Does your leadership team spend more time debating data accuracy than making decisions?
How many analyst hours per week go into manually assembling the same reports?
Your management team is making decisions on last week's data, pulled from a spreadsheet someone built in 2019.
The weekly report arrives Monday morning as a PDF. By then the numbers are five days old. The spreadsheet it was built from pulls data from three different systems, requires two hours of manual assembly, and breaks whenever anyone changes the source format. Half the meeting is spent debating whether the numbers are right, not what to do about them.
We build business intelligence dashboards and analytics systems that give your leadership team live operational visibility. Custom dashboards, automated reporting, and self-service analytics built on a single reliable data layer. Decisions made on current data, not reconstructed history.
Executive dashboards with live data that update automatically, not on someone's Monday morning schedule
Single agreed source of truth for every KPI so nobody debates the numbers in the meeting
Self-service reporting for department heads who need answers without waiting for an analyst
Automated report distribution that replaces manual data assembly and PDF email chains
RaftLabs builds business intelligence and analytics systems including executive dashboards with live data, KPI frameworks and metric definitions, self-service reporting for non-technical teams, automated report generation and distribution, custom visualizations for operational and financial data, and BI implementations on Power BI, Tableau, Metabase, or custom-built solutions. BI engagements are scoped at a fixed price after a discovery phase that assesses your data sources, reporting requirements, and current analytical infrastructure.
The decision support gap
Leadership teams in most $1M-$100M businesses have a data problem that looks like a reporting problem. The real problem is not the dashboard -- it is that the underlying data is inconsistent, assembled manually, and a week behind by the time it reaches the meeting.
Better reporting cannot solve a broken data foundation. We fix both layers: the data infrastructure that makes the numbers reliable, and the presentation layer that makes them accessible to the people who need to act on them.
What we build
Executive dashboards
Real-time dashboards for leadership teams that surface the metrics that drive business decisions. Revenue, margin, pipeline, operational throughput, customer retention, and unit economics -- configured around your specific KPIs. Updated automatically from your data sources without manual assembly. Designed for people who make decisions, not data analysts: clear, direct, filterable by period, region, product line, or any dimension relevant to your business. Built on Power BI, Tableau, Metabase, or custom, depending on your team's technical level and reporting needs.
KPI framework design
Before we build a dashboard, we define what goes in it. A KPI framework documents the metrics that matter for your business, how each one is calculated, which source systems it comes from, who owns it, and what a good or bad number looks like. This work surfaces disagreements about definitions before they become disagreements about dashboard accuracy. The framework becomes the spec for the data model and the guide for every chart we build. Organisations that skip this step end up with beautiful dashboards showing the wrong things.
Self-service analytics
Self-service tools that let department heads and analysts answer their own questions without submitting a request to the data team. Metabase or a similar tool connected to a curated data layer with clear table and column documentation. Training for non-technical users on query building. A governed data catalog that tells users what data is available, what each field means, and what questions each table can answer. The difference between self-service that works and self-service that produces wrong answers is a well-designed data layer with clear documentation.
Automated report distribution
Automated scheduled delivery of reports to stakeholders who need data but do not log into dashboards. Weekly operational summaries, monthly performance reports, daily exception alerts. Reports generated automatically from the data layer, formatted, and delivered via email, Slack, or your internal communication tools. Replaces the manual data assembly, formatting, and emailing process that currently consumes analyst hours every week. Same data source as the dashboards so every number matches.
Operational analytics
Department-level analytics for operations, finance, sales, and customer success teams. Sales pipeline analytics: conversion rates, average deal size, cycle time, win rate by segment and rep. Finance analytics: cash flow, receivables ageing, cost by department, margin by product. Operations analytics: throughput, utilisation, queue depth, cycle time. Customer analytics: churn rate, LTV, support ticket volume and resolution time. Built to answer the questions each team is actually asking rather than generic dashboards that nobody uses after the first week.
Custom data visualisation
Custom visual components for data that does not fit standard chart types. Geographic visualisations for location-based operational data. Network graphs for relationship mapping. Funnel and cohort visualisations for conversion and retention analysis. Time-series comparisons with configurable period overlays. Built in D3.js, Recharts, or ECharts and integrated into your dashboard or web application. For when the insight lives in a visual pattern that a bar chart cannot show.
What business question can your team not answer today because the data is not there?
Tell us your reporting pain and what decisions it is blocking. We will scope a BI system that gives your leadership team the visibility they need.
Related services
Data Engineering -- the data infrastructure that makes BI accurate
Reporting Automation -- automated generation of structured reports
MLOps -- operational intelligence for your AI systems
Predictive Analytics -- forecasting on top of your BI data layer
Custom Software Development -- full-stack applications with built-in analytics
Frequently asked questions
For most $1M-$100M businesses, Metabase and Power BI are the default starting points. Metabase is open source, inexpensive, and easy for non-technical users to build their own queries without a SQL background. It works well for operational dashboards and self-service reporting where the goal is making data accessible across the organisation. Power BI is stronger for organisations already deep in the Microsoft ecosystem -- Office 365, Azure, MSSQL -- and handles more complex modeling through its DAX calculation language. Tableau has the strongest visualisation capabilities and is the choice for data teams that need highly customised, publication-quality charts, but it comes with higher licensing cost and steeper learning curve. For organisations that want full control over the data layer and custom UI, we build dashboards on top of a data warehouse using a custom front end. We recommend the platform that fits your team's technical capacity, existing infrastructure, and budget -- not the platform with the highest margin for us.
Accuracy starts in the data layer, not the dashboard layer. Before we build a single chart, we design the underlying data model with clear, agreed definitions for every metric that will appear in the product. What counts as an active customer? When is a sale recorded -- at order, at invoice, or at payment? How are returns handled in revenue figures? These definitions are documented, agreed by the relevant stakeholders, and encoded into the data transformation layer. From there, every dashboard reads from the same underlying metric definitions. When the CEO dashboard shows revenue and the finance dashboard shows revenue, they show the same number from the same source because they are both reading the same metric. The debates in meetings shift from 'whose number is right' to 'what do we do about it.'
A BI dashboard is an interactive visual interface that displays key metrics, allows filtering and drill-down, and updates automatically as underlying data refreshes. It is designed for regular monitoring -- daily or weekly check-ins on operational health. Custom reporting is structured data output -- formatted tables, summaries, and calculations -- typically scheduled for delivery to specific recipients. Automated report distribution takes the reports that currently require someone to manually pull data and assemble them, and runs that process automatically on a schedule, delivering the finished report to the right recipients. Most organisations need both: dashboards for active monitoring and custom reports for scheduled delivery to stakeholders who do not log into dashboards. We build both and connect them to the same data layer so the numbers always match.
Yes. If you have an existing data warehouse, database, or data tool, we assess what exists and build on top of it where it is sound. If the existing data layer has quality issues or structural problems that would produce inaccurate dashboards, we address those first and tell you why before we start building the presentation layer. We have built BI layers on top of Snowflake, BigQuery, Redshift, PostgreSQL, MSSQL, MySQL, and custom data pipelines. The BI layer is separate from the data infrastructure layer. They do not have to be rebuilt together.