From the warehouse to the executive — in plain English
Most enterprises have a data lake, a BI tool, and a backlog of dashboard requests measured in months. The bottleneck is not the technology — it is the friction between the question a leader asks and the answer the data platform produces. We rebuild data platforms as lakehouses with a semantic layer and a natural-language interface on top, so that the time from question to defensible answer collapses from weeks to seconds.
What we deliver
Natural-language analytics
Business users ask questions in their own words — 'top ten branches by deposit growth quarter on quarter, excluding the new openings' — and receive a live answer with the SQL, the chart, and the data lineage exposed for audit. Backed by a semantic layer your data team controls, so the answer is correct and consistent across every user.
Modern lakehouse architecture
Snowflake, Databricks, or BigQuery as the storage and compute layer with dbt for transformation, an explicit semantic layer for metric definitions, and a governance plane for access control, lineage, and PII classification. The same platform serves BI, ML, and AI workloads — not three siloed stacks.
Real-time streaming pipelines
Kafka and Flink or cloud-native equivalents move operational data from source systems to the lakehouse with sub-second latency. The same platform handles batch and stream — no separate Lambda architectures with reconciliation headaches. Operational dashboards refresh as fast as the source systems update.
Self-service BI done properly
Power BI, Tableau, Looker, or Metabase as the user-facing tool, sitting on top of a governed semantic layer that ensures every dashboard uses the same definition of revenue, the same fiscal calendar, the same customer hierarchy. Self-service without governance is chaos; governance without self-service is a queue.
Data governance and lineage
Automated data cataloguing, column-level lineage from source to dashboard, PII detection and classification, access policies as code, and audit logs your compliance team can actually use. Built on open standards — OpenLineage, OpenMetadata, dbt docs — so you are not locked into a single vendor.
Embedded AI insights
Anomaly detection on every key metric, forecasting on every time series, automated narrative generation that explains what changed and why, and a question-answering layer over your data dictionary. The analytics platform actively surfaces what matters rather than waiting to be asked.
NL-to-dashboard live
How a query actually flows.
A real trace through the sovereign stack. Six stages, ~1.4 seconds end-to-end, zero packets leaving your perimeter.
How we deliver
Data and use-case audit
Three-week audit covering data sources, quality, current reporting estate, user pain points, and the business questions that are not being answered today. Output is a prioritised use-case backlog, a target architecture, and a credible estimate.
Platform foundations
Stand up the lakehouse, ingestion pipelines, semantic layer foundations, governance plane, and developer experience. Engineering-grade from day one — version-controlled, peer-reviewed, environment-promoted — so your team can keep extending it.
First waves of insight
Deliver the prioritised dashboards in incremental waves, with the first usable ones live by week six. Each wave includes the data model, the semantic-layer additions, the dashboards themselves, and the user enablement to drive adoption.
Enable natural language
Once the semantic layer covers the priority domains, layer the NL interface on top. Train it on your terminology, evaluate against a question set your business owners have written, and roll out with a feedback loop that lets you keep tuning.
Operate and evolve
Ongoing platform operations: pipeline reliability, semantic-layer governance, dashboard hygiene, and continuous addition of new domains. Quarterly business reviews tie platform investment to business outcomes.
Analytics & BI across every sector
The stack we build on
Lakehouse and warehouse
Transformation and pipelines
BI and visualisation
Semantic and AI layer
"Our CFO used to spend his Monday mornings reading a forty-page management pack. He now spends them asking questions of a chat interface that draws live charts from our Snowflake. The narrative is auto-generated, the numbers tie, and we have killed an entire team's worth of slide-making."— Head of FP&A, Listed Consumer Manufacturer
How we work together
Common questions
We already have Power BI or Tableau. Why would we need you?+
Often the tool is fine and the problem is upstream — no semantic layer so every report invents its own definition of a metric, no data quality monitoring so trust is fragile, no governance so the estate has sprawled into thousands of reports nobody owns. Our BI modernisation engagements typically keep the visualisation tool and rebuild what sits underneath it. The result is the same tool, better trusted, used more.
How long does a lakehouse build actually take?+
A production-ready lakehouse with five-to-ten source systems, a governed semantic layer, and a first wave of dashboards takes twelve to sixteen weeks. We ship incrementally — the first dashboards are usable by week six and value accrues from there. Larger estates with more sources, regulatory constraints, or migration from legacy warehouses run longer; we will not promise twelve weeks for a job that needs twenty-four.
What does natural-language BI actually mean in practice?+
Business users type a question in their own words and get a chart or table back with the underlying SQL and the data lineage visible. The system works because it sits on a curated semantic layer your data team owns — so 'revenue' means the same thing every time. Without that foundation NL-to-SQL is a coin flip; with it, accuracy on real business questions is typically above ninety percent.
Snowflake or Databricks?+
Both are excellent and the right answer depends on your workload mix and your team's centre of gravity. Snowflake wins on pure BI and SQL-first analytics simplicity. Databricks wins on heavy ML and unified batch-plus-stream engineering. We are accredited on both and have built large-scale platforms on each. If you have neither today, we will recommend based on your three-year roadmap, not our preference.
How do you handle data governance and lineage?+
Automated cataloguing of every dataset, column-level lineage from source system through transformation to dashboard, PII detection and classification feeding access policies, and an audit log of every query against sensitive data. Built on open standards — OpenLineage, OpenMetadata — so you can swap components without rewriting policies. Compliance gets the report they need; engineers get the lineage they need to debug.
Can we keep the data on-premise?+
Yes. We build lakehouses on cloud platforms most often, but for clients with regulatory or sovereignty constraints we deploy on-premise stacks based on Apache Iceberg or Delta Lake, Trino or Spark for compute, and your choice of BI tool. The patterns are the same; the operational burden is higher and we are honest about that trade-off.
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