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10 bd
To start engagement
From signed scope to the first sprint
Every sprint
Working dashboard on a preview URL
Not a status deck describing one
Fixed scope
Agreed before we build
So the request list doesn't grow mid-sprint
100%
Pipeline and code yours
In your own cloud and BI accounts
What BI actually solves
The problems a business intelligence platform has to solve
Better tools, more data, and organization-wide systems give a business a real competitive advantage — but only once three specific problems are solved. Skip any one of them and the platform ships anyway; it just doesn't get used.
Data demand outpaces ad hoc reporting
Every part of the business generates more data every quarter, and a system built to answer last year's questions with a spreadsheet and a weekly export falls behind fast. Without a pipeline built to scale with volume, the reporting that used to take an afternoon starts taking a week.
Raw data isn't insight
A warehouse full of clean tables answers nothing on its own. Someone still has to decide what a metric means, how it should be visualized, and which comparison actually matters — that's the analytics layer, and it's a design problem as much as an engineering one.
Insight nobody acts on is a report nobody reads
A dashboard is only worth building if it changes a decision. The most common failure mode in BI isn't bad data, it's a chart that's technically correct and operationally useless because it doesn't map to a choice someone actually makes.
Cross-departmental access, or it isn't really BI
Business intelligence depends on data from sources inside and outside the organization — sales, finance, support, product usage, sometimes a vendor API. A platform that only sees one department's data answers one department's questions, which is a dashboard, not a BI system.
Adoption dies in an unintuitive interface
A platform that's technically powerful and confusing to open gets used once, by the person who requested it, and then quietly abandoned. Intuitive design isn't a nice-to-have here — it's the difference between a system people check every morning and one that gets rebuilt in a spreadsheet within a quarter.
Off-the-shelf platforms optimize for a market, not your org
A general-purpose BI tool is built to serve thousands of companies at once, which means its defaults are generic by design. That's fine for standard reporting and a real cost the moment your metrics, hierarchies, or data sources don't fit the tool's assumptions.
Build vs. buy
A licensed BI tool, a custom platform, or both
This is the decision the old version of this page never actually made. Here's how we walk through it with a client before writing any code.
When a licensed tool is the right call
Tableau, Power BI, and Looker are mature products with strong visualization, broad connector libraries, and a large hiring pool of people who already know them. For standard reporting against common data sources, buying is usually faster and cheaper than building the equivalent from scratch.
When custom development earns its keep
Once your metrics, data sources, or user roles don't map cleanly onto a licensed tool's model — or the tool's per-seat structure becomes the actual constraint on who gets access — a purpose-built platform stops being over-engineering and starts being the cheaper long-term answer.
Most real BI work is both, at once
A common shape: Tableau or Power BI as the visualization layer, sitting on top of a custom-built pipeline and warehouse that does the hard part — pulling data from a CRM, an ERP, a product database, and a handful of vendor APIs into one consistent, queryable model.
The pipeline is the expensive part, not the chart
Wiring dashboards is the visible ten percent. Deduplicating customer records across three systems, handling schema drift when a source system changes, and deciding what happens when two sources disagree about the same number is the other ninety, and it's where a BI project actually succeeds or fails.
Predictive analytics is a separate decision from reporting
A dashboard describes what already happened. Predictive analytics — forecasting demand, scoring churn risk, flagging an anomaly before it becomes an incident — is a different kind of build, usually a model trained on the same warehouse, and it's worth scoping separately rather than bundling into a first release.
Mobile BI is a real requirement, not a checkbox
A sales lead checking pipeline numbers between meetings or an operations manager checking a dashboard from the floor needs a mobile-first view, not a desktop dashboard shrunk to fit a phone screen. It changes what belongs on the first screen, not just the layout.
What we build
Business intelligence development services
The full scope, whether the engagement is a single dashboard rebuild or a platform covering the whole business.
Data pipeline and warehouse engineering
Extraction from every system that holds relevant data, a consistent schema, and a load process that keeps the warehouse current without someone running a script by hand.
Custom BI dashboard design and development
Dashboards built around the decisions your team makes daily, not a generic template — with the comparison and the time window that actually matters surfaced first.
Mobile business intelligence apps
A native or cross-platform view of the metrics that matter away from a desk, built for the specific workflows that happen on a phone rather than a shrunk desktop view.
Data analytics and reporting solutions
Scheduled reports, ad hoc query tools, and exports built for the people who need a number in a spreadsheet, not just a chart on a screen.
Predictive analytics development
Forecasting and scoring models trained on your own warehouse data, built to answer a specific business question rather than a general-purpose 'AI dashboard.'
BI platform and tool selection consulting
An honest read on whether Tableau, Power BI, Looker, or a custom build fits your data, your team's skills, and your budget — before anything is built.
End-user adoption analysis and management
Usage data on who actually opens the dashboard, training built around real questions people ask, and iteration based on what gets ignored.
How engagements are scoped
Business intelligence engagement options
What moves a BI quote is rarely the number of dashboards. It's how many source systems the pipeline has to reconcile and how clean the data already is when we start.
| Engagement | Commitment | Timeline | What's included |
|---|---|---|---|
| BI audit and platform recommendation | Fixed scope | 1 – 3 weeks | A review of current data sources, reporting gaps, and a recommendation on licensed tool, custom build, or both — yours to act on with or without us. |
| Dashboard build on an existing warehouse | Fixed scope | 3 – 6 weeks | Custom dashboards built on data you already have queryable, whether the visualization layer is Tableau, Power BI, or a custom front end. |
| Pipeline, warehouse, and dashboard build | Fixed scope | 6 – 14 weeks | The full stack: extraction from your source systems, a warehouse schema, and the dashboards on top of it, sized to how many systems are in scope. |
| Predictive analytics build | Fixed scope | 4 – 10 weeks | A forecasting or scoring model trained on your warehouse data, built around one specific business question rather than a general prediction engine. |
| Ongoing BI practice | Ongoing retainer | Ongoing | Standing ownership of the pipeline, the dashboards, and adoption — new data sources added and reports iterated as the business changes. |
A quote well under these bands is usually skipping the reconciliation work — the part where two source systems disagree about the same customer or the same number and something has to decide which one wins. That gap doesn't disappear; it shows up later as a dashboard nobody trusts.
How we build it
From discovery to a platform your team actually opens
The same overall sequence for every BI client, adjusted for how much of the environment already exists.
Discovery and data audit
What you're trying to achieve, what's currently blocking it, and an honest inventory of every system that holds relevant data — including the spreadsheet nobody officially counts as a system.
Workshopping the platform and the metrics
Working sessions to design the dashboards, the data model, and the integrations, with the people who will actually use the output in the room, not just the people who requested it.
Pipeline and warehouse development
The extraction and transformation work that gets data from source systems into a consistent, queryable model — the part of the build that determines whether the dashboards on top of it can be trusted.
Dashboard and analytics build
The visualization layer, built against the real data model rather than a mockup, so what ships matches what was designed rather than a simplified version of it.
Testing and organization-wide launch
Validation against known-good numbers before rollout, plus a launch plan that accounts for training rather than assuming people will figure the dashboard out on their own.
Audits and ongoing improvement
Usage review after launch — who's opening the dashboard, what's being ignored — and iteration based on that data rather than a fixed post-launch checklist.
How an engagement runs
From data audit to a platform your team owns
The same two-week delivery cadence as any build here: a working pipeline or dashboard you can query and click through, every increment, rather than a status update describing one.
Related
Related services
What a BI build usually touches on the way to a finished platform.
Questions
Common questions about BI development
What teams ask before a first call.
The full stack when it's needed: a data pipeline that pulls from every system that holds relevant data, a warehouse that makes it queryable, dashboards built around the decisions your team makes, and — where it's the right fit — predictive analytics trained on that same data.
Many engagements are narrower than the full stack. A dashboard rebuild on a warehouse you already have, or a pipeline project with the visualization layer left to a licensed tool, are both common starting points.
A licensed tool is usually the right call for standard reporting against common data sources — the visualization and connector work is mature, and building the equivalent from scratch rarely pays for itself. Custom development earns its cost once your metrics, roles, or data sources don't map cleanly onto the tool's model.
Most real projects use both: a licensed tool as the front end, sitting on a custom pipeline and warehouse. We'll say plainly which parts of your situation call for which, before scoping anything.
A dashboard describes what already happened — this quarter's revenue, this week's support volume. Predictive analytics is a forecast or a score: what's likely to happen next, or how risky a given account or transaction is, built from a model trained on your warehouse data.
It's a genuinely different kind of engagement from a reporting build, and we scope it separately rather than folding it into a first dashboard release, because the two have different timelines and different ways of being validated.
A dashboard build on a warehouse you already have is the fastest version of this work. A full pipeline, warehouse, and dashboard build is a larger project, and the number of source systems that need reconciling is what actually moves the timeline — not the number of charts on the final dashboard.
The audit exists to answer that question specifically, before either of us commits to a bigger number.
You do. The pipeline, the warehouse schema, the dashboard code, and the cloud and BI-platform accounts it all runs in should be registered under your organization from day one, with us added as a collaborator — not the other way around.
That's the only arrangement that lets your own team query the warehouse directly, add a new data source, or move to a different partner later without renegotiating access to data that was always yours.
Design around the questions people actually ask, not a generic template — the workshopping phase exists specifically to get the people who'll use the dashboard into the room before it's built, not after. After launch, we track who's opening it and what's being ignored, and iterate on that instead of guessing.
A platform that's technically correct and confusing to open gets used once and then quietly replaced with a spreadsheet. Adoption is a design problem we treat as part of the build, not an afterthought.
Yes — that's usually most of the pipeline work. Sales and customer data from a CRM, financial and operational data from an ERP, and whatever's already in your databases are the most common sources a BI project has to reconcile into one consistent model.
The harder part isn't connecting to each system; it's deciding what happens when two of them disagree about the same customer or the same number, which is exactly the kind of decision the audit surfaces before the build starts.
The warehouse is the queryable, reconciled copy of your data — the foundation. The dashboard is the view on top of it, built for a specific audience and a specific set of decisions. A good warehouse can support many dashboards over time; a dashboard built directly on scattered source systems, without a warehouse underneath it, tends to break the moment one of those systems changes.
Most projects that feel like 'the dashboard is wrong' are actually a warehouse or pipeline problem surfacing one layer up.