Finance reports one revenue figure, operations another, and auditors want to know who saw what. We build BI platforms on Power BI, Tableau, or Looker with one governed metric layer, row-level security, and audit trails that hold up in healthcare and finance.
We set up Power BI and Microsoft Fabric, Tableau, or Looker: workspaces, capacity, gateways, and licensing that fits your user count. Still choosing a tool? We can start with a BI strategy and tool assessment and move into the build with the same team.
Your metrics should live in one model, not in a hundred separate reports. We design star schemas and semantic models in Power BI, LookML, or dbt and document each definition with its owner. The same model feeds reports, Excel, and the AI assistants behind AI-powered analytics.
Reports are only as clean as the tables beneath them. We build data marts on your warehouse and the loads that fill them. When sources are messy or scattered, the heavier work moves into ETL development.
Some teams want interactive reports they can slice. Others need the same pixel-perfect PDF for the regulator every month. We build both, with scheduled delivery and templates that match your brand.
Business users should answer routine questions without filing a ticket. We set up certified datasets, workspace rules, and training. Self-service grows without turning into hundreds of conflicting reports.
When your customers need analytics inside your product, we set up embedded BI with tenant isolation and per-customer security. We also model license costs per tenant, so growth doesn’t come with a surprise bill.
We move reports from SSRS, Qlik, Cognos, or older Tableau setups to a modern platform. Old and new reports run side by side until every key total matches, and only then do users switch.
Slow reports usually come from the model, not the tool. We tune DAX and SQL, add aggregations, and set up incremental refresh. After that, we can stay on to support the platform and add new sources.
We review your data sources, current reports, and who uses them. You get a list of what to keep, fix, or retire.
We design the data layer, semantic model, security, and licensing, and price it all before the build starts.
Engineers build the data marts and the semantic model, with quality checks on every load.
We build the reports, set row-level security for every role, and test access with real accounts.
Key users compare report totals with source systems and sign off metric by metric. AI-powered testing handles the regression checks.
We train report builders and viewers, watch refresh jobs and usage, and add new sources when you need them.
Clinical, operational, and procurement reporting with HIPAA and GDPR controls built in, on the same groundwork behind our healthcare analytics projects. For an Austrian pharmaceutical procurement platform, we replaced overnight ETL with real-time analytics, and teams now run 650+ self-service reports a month.
Medical affairs, market access, and field team reporting on de-identified data. Our engineers built AI analytics for AstraZeneca’s Medical Affairs team under GDPR and HIPAA.
Loan, portfolio, and risk reporting with an audit trail behind every figure. For a Caribbean lender, we automated Loan Aging, Roll Forward, and Deferred Fees reports and cut processing time from 30 seconds to 3.
Generation, consumption, and billing reporting across sites and meters, with data marts fed by smart meter streams. Teams that need more than reports can grow this into a full energy analytics platform.
Product, revenue, and customer reporting for your team, plus embedded BI for your customers on one model. When customers need a fully custom analytics screen instead of an embedded report, that’s custom dashboard development.
Delivery, fleet, and warehouse reporting that joins order, telematics, and warehouse data in one model, with shared definitions for on-time rates and costs.
We’re ISO 27001:2022 certified, GDPR covered, and HIPAA-ready. Access controls and audit logs go into the BI design from day one.
We’ve built analytics for AstraZeneca and an Austrian pharmaceutical procurement platform, both under tight data protection rules.
The people who build your pipelines also build the model and the reports. Nothing gets lost between vendors.
We use AI-assisted engineering to cut delivery time by up to 50% and bugs by up to 20%. Seniors still review every release.
You get a scoped architecture and cost estimate within 14 days, before committing to the full build.
Employee retention is 96%. The engineers who build your model will most likely be the ones extending it next year.
Tell us which tools and sources you use today. We’ll show you what a governed BI setup would look like and what it’ll cost.
Most teams start with reports that disagree and a warehouse nobody fully trusts. A BI development project fixes the layers behind those reports:
To find your starting point, pick the three KPIs people argue about most. Ask finance and operations for last month’s value of each. If the answers differ, the project starts with the semantic model. If nobody can produce the numbers quickly, the source audit traces them with you, and you don’t need to prepare anything. On one Austrian healthtech platform, teams now run 650+ self-service reports a month from 8 source systems.
Five variables drive the estimate, and you can count most of them today:
Three numbers cover most of the figure: your source count, the reports people open every week, and the number of users who need access. If that list isn’t ready, the first call builds it with you. Within 14 days, you get a scoped, phased estimate, and the first phase can be small. To get a figure for your setup, send us those three numbers.
Timing depends mostly on where your data lives today. When your data already sits in a warehouse, the first reports usually ship within weeks. A full platform with new pipelines, a migration, and compliance controls takes several months. Either way, the work starts with a roadmap delivered in 14 days. Releases then come in phases, and priority reports go live first.
As a quick check, ask whether your core tables already sit in Snowflake, BigQuery, Databricks, or Azure. If they do, you’re on the faster path. If you’re not sure, the source audit answers that question in its first week. Relevant delivers 99% of projects on time.
For internal reporting with standard KPIs, a BI platform is faster and cheaper. Custom BI pays off in three cases:
To test your case, count how many external users would need access. Multiply that number by the per-viewer license price. Compare the yearly total with the cost of hosting a custom layer. If you don’t have those figures, discovery prices both options for you. When Power BI covers the need, the recommendation is to stay on Power BI.
A safe migration rebuilds the logic and runs both systems in parallel:
Before the project starts, export usage logs for the last 90 days and count the reports opened in that window. That count is your real migration scope. If logs aren’t available, the inventory step collects them for you. When the warehouse moves at the same time, both moves run as one plan alongside the data warehouse migration.
Many companies inherit a Power BI or Tableau environment without documentation. An inherited setup rarely calls for a full rebuild. The takeover starts with an audit of:
Layers that work stay in place. Weak ones get fixed first, usually slow models, duplicate datasets, and missing row-level security. Two quick checks: are your models in version control, and is each core metric defined in writing? If either answer is no, the audit starts there. If you can’t tell, the audit finds out for you. An Austrian healthtech platform’s analytics froze during peak bidding windows. The audit came first, then a new architecture with real-time analytics.
Security works best when it’s part of the data model from the first sprint. Run these checks on your current setup:
If you can’t answer some of these yet, the source audit covers all four. The same controls supported AstraZeneca’s Medical Affairs analytics under GDPR and HIPAA.
An AI assistant gives correct answers only when it reads from the governed semantic model, with each user’s permissions applied. Pointed at raw tables, the assistant returns confident numbers that don’t match finance.
To test your setup, ask the assistant for last month’s revenue and compare the answer with the finance report. A mismatch means the assistant bypasses your metric definitions. If no assistant is connected yet, the metric definitions come first and the AI layer follows. On an Austrian healthtech platform, a text-to-SQL layer built on governed data supports 650+ self-service reports a month. Median response time is under 15 seconds.
The choice depends on what your company already has:
A quick signal: count the people who currently maintain your reports. If the answer is zero or one, a dedicated team is safer. If you’re unsure, the 14-day roadmap includes a staffing recommendation.
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Tech label
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