Data Engineering and Preparation is foundational work, usually 4 to 8 weeks, that makes retrieval and fine-tuning reliable. It covers pipelines, cleaning and labeling, warehouse design, schemas for retrieval, and ongoing data-quality alerts.
Included
- ETL and ELT pipeline design and implementation
- Data cleaning, labeling, and quality validation
- Data lake and warehouse architecture (Snowflake, Databricks)
- Schema design for retrieval and fine-tuning workloads
- Ongoing data quality monitoring and alerting
7
A real data foundation, built before any model work started
from our workSources unified
12 TB/day
A rebuilt pipeline made twelve terabytes of daily sensor data usable
from our workSensor data reliably ingested
96%
Preparing 1.2 million regulated documents made retrieval accurate enough to trust
from our workRetrieval accuracy