Data & Analytics
Data that Earns its
Place in every Decision.
Most data problems are not storage problems. They’re pipeline, trust, and latency problems. We build the infrastructure, the analytics layer, and the domain context that makes data useful.
data projects fail
Problems we see in every data engagement.
And what we do instead.
Smart Buildings
- Energy consumption analytics & benchmarking
- Occupancy sensing across portfolio
- Equipment predictive maintenance data
- Tenant experience dashboards
- ESG & sustainability reporting
Energy & Grid
- Smart meter data platform at utility scale
- Real-time grid telemetry processing
- Demand & generation forecasting models
- Carbon accounting pipelines
- Regulatory reporting (FERC/OFGEM)
Industrial
- Machine telemetry ingestion & historian
- OEE dashboards & production analytics
- Predictive maintenance data platform
- Quality control analytics
- Supply chain data integration
Retail & eCommerce
- Customer 360 platform (online + offline)
- Demand forecasting & inventory analytics
- Personalisation feature store
- Campaign & channel analytics
- Returns & fulfilment intelligence
Healthcare
- HIPAA-compliant clinical data platform
- Population health analytics
- Deterioration & readmission prediction data
- Capacity planning & ED analytics
- Revenue cycle analytics
FinTech
- Real-time transaction scoring pipeline
- Credit risk feature store
- Regulatory reporting (Basel III, IFRS 9)
- Customer LTV & churn analytics
- AML / KYC data layer
Data across industries
What data and analytics looks like in each vertical.
01
The problem
Data that nobody trusts
When dashboards show different numbers, people stop using them. Trust breaks down before the analytics even starts.
◆ Our approach
Data contracts and quality gates at every layer
dbt tests, Great Expectations, and Monte Carlo monitoring catch quality issues at ingestion and transformation not when a user spots a discrepancy in a meeting.
02
The problem
Pipelines that work in staging but fail in production
Brittle pipelines built for known data shapes break the moment source schemas change or volumes spike.
◆ Our approach
Schema evolution, anomaly detection, and automatic alerting
Pipelines built with schema evolution in mind, monitored for data drift and volume anomalies, with alerting that fires before downstream consumers see the problem.
03
The problem
BI that requires an engineer for every question
If every new dashboard requires a sprint ticket, analytics becomes a bottleneck rather than a capability.
◆ Our approach
Semantic layers and self-serve analytics
A well-designed semantic layer (Looker LookML, dbt metrics, Cube.js) means business users ask questions in their language without touching SQL and get consistent answers.
04
The problem
AI models that fail because data is wrong
A model is only as good as its training data. Garbage in, garbage out regardless of model complexity.
◆ Our approach
AI-ready data as a first-class engineering output
Feature stores, versioned datasets, data lineage, and training pipelines designed alongside the data platform not retrofitted after the first model underperforms.
Platforms & tools
full modern data stack.
Connect With us
Show us your data landscape. We'll show you what's possible.
Describe your current sources, volumes, and the decisions your analytics needs to support we’ll map the architecture and the gaps worth closing first.