Data infrastructure AI can depend on.
Pipelines, quality checks, and retrieval that put the right information in the right shape for analytics, applications, and agents, with clear sources and ownership.
| Good for | Fragmented or unreliable data |
|---|---|
| Engagement | Scoped project or $200 / hour |
| First step | A short brief by email. Free. |
What the work involves
Data engineering covers how information is collected, transformed, stored, and served. An agent answering from documents needs a different data path from an application querying live records, so I design the pipeline around how the information will be used.
- Build ingestion and transformation pipelines across approved sources.
- Prepare datasets and APIs for analytics and AI applications.
- Add checks for missing records, duplicates, stale data, and broken mappings.
- Build document retrieval pipelines with source tracking and update rules.
When it helps
Your information is spread across systems, arrives inconsistently, or isn’t reliable enough for the application or AI workflow you want to build.
A typical project
A team needs answers from its runbooks and support documentation. We identify the approved sources, preserve source links and access boundaries, and establish an update process. The assistant retrieves the relevant material, and users can see where each answer came from.
What you get
| Deliverable | What it includes |
|---|---|
| A defined data path | Source mappings, transformations, and the data model the use case requires. |
| A production pipeline | Ingestion, processing, and access layers, with quality checks and failure handling. |
| Operating visibility | Documentation for refresh behavior, data ownership, and what to do when a source changes. |
Questions
Does every AI project need a vector database?
No. Document search, structured records, and live operational data have different access patterns. I choose storage and retrieval after understanding the task and the sources.
How do you keep an assistant’s information current?
We define where the information comes from, how updates are detected or scheduled, and what happens when a source is unavailable. Refresh behavior, permissions, and source traceability are part of the pipeline design.