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AI-ready data foundations · Production DataOps & pipeline engineering · Fractional data leadership · CI/CD for data pipelines · Schema drift, caught before it ships · RAG-ready semantic layers · Infrastructure as code · North America, remote-first

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Data engineering service

AI-Ready Data Foundations for Reliable RAG and Analytics

AI-ready data foundations give AI and analytics systems the context they need: semantic layers, metadata cataloging, clean lineage, and validated RAG-ready pipelines.

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When you need it

Where this service fits

  • An AI or RAG feature depends on data that was designed for dashboards rather than retrieval.
  • Teams use different definitions for the same metric, entity, or business concept.
  • You need provenance and validation before context reaches a model endpoint.

Audience and problems

Built around the current data reality

For teams like

  • Product and data teams building AI features on an existing warehouse or lakehouse.
  • Startups and scale-ups that need shared data meaning before adding more AI tooling.

Common problems

  • Business meaning is trapped in dashboards, SQL, or individual team knowledge.
  • Metadata is incomplete, stale, or disconnected from the pipeline that produced it.
  • Retrieval pipelines pass context without enough validation or provenance.

Outputs

What the work produces

Outcomes

  • A clearer semantic layer for the metrics and entities teams need to use consistently.
  • Traceable lineage and metadata around the data used by AI and analytics consumers.
  • RAG-ready ingestion with validation before context reaches the application.

Deliverables

  • Semantic-layer and metric-definition design.
  • Metadata cataloging and ownership patterns.
  • Data-lineage mapping from source to consumer.
  • RAG-ready ingestion and validation pipelines.

Engagement process

A practical path from current state to next step

  1. 01

    Define

    We identify the business concepts, metrics, and data products that need shared meaning.

  2. 02

    Connect

    We connect definitions to metadata, lineage, and the pipelines that produce the data.

  3. 03

    Validate

    We put checks around the context path so downstream AI and analytics consumers receive data with clearer provenance.

Tools and platforms

Work with the stack you have

  • dbt
  • Snowflake
  • Databricks
  • Apache Airflow
  • Python

Verified client proof

Aeolus Data Solutions provided us with an early prototype that solved our analytical needs, and set up the foundation for building future pipelines. Their work was essential in enabling us to make data-driven decisions as we scale.
Jiaqi Chen, Senior Product Manager, Figg
Aeolus helped us scope, plan and build out the data and analytics foundation that scales. DE is an ever-changing industry and Aeolus seems to always know the best solution to our specific problem.
Alina Chang, Senior Analytics Engineer, Confido

Questions

Frequently asked questions

What makes a data foundation AI-ready?

It has shared semantic definitions, useful metadata, traceable lineage, and validation in the pipelines that prepare context for AI or RAG consumers.

Do you need a vector database first?

Not necessarily. The first step is making the source data, metadata, definitions, and validation reliable enough for retrieval.

What tools and platforms does Aeolus work with?

Aeolus works with dbt, Snowflake, Databricks, Apache Airflow, Terraform, and the broader modern data stack.

Keep exploring

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