Service line
Data & AI Engineering
We build the pipelines, models, and governance that let your organization trust its data enough to act on it, then put applied AI on top of that foundation rather than ahead of it.
The problem we solve
Most AI initiatives fail at the data layer, not the model layer
Teams reach for a large language model or a forecasting algorithm before they have resolved which dataset is authoritative, how fresh it is, or who is allowed to see it. We reverse that order. Aetherion engineers spend the first phase of every engagement mapping data lineage, fixing ingestion gaps, and establishing a single source of truth, so the AI layer that follows is working with something solid.
- Data quality and lineage assessment before any model work begins
- Governance and access controls mapped to actual regulatory exposure
- Production grade pipelines built to handle schema drift
Scope of work
What this service line covers
| Capability | What it includes |
|---|---|
| Data engineering | Batch and streaming pipelines, ELT and ETL design, orchestration with Airflow or Dagster, and ingestion from operational systems, SaaS tools, and IoT sources. |
| Warehousing and lakehouse | Architecture and build out on Snowflake, Databricks, BigQuery, or Redshift, including partitioning strategy, cost-aware compute scaling, and semantic layer design. |
| Applied machine learning | Forecasting, classification, anomaly detection, and recommendation systems built and validated against your actual business metrics, not generic benchmarks. |
| Generative AI integration | Retrieval augmented generation, internal copilots, and document intelligence, with grounding and evaluation built in to control hallucination risk. |
| MLOps | Model versioning, feature stores, drift monitoring, and retraining pipelines so a model that works on day one still works on day two hundred. |
| Data governance | Cataloging, lineage tracking, access policy enforcement, and privacy controls aligned to frameworks like GDPR, HIPAA, and SOC 2. |
Engagement models
Three ways to bring us in
Foundation sprint
A four to eight week engagement to assess your current data estate and deliver an architecture and roadmap, with no assumption that you continue with us afterward.
Embedded build team
Aetherion engineers join your team for the duration of a defined build, reporting into your existing technical leadership and tooling.
Ongoing platform ownership
We own the pipelines and models end to end under a managed services agreement, with defined SLAs for freshness, accuracy, and uptime.