Data Engineering
Intelligence is only as good as the data engineering beneath it.
The data layer intelligence runs on: pipelines, platforms, evaluation data and knowledge systems.
Architectural layers
Problem
What problem does this solve?
- Models are starved, biased or poisoned by data pipelines nobody engineered.
- Knowledge lives in scattered documents that no search system can trust.
- Evaluation data is missing, so model changes become acts of faith.
Scope
What CAS builds
- Ingestion and transformation pipelines with lineage and quality checks.
- Knowledge systems: corpus engineering, metadata and access control for grounded AI.
- Evaluation datasets and golden sets maintained like production code.
- Feature and embedding stores for reuse across models and products.
Architectures
Architectures that may be used
Each links into ModLens, the CAS architecture explorer, where the structure and trade-offs are diagrammed.
Method
How CAS approaches engineering
- Treat corpus quality as a product: owners, review, freshness, coverage metrics.
- Every pipeline has lineage, quality gates and a rollback story.
- Evaluation data is versioned and protected — it is the ground your models stand on.
Outcomes
What can result
- AI systems fed by data you can account for.
- Answer quality that improves because the corpus is engineered.
- Model changes that can be validated before they ship.
Delivery
What the process looks like
01 · Data audit
Sources, quality, gaps, access and governance.
02 · Platform design
Pipeline architecture, storage, quality gates.
03 · Build
Pipelines, knowledge systems, evaluation datasets.
04 · Operate
Freshness, drift, coverage and cost monitoring.
Preparation
What a client should prepare
- An inventory of data sources and their ownership.
- Access constraints and governance requirements.
- The AI use cases this platform must serve.
Outcomes depend on data, constraints and integration reality. CAS states assumptions explicitly and reports negative results when evidence demands them.
Discuss data & ai platforms with an engineer.
Bring the problem; we will bring the architecture, the evaluation plan and the honest feasibility read.