Business intelligence is only valuable when someone actually uses it to make a decision. We build the data platforms, semantic layers and reporting surfaces that move BI from a slide-deck exercise to part of how the business runs day-to-day.
The mistake most BI programmes make is starting with the platform — Snowflake vs Databricks vs BigQuery — before clarifying which decisions actually need better data. We start with the decisions and let the platform fall out of that conversation.
What we build
Modern data platforms
Lakehouse and warehouse architectures on Snowflake, Databricks, BigQuery, Synapse or the right managed substrate for the workload. Ingest pipelines, transformations and lineage built with the tools the data team will keep operating after we leave.
We are tool-agnostic but opinionated. The right choice depends on data volume, the team's existing skills, sovereignty constraints and the analytics workloads that matter. We document our reasoning so the choice is auditable a year later.
Semantic layer and metrics
A single, versioned definition of the metrics the business is run on — revenue, margin, retention, throughput — so the answer is the same in every dashboard, model and email.
This is the highest-leverage piece of any BI programme. Without it, the same KPI ends up with three different values across the company and the leadership conversation degrades into a data-quality debate.
Reporting and self-serve
Dashboards designed for the operator, not the analyst — fewer charts, clearer questions, faster path from observation to action. Self-serve for the teams that can use it, governed access for the data that needs it.
We borrow from product design as much as from analytics: each dashboard answers one question well rather than presenting forty charts in case someone needs them. The result is dashboards that get opened daily instead of monthly.
Embedded analytics
Analytics inside the operational tool where the decision is made — pricing within the CRM, scheduling within the planner, performance within the workbench — instead of a separate destination users have to remember to visit.
Embedded BI is where the next decade of analytics ROI lives. We build it on the same platforms that power the dashboards, with one semantic layer, one access model, one source of truth.
AI-augmented analytics
Natural-language query, narrative report generation, anomaly explanation. We use LLMs where they earn their place and we are honest with clients about where they don't yet.
How we work
We start with the decisions, work backwards to the metrics, and only then to the data and the platform. Most BI engagements that fail do so because they reversed that order.