Data and AI
AI data engineering: getting the right data to your AI, in a form you can check
Kriv AI helps teams plan the data side of an AI project: where the data comes from, how it is cleaned and checked, and who can see it. This page explains the work in plain terms.
AI data engineering is the work of collecting data from your systems, cleaning and checking it, and delivering it to an AI model or agent in a form the team can trace. It uses the same building blocks as ordinary data engineering: pipelines that read from source systems, transform the data and write it to a target. The AI part adds a few jobs: preparing documents and text, limiting what each AI system may see, and keeping a record of which data produced which answer. Without that work, an AI project tends to stall on the data, not the model.
plain answer
What is AI data engineering?
Data engineering is the work of moving data from where it is created to where it is used. Microsoft's architecture guide describes the common pattern: organizations gather data from multiple sources in various formats and move it to one or more data stores, and the data often needs to be shaped, cleaned or transformed before loading.
AI data engineering is that same work with an AI system at the receiving end. The receiver may be a model that answers questions over your documents, an agent that looks up records, or a report written by a model from tables you already hold. In each case the AI is only as useful as the data it is given, so the data work comes first.
framework
The six parts of AI data engineering
A data pipeline reads from source systems, transforms the data, and writes it to a target. A Microsoft Learn tutorial for Azure Databricks describes it in those terms: read from source systems, transform with steps such as data quality checks and record de-duplication, and write to a target. For an AI project we add the steps below.
1. Find the sources
List the systems, files and inboxes the AI will draw on, and who owns each one.
2. Bring the data in
Pull it into one place on a schedule, so the AI reads a current copy and not a stray export.
3. Clean and check it
Remove duplicates, fix formats, and write down the rules a record must pass. Records that fail go to a person, not into the AI.
4. Prepare documents and text
Split long documents into pieces, keep the title, date and owner with each piece, and drop what should not be there.
5. Limit what each AI system can see
Give each model or agent only the data its task needs, and keep sensitive fields out unless the task requires them.
6. Keep a record
Log which data was read, when, and which output it fed, so a reviewer can trace an answer back.
differentiation
How it differs from ordinary data engineering
Ordinary data engineering often ends in a table or a dashboard, where a person reads the result. In an AI project the reader is a model, and it will use whatever it is given, including a stale or wrong record, without any sign that something is off. That makes the checking steps more important, not less.
Documents are the second difference. Contracts, clinical notes, policies and emails are not rows and columns. They need their own preparation so the AI can find the right passage and so a reviewer can see where it came from.
how to
ETL or ELT: which pattern fits
Microsoft's guide says the two patterns differ in where the transformation takes place. In ETL, data is transformed before it is loaded. In ELT, the transformation happens in the target data store. The guide suggests ETL when regulatory or compliance requirements call for curated staging audits before loading, and ELT when the target is a modern data warehouse or lakehouse and you want to keep raw data for later use.
Which one suits you depends on your systems and your review needs. We would settle that with your engineers after seeing where the data lives. Our data and AI consulting page describes how we approach that first look.
security
Controls that belong in the pipeline
In regulated teams the pipeline is where several decisions get enforced: which fields leave a source system, which records the AI may read, and who can change the rules. We write these decisions down with your IT and compliance owners before building, informed by the frameworks you already answer to. NIST describes its AI Risk Management Framework as intended for voluntary use and as aiming to improve the ability to incorporate trustworthiness considerations into the design, development, use and evaluation of AI systems.
Your compliance team keeps the final judgement on what is allowed. For the policy side, see our pages on AI data governance and data governance solutions.
kriv role
Where Kriv helps
Kriv AI helps teams plan and build the data side of an AI project. That starts with one use case: we map its sources, write the checks a record must pass, decide what the AI may see, and agree how the work is logged. Work is scoped and priced after a short discovery call.
We do not replace your data team. We work with them, and hand over the pipeline design in writing so they can run and extend it.
how an engagement runs
How an engagement runs
1. Discovery call
We pick one AI use case and learn which systems and documents it depends on.
2. Data map
We list sources, owners, formats and the fields that need protection.
3. Pipeline design
We write the steps, the checks and the access limits with your IT and compliance owners.
4. Build and pilot
We build on limited data and a person reviews the outputs before the scope grows.
5. Handover
Your team receives the design, the checks and the run log.
limits
What this page does not cover
This page describes the general approach. It does not recommend a specific platform, and it does not state costs or timelines, because both depend on your systems. We read three vendor and standards pages for this page on 10 November 2026 and quote them only where cited above.
Straight answers
Frequently asked questions about AI data engineering
What is AI data engineering?
It is the work of collecting data from your systems, cleaning and checking it, and delivering it to an AI model or agent in a form your team can trace. It uses ordinary data pipelines, plus extra steps for documents, access limits and a record of which data fed which answer.
Is AI data engineering different from data engineering?
The building blocks are the same: pipelines that read from sources, transform the data and write it to a target. The difference is the receiver. A model uses whatever it is given and gives no sign that a record is wrong, so the checks and the access limits carry more weight.
Do we need a new data platform before starting?
Not necessarily. Many projects begin with one use case on the systems you already have. We look at where the data lives and what it needs before recommending any change, and the decision stays with your team.
How do you handle documents and unstructured data?
Documents get their own preparation. We split them into pieces, keep the title, date and owner with each piece, and remove what should not be there, so the AI can find the right passage and a reviewer can see where it came from.
Who decides what data the AI may see?
Your organisation does. We write the options down with your IT and compliance owners, build the limits into the pipeline, and log what was read. Your compliance team keeps the final judgement.
How is the work priced?
Work is scoped and priced after a short discovery call, once we know the use case and the systems involved.
Talk to the team that would do the work
Bring your requirements to a working session with the person who'll actually deliver.
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