Analytics that explain why not just what moved.
Dashboards that don't just show numbers — they explain them. Anomaly detection, drill-down narratives, and the SQL behind the slide.
- Typical duration
- 8–12 weeks
- Squad
- 2–4 senior engineers
- Starting at
- $32,000 / month
- Ad-hoc analyst requests removed
- −63%Ad-hoc analyst requests removed
- Time to detect an anomaly
- < 1 hrTime to detect an anomaly
- Metrics under governance
- 340Metrics under governance
Dashboards show what changed. Nobody has time to work out why.
Most analytics investment produces charts that raise questions rather than answer them. The expensive part is the human hours spent afterwards, slicing dimensions to find the driver behind a number. That investigation is mechanical enough to automate — and doing so is what turns a dashboard into a decision tool.
Sound familiar?
- Metrics that disagree between teams because definitions drifted
- Analysts spending most of their week on ad-hoc 'why' requests
- Anomalies discovered in the monthly review, weeks late
- Dashboards built, launched, and quietly abandoned
How we deliver data analytics.
Four phases, each with a written definition of done. You will always know which phase we are in and what has to be true to leave it.
Define the metrics
A governed metric catalog with one owner and one definition per metric, so 'revenue' means the same thing in every conversation.
Model the data
Tested, documented transformation layers with lineage — so a number on a slide can be traced back to the source row that produced it.
Detect & explain
Automated anomaly detection paired with driver analysis that decomposes a movement into its contributing dimensions.
Narrate
Written explanations attached to every chart — plain-language summaries with the SQL one click away for anyone who wants to check.
What is included.
Every engagement is scoped to your problem, but these are the capabilities we bring to the table.
Narrative dashboards
Every chart ships with a written explanation of what moved and why, generated from the underlying driver decomposition.
Anomaly detection
Seasonality-aware detection tuned to suppress the noise that trains people to ignore alerts entirely.
Driver analysis
Automatic decomposition of a metric movement across dimensions, ranked by contribution — the analysis analysts do by hand today.
Metric catalog
Governed definitions with owners, lineage, and change history, so metric drift becomes a reviewable event rather than a surprise.
Self-serve analytics
Natural-language querying over the governed semantic layer — answers stay consistent with the catalog rather than inventing new logic.
Embedded reporting
Analytics embedded directly in your product or internal tools, with row-level security enforced at the query boundary.
Technology we typically reach for.
Chosen per engagement against your constraints — never because it is the fashionable choice this quarter.
- dbt
- Snowflake
- Python
- Claude
- Airflow
- Next.js
What this looks like in production.
Predicting equipment failure 72 hours out
Vertex Manufacturing
- Problem
- Unplanned downtime cost the company an estimated $42M annually across 14 production lines.
- Solution
- A multimodal model combining SCADA telemetry, vibration sensors, and floor-camera vision to forecast failures.
- Outcome
- False-positive rate dropped to 9%, downtime reduced 34%, and OEE improved by 6 percentage points.
- False-positive rate
- 9%False-positive rate
- Downtime reduction
- 34%Downtime reduction
- OEE lift
- +6 ptsOEE lift
Capabilities that pair well with this one.
Tell us what you're trying to solve.
A 30-minute call with a senior engineer — no SDRs, no discovery deck. You will leave with an honest read on whether this is the right capability and what it would take.