourselves before agreeing what to build, because a status report or a closed ticket is a claim rather than a measurement. One to two weeks.
We count how many users, records, and configurations each problem actually touches, and that count sets the order of work rather than how large the fix looks. We try to refute our own findings first, and tell you which ones did not survive.
Before building starts you get a written scope that separates what you asked for, what we chose, what we are assuming but have not proven, and what we are deliberately leaving out. Nothing starts on an assumption nobody agreed to.
Work arrives in slices, and each one carries a before-and-after measurement of the thing it was meant to change, because a passing test shows only that the test ran. Two to four weeks per slice, closing with a written record of what was verified.
Proof
Professional services
A certified semantic model and executive reporting layer that ended the reconciliation debate, replacing conflicting spreadsheets with one agreed set of measures.
View case study
Product names are used descriptively. We select against the problem, not against a reseller agreement.
One agreed set of measures, a semantic model behind them, and a dashboard leadership uses. Platform and warehouse work is quoted after a paid discovery.
Questions
A semantic model is the layer where a business measure is defined once — what counts as revenue, which records are excluded, how the period is bounded — so every report inherits that definition instead of restating it. It matters because without one, each analyst encodes their own interpretation in their own query, and the resulting disagreement looks like a data problem when it is a definition problem.
By separating reports the business certifies from reports individuals build for themselves, and being explicit about which is which. Certified reports have a named owner, a documented definition, and a review date. Personal reports are allowed and useful — the failure mode is not that they exist, it is that nobody can tell them apart from the official ones.
Documented lineage, defined ownership, consistent structure, and known quality. An assistant asked a question about your business will answer from whatever it is pointed at, confidently, whether or not that source is correct. AI does not tolerate ambiguous data better than a human analyst does — it just fails to mention that it noticed.
Usually yes, and that is normally the cheaper answer. Replacing a warehouse is a large project with a long payback, and most of the problems we are called about — conflicting numbers, stale reporting, no agreed definitions — are semantic and governance problems rather than storage problems. We will say so if a replacement is genuinely warranted.
It is designed into the model rather than applied per report, because per-report security is where leaks happen. Roles are defined against the data model, tested with representative users before release, and documented so a future change can be reasoned about. Where the rules are complex, we build a test that asserts them rather than relying on a spot check.
Next step
Describe the problem rather than the solution. We will tell you what we would do first, how long it takes, and what it costs.
We reply to every message, usually within one business day.
info@aqlyst.ai · (901) 232-2944
