We build the data infrastructure that powers AI - from multi-source ingestion and transformation pipelines to model lifecycle management, automated retraining, and production monitoring.
Data lake architecture on BigQuery, Snowflake, or Delta Lake - built to query at scale
ETL/ELT pipelines with Airflow, dbt, and Airbyte - fully orchestrated and observable
MLOps platform: experiment tracking (MLflow), model registry, CI/CD for models
AutoML pipelines and A/B testing infrastructure for continuous model improvement
Real-time streaming ingestion with Kafka and Pub/Sub
Analytics-ready semantic layers for self-service BI across business teams
Step 1
Inventory all data sources, assess quality, and define the target schema and freshness requirements.
Step 2
Design the ingestion, transformation, and serving layers - batch and streaming where appropriate.
Step 3
Build pipelines with full test coverage: unit tests for transforms, data quality checks, and SLA monitoring.
Step 4
Connect model training to data pipelines, set up experiment tracking, and automate deployment gates.
Step 5
Dashboard data quality, model drift, and pipeline health - retraining triggered automatically on drift signals.
The tools powering our solutions.
PySpark
dbt
Airflow
Airbyte
BigQuery, Snowflake
AWS Glue
MLflow
Kafka
Tell us what you're building or struggling with - we'll tell you if and how we can help.