Models do not decay because the algorithm ages. They decay because the data beneath them drifts. The foundation is the product.
From raw sources to AI-ready features
The invisible foundation that decides whether AI stays accurate over time. We build it to keep bad data out before a model ever sees it.
Pipelines
Streaming and batch ingestion with schema evolution and lineage.
Lakehouse
Governed lake / lakehouse with quality checks and a schema registry.
Feature store
Training/serving-consistent features, monitored for drift.
Quality & lineage
Quality frameworks, cataloguing, PII detection, and retention.
Quality checks run inside the pipeline — issues are caught and traceable to source.
How it's built
The data foundation that keeps production AI accurate over time.
- INGESTIngestion
Streaming and batch pipelines with schema evolution and lineage.
- STORELakehouse
Governed lake/lakehouse with quality checks and a schema registry.
- SERVEFeature layer
Training/serving-consistent features monitored for drift.
- GOVERNQuality & governance
Data-quality frameworks, cataloguing, PII detection, and retention.
What this engagement delivers
Streaming & batch pipelines
Ingestion that handles real-time and batch sources with schema evolution and lineage tracking.
Lakehouse layer
A governed lake/lakehouse with quality checks and a schema registry.
Feature store
Training/serving-consistent feature computation with monitoring for production ML.
Data quality & governance
Quality frameworks, cataloguing, PII detection, and retention enforcement.
The delivery timeline, sized by duration
Four phases from architecture assessment to data governance — sized by their stated duration.
Segment width follows each phase's stated duration range.
Reliable by design
4
Layer platform engineered into every deployment — ingestion, lakehouse, feature store, governance
<15 min
Streaming data freshness in real-time fraud detection architectures
99.9%
Pipeline uptime target on production data infrastructure
The right engagement profile
Data Engineering Teams
Modernising legacy ETL and building AI-ready data infrastructure
AI Programme Leaders
Needing a feature layer and pipeline to support model training and inference at scale
CDO / Data Leaders
Establishing data governance, quality, and observability across the enterprise
Where this service has the most impact
Questions buyers ask first
Why invest in data infrastructure before models?
Models are only as reliable as their data. Pipelines, quality checks, and a feature layer are what keep AI accurate over time — without them, model performance decays silently.
Can you modernise our legacy ETL?
Yes. The architecture assessment maps your current estate and returns a blueprint to modernise incrementally toward an AI-ready platform.
How do you ensure data quality?
Quality frameworks (Great Expectations / Soda) run inside the pipeline with lineage tracking, so issues are caught and traceable.
What is a feature store and do we need one?
It is a layer that serves consistent features to training and inference. If you run more than one model on shared data, it prevents training/serving skew and duplicated work.
NOT SURE THIS IS YOUR GAP?
The AI Readiness Assessment scores data maturity alongside the three other layers a programme depends on, and tells you which one is holding the rest back. About five minutes, nothing is sent anywhere.
Score your readinessBuild the data foundation AI needs
Book a 40-minute discovery call for a data architecture assessment of your current estate. No commitment.