AI-powered self-service analytics
Ask in plain English. Covirage chooses the calculation, runs it in deterministic code, and shows the figure with the rows behind it. No dashboard backlog, no analyst queue, no hallucinated totals.
A dashboard answers what someone anticipated. Self-service means the person with the question gets to ask the next one, and gets a number they can take into a meeting.
Filters, comparisons, rankings and time windows, understood the way your team says them.
Every figure is computed by our tools and checked. The AI model explains it; it never does the arithmetic.
One definition of revenue, coverage and share, set by ops and used by every answer.
Three steps, in this order.
Upload it, push it to the API, or drop it in storage on a schedule. One format, whatever the source.
Confirm the definitions. Ops owns them; everyone else inherits them.
Anyone on the team, any question, with the rows behind every figure.
Short answers. The Help centre has the long ones.
A chatbot writes the number. Covirage runs a tool that computes it and shows the rows. The model only chooses the tool and explains the result.
It says so, and says what data would be needed. It never fills the gap with a guess.
Every answer names the tools it called and links to the rows they used.
Written for this job: the measures, the data you already hold, and the arithmetic.
How an analytics product uses a language model without letting it touch a number: deterministic code computes every measure from stated definitions, a tool registry names each computation, the model plans which tools to call and reads their outputs, every output carries its citation, and the model's own text is labelled as a reading. The four boundaries that make this hold, what crosses each, and what never does.
16 Sept 20263 min readWhy 'real-time' is the wrong answer to how often sales data should be refreshed, the cadence each measure actually needs, weekly for coverage and pipeline, monthly for share of wallet and concentration, quarterly for norms and segments, annually for definitions, what a refresh more frequent than the measure's cadence does to the reader, and the export schedule that follows.
16 Sept 20262 min readWhat separates a question an AI analytics assistant answers correctly from one it answers plausibly: naming the measure, the level, the period and the comparison, asking for the table before the sentence, and checking the citation. Six rewrites of common questions, and the four kinds of question an assistant should refuse.
16 Sept 20263 min readA plain comparison for a commercial leader deciding what to buy: what self-service dashboards do, what an AI layer adds, the parts of the job that neither replaces, the two failure modes specific to AI analytics, generated numbers and unstated definitions, and the test that tells a trustworthy AI analytics tool from a fluent one.
16 Sept 20263 min readA practical checklist for the analyst or operations person who refreshes a coverage report each month from exports: what to export, in what order, the control totals to note before uploading, the validation lines to read after, the three failures that stop the refresh and what to do about each, and what to send the team when it is done.
16 Sept 20263 min readThe six lines a validation report on an uploaded sales file should carry before any measure is computed: the period it detected, the row and value totals against what the source showed, the columns it mapped and the ones it dropped, the roll-up identity at every level, the identifiers it did not recognise from last time, and the exceptions it will exclude. What each line catches, what a pass looks like, and why a report that says only 'upload successful' has told you nothing.
16 Sept 20263 min read