Services/Scale
AI Data Engineering
Pipelines, warehouses, and vector stores that give AI systems clean, current, and governed data.
- 8 to 12 weeks
- Project-based, then optional retainer
- Data engineers with a platform architect
- AI initiatives blocked by scattered, stale, or untrusted data
What we do
AI systems are only as good as the data behind them. We build the ingestion, transformation, quality, and serving layers that turn raw sources into datasets models and agents can rely on, including the vector and graph stores retrieval depends on.
What the work includes
Medallion pipelines
Bronze, silver, and gold layers with clear ownership, so raw data, cleaned data, and business-ready data never get confused.
Streaming where it matters
Event-driven pipelines on Kafka or cloud-native equivalents for the cases where a nightly batch is too slow.
Quality as code
Schema enforcement, anomaly checks, and lineage tracking that fail loudly before bad data reaches a model.
Retrieval-ready stores
Vector databases, knowledge graphs, and search indexes designed for the retrieval patterns your agents and RAG systems will actually run.
In production
A governed data platform with 500+ connectors
Medallion pipelines into a columnar warehouse, exposed via SQL and REST, with catalogue, vector store, and knowledge graph, delivered for an enterprise supply-chain pilot.
See our enterprise experienceHow an engagement runs
Scope, build, evaluate, operate.
The same four stages on every engagement, so you always know what happens next and what you will have at the end of it.
Scope
A short discovery with the people who own the problem. Output: a written scope, acceptance criteria, and a fixed estimate.
Build
A named lead and a team sized to the scope. Working software from the first weeks, demonstrated on a fixed cadence.
Evaluate
Every AI component is measured against real cases before rollout. The numbers decide when it goes live.
Operate
Deployment, monitoring, and a support window. Then a handover, or an ongoing team if you want one.
Typical stack
Tools we commonly use for this work. The final choice follows your requirements, region, and existing platform.
- Python
- Apache Airflow
- Apache Kafka
- Apache Spark
- PostgreSQL
- Neo4j
- Redis
- Elasticsearch
- dbt
- Snowflake
Data
The layer every AI system stands on
Most stalled AI projects are data projects in disguise. We build the foundation first, so the model work that follows is measured against data you trust.