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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

Data & AI-Readiness Audit for AI and Analytics Teams

A data & AI-readiness audit is a short, fixed-fee diagnostic of your data stack. We review pipelines, schemas, metadata, and your warehouse, then return a technical report and a prioritized remediation roadmap.

Book a data & AI-readiness audit

When you need it

Where this service fits

  • You are deciding whether an AI or analytics initiative can rely on the current data foundation.
  • Pipeline failures, schema drift, or missing ownership make the current state hard to trust.
  • You want a bounded engineering assessment before committing to a larger build.

Audience and problems

Built around the current data reality

For teams like

  • Seed to Series B startups and scale-ups across North America.
  • Teams shipping AI, RAG, or analytics features without a senior data lead.

Common problems

  • Inconsistent schemas and undocumented data contracts.
  • Missing metadata, lineage, or ownership around important datasets.
  • Warehouse and pipeline breakages that are difficult to diagnose.

Outputs

What the work produces

Outcomes

  • A shared view of the current pipeline, schema, metadata, and warehouse state.
  • A prioritized remediation roadmap that separates foundational work from later improvements.
  • A clear basis for deciding what to build next.

Deliverables

  • Pipeline, schema, metadata, and warehouse review.
  • Technical report with findings and risks.
  • Prioritized remediation roadmap.

Engagement process

A practical path from current state to next step

  1. 01

    Review

    We examine the current data environment and the failure modes that matter to the intended AI or analytics use case.

  2. 02

    Report

    We organize findings into a technical report with the evidence needed to understand the current state.

  3. 03

    Roadmap

    We return a prioritized next-step roadmap so a larger build can be scoped against the actual environment.

Tools and platforms

Work with the stack you have

  • dbt
  • Snowflake
  • Databricks
  • Apache Airflow
  • Terraform

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
We highly value Aeolus Data Solutions' professional recommendations when we migrated from GCP to Databricks. They evaluated the requirements and growth projections before advising.
Julia Santos, Data Engineering Manager, Zensors

Questions

Frequently asked questions

What is a data & AI-readiness audit?

It is a short, fixed-fee diagnostic of your data stack. We map your pipelines, schemas, metadata, and warehouse, then hand back a technical report and a prioritized remediation roadmap.

How much does a data engineering engagement cost?

Aeolus does not publish fixed prices because scope depends on your stack, data volume, and goals. The audit is the concrete, bounded first step before any larger commitment.

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

Book a data & AI-readiness audit