Analytics and BI built around the decisions your teams make.
We define the metrics, build the semantic layer and design dashboards and analysis that answer specific business questions — with one definition of each number.
- Metric definitions
- Semantic layer
- Decision-focused dashboards
- Self-service setup
- Analysis and findings
Where this helps
What we deliver
How it works
- 01
Start with decisions
We interview the people who use the reports and list the decisions they make, how often and with what information.
- 02
Define the metrics
We draft definitions, reconcile conflicting versions with each team and get sign-off from metric owners.
- 03
Model the data
We build or adjust the underlying data models and semantic layer so the metrics can be computed reliably.
- 04
Build and review
Dashboards are built iteratively and reviewed with their users in working sessions, not handed over finished.
- 05
Retire and maintain
Unused and duplicate reports are retired, and ownership and review cadence are agreed for what remains.
Design decisions we make with you
BI tool
We work with the tool you have where it is adequate. Where a choice is needed, licensing, governance features and your team's skills matter more than chart types.
Where logic lives
Business logic belongs in the data model or semantic layer, not duplicated inside individual dashboards.
Access and row-level security
Users see the data their role permits, enforced in the data layer rather than by hiding tabs.
Data freshness
Refresh frequency is set per dashboard according to the decision it supports, balancing currency against compute cost.
Applications
Related capabilities
- Demand ForecastingForecast demand by product, location and week, with the uncertainty visible, so planners can make and explain their decisions.
- Sales & Marketing AssistantsResearch accounts, draft tailored outreach and review content against brand rules, with reps and marketers approving everything that goes out.
- Data EngineeringData architecture, pipelines, quality and access that make your data usable for reporting, operations and AI.
- ETL & Data PipelinesBatch and event-driven pipelines that extract, transform and load data reliably, with testing, lineage and alerting built in.
- Machine Learning & Predictive AnalyticsForecasting, classification, anomaly detection and recommendation models trained on your data and evaluated against the decisions they support.
- CRM AutomationLead routing, follow-ups, record hygiene and sales or service workflows automated inside the CRM you already use.
Questions buyers ask
For anything beyond a single source, usually yes — or at least a modeled reporting database. If the data foundation is missing, we start there through data engineering.
Yes. Most projects improve definitions, models and dashboard design within the existing tool rather than replacing it.
Natural-language querying works well on top of agreed metric definitions and a clean semantic layer — without them, it returns confident but inconsistent answers. We build the foundation first.
From the decisions people make. Each dashboard has a named audience, a recurring decision and an owner; anything without those is a candidate to retire.
Discuss this capability with an engineer.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.