AI Analytics Platform
Claravance Insights - Analytics That Answer the Question
Claravance Insights is an AI analytics platform that turns operational data into decision-ready answers: automated recurring reports, natural-language querying for non-technical teams, and narrative summaries that explain why a number moved rather than only displaying that it did.
What It Does
Dashboards were supposed to democratize data. In practice they produced a new bottleneck: forty charts nobody interprets the same way, and a queue of Slack messages asking the analytics team what changed. Insights closes the last mile between a number and a decision. It doesn't just show that revenue fell four percent; it identifies where, since when, and which segment drove it.
Automated Reporting
Recurring reports built once and delivered on schedule to the people who need them - replacing the Monday-morning spreadsheet ritual that consumes analyst hours and produces stale numbers.
Natural-Language Querying
Ask in plain language: "How did retention change by segment last quarter?" No SQL, no dashboard archaeology, no ticket. Answers come with the figures and their source so they can be checked.
Narrative Summaries
Written explanations of what changed and the most probable drivers, generated from live data - the format executives actually read, as opposed to the format analysts enjoy building.
Trend and Anomaly Detection
Surfacing shifts that nobody thought to chart, which is where most of the value in analytics has always lived.
Who It's For
Organizations with plenty of data and too few analysts
Non-technical teams currently dependent on a reporting queue
Leadership teams who want explanations, not exports
Analytics teams who'd rather do analysis than serve routine report requests
Deployment and Security
Live in two days with connectors for your CRM, finance, product, and support data
SSO, role-based access control, audit logging, and data-residency options
Never used to train models shared with other customers
Ask your data a question right now.
Bring one reporting question your team keeps asking. We'll answer it live.
Questions It Should Answer Well
Typical patterns:
Which customers reduced spend this quarter, and by how much?
What changed in our margin, and which product line drove it?
Are we on track against the quarterly target, and what would have to happen to close the gap?
Which team's cycle time deteriorated, and since when?
Show me every account with declining usage and an upcoming renewal.
Each of these returns the figures alongside the answer, with the source visible - so the number can be checked rather than taken on trust.
What It Can and Cannot Tell You
Being clear about this builds more trust than overclaiming, and it prevents the disappointment that kills renewals.
It can compute accurately from your data, surface changes and anomalies you didn't think to look for, identify which segments and dimensions correlate with a shift, and explain the arithmetic of a movement - which components combined to produce the change.
It cannot establish causation. Knowing that churn rose in one region while a price change happened in the same quarter is correlation. Distinguishing cause from coincidence needs domain knowledge, and sometimes an experiment.
We describe generated explanations as strong starting points for investigation rather than conclusions, because that's what they are - and because a tool that overclaims on causation loses credibility the first time a leader acts on a spurious correlation.
Where It Fits Alongside Your Analytics Team
Analysts don't lose work, they lose the queue. Routine reporting requests - the ones that consume analyst hours and produce no new insight - get self-served. What remains is the work that genuinely needs an analyst: experiment design, causal investigation, modeling, and the questions nobody has framed yet. Teams that adopt tools like this generally get more from their analysts rather than fewer of them, and it's worth saying so plainly to the analytics leads who'll be asked to evaluate the purchase.
Frequently Asked Questions
It can identify which segments and components drove a movement, and explain the arithmetic of the change. It cannot establish causation - distinguishing cause from coincidence requires domain knowledge and sometimes an experiment. Treat generated explanations as strong starting points for investigation.
It removes the routine reporting queue, not the analysts. What remains is the work that genuinely needs them: experiment design, causal investigation, and modeling. Teams typically get more value from their analysts rather than needing fewer.
Traditional BI shows you charts and leaves interpretation to you. Insights answers questions in plain language, explains what changed and why, and delivers narrative summaries - so the analysis step doesn't require an analyst.
No. Insights connects directly to your source systems, so you can start without a warehouse project in front of you. A warehouse does improve results once the questions span many systems or reach back over long histories - but that's an upgrade you make when the questions demand it, not a precondition for getting value. Where the data foundation is genuinely the constraint, our data engineering team can address it.
Answers are computed from your actual data, and every figure is traceable to its source so it can be verified. Narrative explanations identify probable drivers based on the data available - they're a strong starting point for investigation, not a substitute for domain judgment on causation.
That's the design goal: plain-language questions and written answers rather than query builders and joins. The practical test during a pilot is whether the people who currently raise reporting tickets stop raising them - we'd rather you measure that than take the claim on trust.
It removes the routine reporting load so they can do the analysis only they can do. In practice, teams that adopt tools like this get more value from their analysts, not fewer analysts.