Business intelligence that gives every team the same number.
We build dashboards and reporting systems on one shared set of metric definitions, so marketing, finance, and leadership stop arguing about whose revenue figure is correct and start acting on it.
What is a business intelligence solution?
A business intelligence solution turns raw business data into dashboards and reports people actually use to make decisions. It covers the data warehouse underneath, the metric definitions in the middle, and the visual reports on top — so a non-technical team member can answer a business question without asking an analyst to pull a custom report.
BI implementations return an average of 127% ROI within three years, and data-driven organizations are 23 times more likely to acquire customers than those relying on gut instinct. But 75% of BI project failures trace back to data quality, and 67% of organizations say they don't fully trust the data behind their own dashboards. A polished dashboard built on inconsistent data is worse than no dashboard — it just makes bad decisions look confident.
- Semantic layer
- One central definition per metric, shared by every report.
- Self-service
- Business users filter and drill without raising a ticket.
- Validated numbers
- Every figure checked against source data before it ships.
- Role-based access
- Each team sees the slice of data it is entitled to.
BI, data analytics, or machine learning?
All three deal with data. Each answers a different question.
| Service | What it answers | Best fit |
|---|---|---|
| Business Intelligence | "What happened, and what's happening right now?" — dashboards and reports on historical and live data. | Teams that need to see and act on business performance daily. |
| Data & Analytics | "Is the underlying data clean, connected, and reliable?" — the pipeline BI and ML both depend on. | Businesses whose data lives in silos and needs consolidating first. |
| Machine Learning | "What's likely to happen next?" — predictions, scores, and forecasts. | Fraud risk, churn probability, demand forecasting. |
Why your dashboards show three different revenue numbers.
The single most common BI failure we see — and it has nothing to do with the software.
The problem: every team defines the metric differently
Marketing counts revenue as gross bookings, finance excludes refunds, leadership expects deferred revenue treated separately. One company found 60% of its dashboards used inconsistent revenue definitions — some including refunds, some not, some using bookings instead of recognized revenue — and it took a six-month cleanup to fix.
The fix: a semantic layer, built in from the start
A semantic layer is one centralized definition for every core business metric that every dashboard, in every tool, pulls from. "Revenue" means the same number whether marketing, finance, or the CEO is looking at it. We build this layer into every multi-team BI project by default — not as an optional add-on discovered after the first argument in a leadership meeting.
Business intelligence systems we build most often.
Most BI projects need a working data pipeline before the dashboards can be trusted. We scope both together where needed, rather than building reporting on top of data nobody has verified.
Custom executive dashboards
Purpose-built dashboards for leadership, showing the handful of metrics that actually drive decisions, not everything available.
Self-service BI portals
Filterable, drillable reporting tools business users operate independently, without submitting a ticket for every new view.
Data warehouse & semantic layer
A consolidated data store with centrally defined metrics, so every report pulls from one trusted source, not five conflicting ones.
Embedded analytics
Dashboards built directly into your own product, so customers see their data without leaving your application.
Real-time & operational dashboards
Live-updating views for operations, support, or logistics teams that need current status, not yesterday's numbers.
KPI & performance reporting
Standardized reporting cadences and scorecards that replace manual, spreadsheet-built monthly reports.
A clear path from scattered data to trusted dashboards.
Four stages, with metric definitions settled before any dashboard gets built on top of them.
Metric & data source discovery
We map every data source, the metrics leadership actually needs, and where current definitions conflict.
1–2 weeks · DiscoverySemantic layer & data model design
We define each core metric once, centrally, before a single dashboard gets built on top of it.
1–3 weeks · DesignBuild & validate dashboards
We build in sprints, validating every number against source data before any dashboard ships to users.
3–10 weeks · BuildRollout & training
We launch with role-based access set up and walk your teams through self-service reporting.
1–2 weeks · RolloutThe business intelligence technology we use.
Proven BI and data infrastructure tools, chosen for the scope of your reporting needs.
Power BI
Tableau
Looker
Metabase
dbt
Snowflake
PostgreSQL
PythonProof, not promises.
Dashboards built on data teams actually trust.
BI · Full-Stack · Real-Time
A real-time admin dashboard replacing manual reconciliation
BeesApp needed live visibility into inventory, vendor performance, and order flow across a multi-vendor marketplace, previously tracked manually across disconnected systems. We built a real-time admin dashboard on Next.js and FastAPI, giving the operations team one live view instead of five conflicting spreadsheets.
Read the case studyExplore more software services.
Business intelligence is one of four services we cover under Data & Analytics.
Data Analytics Services
The pipelines every dashboard depends on.
ExploreDashboard Development
Working dashboards connected to live data sources.
ExploreData Visualization
Turning complex datasets into charts people read correctly.
ExploreData Reporting
Automated reports delivered on a schedule your team trusts.
ExploreMachine Learning
Predictions, not just reporting.
ExploreAI Development
The full range of AI systems we build.
ExploreCloud SaaS Apps
Infrastructure that scales BI workloads.
ExploreAPI Integrations
Connecting the source systems your warehouse reads from.
ExploreBusiness intelligence questions
The things clients ask us most before starting a BI build.
A business intelligence (BI) solution turns raw business data into dashboards and reports people actually use to make decisions, covering everything from the data warehouse underneath to the visual reports on top. It is the layer that lets a non-technical team member answer a business question without asking a data analyst to pull a custom report.
Data analytics is the broader pipeline: collecting, cleaning, and storing data so it is usable. Business intelligence is the reporting and decision-support layer built on top of that pipeline — dashboards, KPIs, and self-service tools business users interact with directly. Most BI projects need a working data pipeline before the dashboards can be trusted.
Use an off-the-shelf tool such as Power BI, Tableau, or Looker if your reporting needs are standard and your team can self-serve inside a licensed platform. Build custom if you need embedded analytics inside your own product, a semantic layer that enforces one metric definition company-wide, or a scale and cost profile the per-seat licensing of an off-the-shelf tool doesn't support.
This is common, and it happens when teams calculate the same metric differently — one dashboard including refunds in revenue, another excluding them. The fix is a semantic layer: one centralized definition for each business metric that every dashboard pulls from, so "revenue" means the same number everywhere.
Cost depends on how many data sources need connecting and how much of the underlying pipeline already exists. A focused executive dashboard on top of clean, existing data costs far less than a full data warehouse and semantic layer built from scratch. We scope your data sources first, then give a fixed price.
A single dashboard on top of existing, clean data ships in 3 to 6 weeks. A full data warehouse, semantic layer, and multi-team dashboard suite typically takes 10 to 16 weeks, depending on how many source systems feed into it.
Yes. Self-service BI is standard in every build we deliver: business users filter, drill down, and export on their own, without submitting a ticket for every new report.
Yes. You own 100% of the dashboards, data models, and semantic layer definitions in documented repositories, with no dependency on us to maintain or extend them.