Data platforms
Warehouse and lakehouse platforms sized to your scale, with storage and compute managed for cost.
- Lakehouse and warehouse architecture
- Medallion and dimensional modelling
- Platform migration from legacy BI
Data Engineering & Intelligence
Data your people and your AI can trust.
We build the pipelines, platforms and governance that turn fragmented data into a reliable foundation for reporting, decisions and AI.
Overview
Data Engineering & Intelligence
Every AI initiative eventually runs into the same wall: the data is incomplete, inconsistent or locked in systems nobody can easily query. Dashboards disagree with each other and analysts spend more time reconciling numbers than interpreting them.
We fix the foundations. Modern data platforms with tested pipelines, clear ownership and quality monitoring mean the numbers line up, new questions can be answered in days and AI systems have the context they need.
Business challenges
Problems we are usually asked to solve
01
Conflicting numbers
Finance, sales and operations each report different figures for the same metric.
02
Data that arrives too late
Overnight batch jobs mean decisions are made on yesterday's information.
03
Silent pipeline failures
Broken loads go unnoticed until a stakeholder spots a wrong number in a board report.
04
Not ready for AI
Documents and records are unlabelled, duplicated and lack the permissions model AI retrieval needs.
Our solutions
What we build and deliver
Pipelines and integration
Batch and streaming pipelines with tests, lineage and alerting built in.
- ELT with version-controlled transformations
- Change data capture and streaming
- API and SaaS connectors
Governance and quality
Practical governance that improves trust without slowing teams down.
- Data catalogue and lineage
- Quality checks and observability
- Access control and privacy classification
Analytics and intelligence
Dashboards, semantic layers and predictive models that people actually use.
- Self-service BI and semantic models
- Predictive and prescriptive analytics
- Natural-language querying over governed data
Capabilities
Everything in scope
- Data strategy and consulting
- Data engineering
- Data pipelines and integration
- Data warehousing
- Data lakes and lakehouse architecture
- Business intelligence
- Predictive analytics
- Data governance
- Data quality and observability
- Real-time data processing
- Master data management
- AI-ready data preparation
Technologies
Tools we work with
- Platforms
- DatabricksSnowflakeMicrosoft FabricBigQueryRedshift
- Pipelines
- dbtApache AirflowDagsterFivetranApache Spark
- Streaming
- Apache KafkaKinesisFlinkDebezium
- BI & quality
- Power BITableauLookerGreat ExpectationsMonte Carlo
Technology choices are made per project based on your environment, skills and constraints.
Industry applications
How this applies in practice
Retail & E-commerceUnified customer and inventory data powering personalisation and replenishment.Energy & UtilitiesReal-time sensor and meter data for load forecasting and asset monitoring.Insurance & InsurTechClaims and policy data platforms supporting pricing and fraud analytics.Logistics & Supply ChainShipment event streams for live visibility and ETA prediction.
Engagement approach
How an engagement runs
- 01
Data assessment
Sources, quality, flows and reporting needs mapped against business priorities.
- 02
Architecture
A target platform design with a migration path that avoids a big-bang cutover.
- 03
Build by domain
Data products delivered domain by domain, each with owners and quality checks.
- 04
Adopt
Training, documentation and support so teams trust and use the new platform.
Should we use a data warehouse or a lakehouse?
Warehouses suit structured reporting workloads. Lakehouses add flexibility for semi-structured data, data science and AI. Many organisations benefit from a lakehouse with warehouse-style modelling on top. We recommend based on your workloads and skills.
How do you make data AI-ready?
By cleaning and deduplicating sources, adding metadata and permissions, chunking documents sensibly and building pipelines that keep AI indexes in sync with source systems.
Can you work with our existing BI tools?
Yes. We connect to the tools your teams already use and only recommend changes where they solve a real problem.
How do we know the data is correct?
Automated tests run on every pipeline, freshness and volume are monitored, and anomalies trigger alerts before stakeholders see bad numbers.
Start a conversation
Tired of arguing about whose number is right?
Let us help you build one version of the truth.