Data Engineering & AI Readiness Advisory

Most AI initiatives that stall don't fail at the model — they fail because the underlying data was scattered, inconsistent, or inaccessible. We build the ETL/ELT pipelines and warehousing layer (using platforms like Snowflake, Databricks, or BigQuery) that consolidate data from CRMs, ERPs, product databases, and third-party sources into a state actually usable for AI and analytics work.

This is frequently the honest first recommendation from our readiness assessment: before a predictive model or generative AI feature is worth building, the data pipeline feeding it needs to be reliable, deduplicated, and current. We scope this as a standalone engagement when that's genuinely the priority, rather than skipping straight to a model build on a shaky data foundation.

• ETL/ELT pipeline design and implementation
• Data warehousing on Snowflake, Databricks, or BigQuery
• Data quality, deduplication, and consolidation across source systems
• Pipeline architecture scoped specifically to support downstream AI/ML work
• Often recommended as a prerequisite phase ahead of a model or GenAI build