Insight. Intelligence. Impact.
Your AI-first Data Intelligence partner. We deliver end-to-end data solutions — engineered pipelines, business intelligence, interactive visualisations, and data science models that go into production and stay there.
Five disciplines. One integrated practice.
Most data teams treat these as separate silos. We treat them as one connected practice — because data engineering feeds your BI, your BI feeds your analytics, and your analytics feeds your decisions.
Data Visualisation
Interactive dashboards and reports that make complex data instantly readable. We design and build visualisations people actually use — not ones that get ignored after the first demo.
Data Engineering
Reliable pipelines that move, transform, and deliver your data where it needs to be — on time, every time. From raw ingestion to clean, queryable data products.
Business Intelligence
Self-serve BI environments where your team can explore data without waiting for a developer. KPI frameworks, semantic layers, and governed metrics your whole organisation can trust.
Analytics
Descriptive, diagnostic, predictive, and prescriptive analytics — matched to the decisions you actually need to make. We build analytics around business questions, not data availability.
Data Science
Statistical modelling, machine learning, and forecasting applied to real business problems. We build models that go into production — not notebooks that sit in a folder.
Data you can actually trust.
Reliable data is not an accident. These are the engineering standards every Data Studio engagement ships with as standard.
Data quality built in
Every pipeline ships with automated data quality checks. Row counts, schema validation, freshness alerts, and anomaly detection — not bolted on after something breaks.
Freshness you can rely on
Near-real-time data delivery for operational dashboards. Batch jobs that actually complete on time. SLAs defined upfront and monitored in production.
Performance at scale
Warehouse query optimisation, incremental models, partitioned tables, and caching strategies so your dashboards load fast even as your data grows.
Version-controlled everything
SQL transformations, pipeline definitions, and infrastructure as code — version-controlled, peer-reviewed, and deployable. No undocumented manual changes.
Best-in-class tools. Right for your problem.
We're tool-agnostic. We recommend and use the stack that best fits your scale, budget, and team — not whatever we happen to know best.
Visualisation
- Apache SupersetOpen-source BI platform
- MetabaseSelf-serve analytics
- Plotly / RechartsCustom chart libraries
- D3.jsBespoke interactive visuals
- Power BI / TableauEnterprise BI tools
Data Engineering
- dbtData transformation layer
- Apache AirflowPipeline orchestration
- Apache KafkaReal-time streaming
- Fivetran / AirbyteManaged connectors
- Apache SparkLarge-scale processing
Storage & Warehousing
- BigQueryGCP data warehouse
- SnowflakeCloud data platform
- PostgreSQLOperational analytics
- Delta LakeLakehouse architecture
- RedisLow-latency caching
Data Science
- Python / scikit-learnCore ML toolkit
- TensorFlow / PyTorchDeep learning
- MLflowExperiment tracking
- Vertex AI / SageMakerManaged ML platforms
- Jupyter / MarimoExploration & notebooks
Industries we've built data for.
Data problems look different in every industry. We bring domain context alongside technical depth.
Financial Services
Risk dashboards, transaction analytics, regulatory reporting, and fraud detection models built to compliance standards.
E-commerce & Retail
Revenue analytics, customer segmentation, inventory forecasting, and marketing attribution across channels.
SaaS & Technology
Product usage analytics, cohort analysis, churn prediction, and self-serve data for every team.
Healthcare & Life Sciences
Clinical data pipelines, operational reporting, patient analytics, and outcome modelling with appropriate governance.
Data that gets used. Not just collected.
Most data projects fail not because of bad technology, but because the work never connects to decisions. Here's how we approach it differently.
We build for production, not demos.
Every model, pipeline, and dashboard we deliver runs in production. We're not in the business of impressive proofs-of-concept that never get deployed.
Business questions first.
We start with what decisions your data needs to support — not with the data itself. Tools and architecture follow from that.
Self-serve, not dependency.
We build environments where your analysts and teams can explore data independently. The goal is to reduce your dependency on us, not increase it.
No black boxes.
Documented data lineage, transparent transformations, and explainable models. Your team understands what's running and why — always.
From audit to production.
A structured process that ensures what we build actually fits your data, your team, and your real business decisions.
Data Audit
We map your existing data sources, quality issues, and gaps before proposing anything. No assumptions — we understand what you have before deciding what to build.
1 week · Findings report
Architecture Design
Data model, pipeline design, and tool selection agreed upfront. You know exactly how your data will flow, where it will live, and how it will be accessed.
Architecture document
Engineer & Build
Pipelines, transformations, and storage layers built with reliability and observability from the start. Every pipeline has monitoring, alerting, and data quality checks.
Staged delivery
Visualise & Deliver
Dashboards, models, and self-serve environments handed over with documentation. We train your team so the work doesn't just sit in a tool nobody uses.
Handover + training
Iterate & Support
Data needs evolve. We offer retainers for ongoing development, new data sources, model retraining, and expanding your analytics as your business grows.
Retainer available
Tell us what decisions your data should be driving.
Whether you're starting from scratch, inheriting a mess, or scaling an existing data platform — bring us the challenge and we'll tell you exactly how we'd approach it.
We respond within one business day. No pitch decks, no NDAs upfront.
Frequently asked
We already have a BI tool. Can you work with it?
Yes. We work with your existing stack wherever possible — Tableau, Power BI, Looker, Metabase. We'll extend what you have, not rip it out.
How long does a data engineering project typically take?
A focused pipeline and BI layer: 6–10 weeks. A full data platform build: 3–6 months. We always deliver an audit and architecture document in week one so you know what you're getting.
Do you work with small data teams or solo analysts?
Yes. We frequently work alongside a single analyst or a small data team to scale their capacity without requiring them to hire. We're an extension of your team, not a replacement.
Can you build ML models and put them in production?
That's core to what we do. We don't deliver notebooks — we deliver models that are monitored, versioned, and retrained as your data evolves.