Business Intelligence and Analytics Services
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
Recent outcomes
AI OCR · Gas station operations
20,000+ transactions/day
Deployed an AI OCR pipeline that turns scanned vendor invoices into a live cross-location operational dashboard, replacing manual line-item retyping.
Loyalty platform · Utility provider (Energia)
Real-time visibility, zero blind spots
Live Metabase dashboards for registrations, logins, and redemptions, with alerts firing before a missing data file becomes a customer-facing problem.
The problem
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?
Short answer
RaftLabs builds business intelligence dashboards, KPI frameworks, and self-service analytics for operations and leadership teams in the US, UK, Europe, Canada, and the UAE. Every metric gets one documented definition in the data layer, so all dashboards read the same number from the same source. Fixed price, scoped in week one.
Key takeaways
Trusted by
The weekly report lands Monday morning as a PDF. The numbers are already five days old. The spreadsheet behind it pulls from three systems, takes two hours to assemble by hand, and breaks the moment someone changes a source format.
So the meeting opens with the wrong argument. Half of it goes to debating whether the numbers are right, not what to do about them.
Live data does not have that problem. The dashboard shows the same number to everyone, refreshed on its own schedule, sourced from one place.
The report was never the problem. The data underneath it was.
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. The failure mode is rarely an ugly dashboard. It's a believable, wrong one, five slightly different versions of the same KPI, each trusted because the chart looks polished.
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. RaftLabs has shipped production software since 2015 for clients that include Vodafone, Aldi, and Cisco. We scope SOC 2 and GDPR access controls into the data architecture from the start, not as an afterthought before launch.
These are our own delivery numbers, not a benchmark study. The gas station dashboards read from an AI OCR pipeline that turns scanned vendor invoices into structured transaction data. The Energia dashboards track registrations, logins, and redemptions in real time, with alerts that fire before a missing data file becomes a customer-facing problem.
In October 2020, Public Health England's automated test-result upload process used a legacy Excel file format capped at 65,536 rows. When a batch of positive results exceeded that limit, the excess rows were silently dropped, no error, no alert, just missing data. Roughly 16,000 positive test results went unreported to the contact-tracing system for several days, delaying contact tracing for people who had been exposed. Nobody wrote bad code on purpose. A file-format limit nobody had checked against real volume did the damage silently, which is exactly how bad data usually fails: not with a crash, with a number that's simply wrong and nobody notices until it matters.
The more expensive version of the same failure shows up in finance. JPMorgan's own task force review and the SEC's subsequent order both cited spreadsheet miscalculations that produced large valuation errors inside the trading desk's risk model, a documented contributing factor in a loss that reached $6.2 billion. A wrong number in a report is not an embarrassment. It's a decision made on the wrong information, and the cost scales with how much rides on the decision.
Everything on the left should already be true for your operation. Even one thing on the right, and a single one-off report is the smarter first step.
A $1M-$100M business whose leadership runs on weekly reports that are days old by the time they land.
Analyst hours going into manually assembling the same reports from multiple systems every cycle.
Existing data in a warehouse, database, or tools (Snowflake, BigQuery, PostgreSQL, MSSQL) you want a reliable reporting layer on top of.
What we build
Self-service analytics has two outcomes. Either everyone queries one governed set of metric definitions, or five people build five slightly different versions of the same KPI and the meeting is back to arguing about whose number is right. The difference is a semantic layer: a modeled set of business-concept tables, built in dbt, that every dashboard and every self-service query reads from.
We model metrics once, version the definitions in Git, and expose them through the BI tool so a non-technical user filtering a Metabase question is still reading the same "active customer" the CEO dashboard reads. Column-level lineage traces every figure back to its source table, so when a number looks wrong you can see exactly which upstream field produced it. Row-level security lives in the same layer, so each user sees only the data their role permits.
| Ungoverned self-serve | Governed semantic layer | |
|---|---|---|
| Metric definitions | Redefined in each dashboard and each ad hoc query | Modeled once in dbt, versioned in Git, read by every query |
| When two numbers disagree | Nobody can explain which is right or how either was calculated | Column-level lineage traces each figure to its source field |
| Access control | Bolted on per dashboard, easy to miss a gap | Row-level security enforced in the shared data layer |
| Failure mode | Dashboard sprawl, five versions of one KPI, eroding trust | One source of truth that self-service extends, not multiplies |
Natural-language querying is arriving in every BI tool. A department head types "show me churn by cohort for enterprise accounts" and a model writes the SQL. It only works if the semantic layer underneath it is sound. Point a large language model at raw, unreconciled tables and it will confidently generate a query against the wrong definition of churn and return a wrong number with no warning. Point it at a governed semantic layer and it inherits the agreed definitions, the lineage, and the access rules. We build the layer first, so LLM-assisted querying becomes a genuine accelerator rather than a faster way to produce a plausible wrong answer.
Tell us the reporting pain and what decisions it is blocking. We will scope a BI system that gives your leadership team the visibility they need.
How it works
Every BI project follows the same four phases. Scope is locked and price is fixed before development starts.
We map your data sources, reporting requirements, and the decisions your leadership team needs to make. You leave week 1 with a written scope document, agreed metric definitions, and a fixed-price quote. No development starts without your sign-off.
We design the underlying data model with clear, agreed definitions for every metric before building a single chart. Contested definitions - what counts as revenue, what counts as an active customer - are resolved in this phase, not discovered mid-build.
Working dashboards at a staging URL by the end of sprint one. Bi-weekly demos. Automated reports and self-service access are configured in parallel with dashboard development, not sequenced after it.
Production deployment with monitoring active on launch day. Training session for non-technical users. 8 weeks of post-launch support included in every project.
What clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
USAAll of the sprints were completed on schedule and on budget. We highly recommend RaftLabs!
Rarely the chart library. Always the decision made before anyone opens a design tool.
A dashboard is only as trustworthy as the definitions underneath it. Resolving what counts as an active customer or when revenue is recognized, before building anything, is what makes people believe the number on screen.
Handing department heads query access without permissions modeled at the data layer is how five different, unreconciled answers to the same question end up in five different decks.
When a shipped dashboard goes unused, the cause is almost never the design. It's that the numbers on it were never trusted in the first place, which traces back to a metric-definition step that got skipped.
A dashboard that goes quiet when a source feed fails looks fine right up until someone makes a decision on stale numbers. Failed-generation and missing-file alerts catch that before a customer or a board member does.
Where you land in that range depends on scope, not negotiation:
What it costs
A working executive dashboard on a reliable data layer, with automated reporting and self-service access configured in parallel, not sequenced after it.
Priced for a focused engagement, scoped in week 1. Many teams start with the executive dashboard, then add automated reporting and self-service access once the data layer is proven.
Start with the dashboard on your most critical metrics, then expand into automated reporting and self-service access once the data layer holds up under real use.
No hourly billing
Once we scope the engagement, that price is locked in writing. No hourly billing, no scope creep quietly added to the final invoice.
The team that scopes it ships it
The team that maps your data problem in week 1 is the team that ships the dashboard in week 10. No handoff after the contract is signed.
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Read moreFor 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 organization. Power BI is stronger for organizations already deep in the Microsoft ecosystem, Office 365, Azure, MSSQL, and handles more complex modeling through its DAX calculation language. Tableau has the strongest visualization capabilities and is the choice for data teams that need highly customized, publication-quality charts, but it comes with higher licensing cost and steeper learning curve. For organizations 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 teams, 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 organizations need both: dashboards for active monitoring and custom reports for scheduled delivery to decision-makers 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.
A focused engagement - discovery, data model design, and a working v1 executive dashboard on a reliable data layer - typically runs 6 to 10 weeks. That first slice is the thing you validate against real decisions, then expand. The full platform, with self-service analytics, automated reporting, and multiple departmental dashboards, grows to 12 to 20 weeks. We scope the work and give you a fixed price before any development starts. The most common range for a focused v1 is $15,000 to $40,000, growing as you add scope. We do not bill by the hour, so the number you see in week 1 is the number on the final invoice.
Yes. We sign NDAs before any discovery call where sensitive data is discussed. We have worked with financial data, operational metrics, patient records, and proprietary pricing models for clients in the US, UK, Europe, Canada, and the UAE. Access controls are configured at the data layer so each user sees only the data their role permits. For clients with SOC 2 or GDPR requirements, we scope those controls into the initial data architecture, not as an afterthought before launch.
Work with us
We scope Business Intelligence and Analytics in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
