Capability 04
Data, AI & Architecture
Architecture as the bridge between business strategy and engineering reality. Cipherer designs the data and AI foundations that make decisions defensible and platforms governable.
What this discipline means at Cipherer
Data and AI architecture is where business strategy meets engineering constraint. We work at the level where decisions are made about how an organisation will store, govern and use its data over the next decade, and we deliver the engineering that makes those decisions real. The work spans enterprise architecture (cross-system standards, data governance, integration patterns), data platform design (lakehouses, warehouses, streaming) and AI integration where it earns its place.
Approach
Enterprise architecture as alignment, not bureaucracy
Architecture decisions only earn their cost if they make subsequent delivery faster. We define standards, design contracts and integration patterns that engineering teams want to use, with governance light enough to stay current.
Lakehouse where it solves the right problem
We design lakehouses on Databricks, Delta Lake and equivalent stacks for organisations whose data needs are bigger than a warehouse and richer than a lake. The pattern earns its keep when both ML and SQL access matter and governance has to be machine-checkable.
AI integration with engineering discipline
AI workloads sit on the same engineering rails as everything else: source-controlled, tested, observable, audit-traceable. We integrate Bedrock, SageMaker, Databricks ML and equivalent platforms into existing engineering practice rather than running them as a parallel universe.
Strategic advisory at executive level
Architecture choices are business choices. We advise CTOs, CIOs and CDOs on the trade-offs that determine cost, compliance posture and strategic optionality for years. Engagement happens at executive level, not just delivery level.
Tools and frameworks
- Databricks, Delta Lake, Apache Spark
- Amazon SageMaker, AWS Bedrock
- Data warehousing on Snowflake, BigQuery, Redshift where appropriate
- Streaming: Kafka, Kinesis, Pub/Sub
- Architecture frameworks: TOGAF-informed, decision-record driven
- AI integration patterns and governance