Question: I need a team that can handle data engineering, from sourcing to enrichment, to support my AI projects.

If you need a team to manage data engineering for your AI projects, Dataiku is worth a look. It's an all-purpose platform for data prep, machine learning, MLOps and AI app development. It's geared for teams like AI and Machine Learning, Data Analytics and Enterprise AI, so it should work for many industries. Dataiku is a Leader in the Gartner Magic Quadrant, and it's free for up to three users and offers a 14-day free trial.

Another good choice is Dataloop, which combines data curation, model management, pipeline orchestration and human feedback to speed up AI development. It can handle a variety of unstructured data formats and automates preprocessing and model management. Dataloop is designed to make collaboration easier and speed up development, with features like 20x faster development and 95% automation, and it's designed to meet high quality and security requirements.

For data integration, check out Airbyte, an open-source project that lets you move data from more than 300 data sources to a variety of destinations. It's geared for AI engineers and data analysts with features like custom connectors, automated schema evolution and support for big security standards. Airbyte can be deployed in a variety of ways and controls pipelines through an interface, Python library or Terraform provider.

Last, Stitch is a cloud-based ETL tool that lets you connect data from more than 140 sources to a cloud data warehouse without having to write any code. It offers fully automated cloud data pipelines, which can be a big time saver, and supports a broad range of data sources. That makes it a good choice for data engineers and business analysts trying to streamline their data integration.