Looking for a Fractional AI Engineer?
Start With the Data.
Most AI projects fail because of bad data, not bad models. We build the data layer that makes AI work.
Companies searching for a fractional AI engineer usually have the same problem: they want AI to work with their data, but the data is not ready. Columns that could mean three different things. Customer records duplicated across systems. Pipelines that break without anyone knowing. An AI model trained on this data does not give you insights — it gives you confident nonsense. We are fractional data engineers. We build the layer that makes AI tools trustworthy: clean pipelines, a centralized warehouse, modelled data with clear definitions, automated quality checks, and documentation that gives AI the context it needs.
Last updated: June 2026
Why AI Projects Fail
63%
Of organizations do not have, or are unsure they have, the right data management practices for AI (Gartner, 2025)
60%
Of AI projects will be abandoned through 2026 without AI-ready data (Gartner, 2025)
10h
Per week one CEO spent manually validating AI outputs because the data had no structure (based on FDE client engagement)
3mo
From zero infrastructure to a working data foundation the whole team relies on (based on FDE client engagement)
Is This You?
This is for you if
- You are evaluating AI tools but your data lives in multiple systems with no unified layer
- Your team tried an AI analytics tool and the answers were wrong or inconsistent
- You want to adopt AI for internal operations but nobody trusts the underlying data
- A founder or executive is manually validating every AI output because there is no structured data layer
- You are not sure whether to hire a data engineer or an AI engineer first
Not the right fit if
- You need someone to build and deploy ML models — we build the data layer, not the model layer
- You already have clean, centralized, documented data and just need an AI/ML specialist
- You are looking for a chatbot or customer-facing AI product — we build internal data infrastructure
- Your data is already in a warehouse with tested models and you need help with the AI layer on top
What Makes Data AI-Ready?
AI tools do not fail because of the model. They fail because of what is underneath it. An AI-ready data foundation means your data is centralized, modelled, tested, and documented — so any AI tool you plug in gives answers your team can trust without double-checking.
One Warehouse, Not Five Disconnected Systems
Your AI tool queries one source at a time. If revenue lives in Stripe, customer data in HubSpot, and product usage in PostgreSQL, the AI cannot answer cross-source questions like “which customers from which channel have the highest lifetime value.” We connect all your sources into a single warehouse where the AI — and your team — can see the full picture.
Modelled Data With Clear Definitions
Raw database tables are not AI-ready. A column called “revenue” might mean bookings, recognized revenue, or cash collected depending on who set it up. Without a modelled data layer in dbt, the AI picks one interpretation and presents it as fact. We build models that encode your actual business logic — what counts as a customer, what counts as churn, how you define revenue — so every query returns the same answer regardless of who asks.
Automated Quality Checks That Catch Problems First
If a pipeline breaks at 2am and half your product data stops syncing, an AI tool querying that warehouse will tell you engagement dropped 50%. It did not. The data just stopped arriving. We set up automated tests — not-null checks, uniqueness, row counts, freshness — that catch these problems before they reach anyone. Your team finds out from an alert, not from a wrong answer in a meeting.
Documentation That Gives AI Context
AI does not know that “status = 4” means cancelled, or that your users table includes internal test accounts. That context needs to live somewhere the AI can reference. We document every model, every column, every business rule in dbt — so when an AI tool generates a query, it has the context to get it right instead of guessing.
How Do You Build an AI-Ready Data Foundation?
Most companies try to add AI before their data is ready. The sequence matters.
Audit what AI will depend on
We map every data source your AI tools will need to query — product database, CRM, financial systems, marketing platforms. We identify what is missing, what is duplicated, and what is unreliable. Most companies discover gaps they did not know existed.
Build the foundation layer
Centralized warehouse, automated pipelines, modelled data with dbt, quality tests running on every refresh. Business logic encoded once and used everywhere. This is the layer that turns raw data into something an AI tool can query and get correct answers.
Connect, verify, hand off
Once the foundation is solid, your AI tools connect to clean data instead of raw tables. We verify outputs together, document everything, and hand off infrastructure your team can maintain independently — or keep us on for ongoing maintenance as your AI needs evolve. When you hire an AI engineer, they build on a foundation that already works.
See the full 5-phase process on our homepage for details on compliance, onboarding, and knowledge transfer.
What AI-Ready Data Looks Like in Practice
Building a dbt Data Foundation from Scratch: A Case Study for Startups Without a Data Team
Health tech · ~50 employees · No data person, just a CEO, AI, and a lot of manual checks. Now 12 people rely on the same infrastructure.
0 → 1
dbt data foundation built from scratch
10 hrs/week
given back to the CEO by eliminating manual data validation
Automated
data quality checks running continuously
Tech: dbt · dbt Cloud (free tier) · PostgreSQL · Data Modelling · dbt Tests · Calculated Columns · dbt Documentation
Read case study →
Building a Self-Serve Analytics Culture with Metabase: A Case Study for Startups Without a Data Team
Health tech · ~50 employees · 11 non-technical users now making decisions from data, without asking anyone for help
0 → 1
Metabase built and adopted from scratch
11
non-technical users actively using self-serve analytics
4 departments
onboarded: marketing, sales, operations, C-level
Tech: Metabase · Metabase (basic plan) · dbt · PostgreSQL · Workshop Design · Notion · Data Governance · Self-Serve Analytics
Read case study →
From a CEO Whose AI Now Gets the Right Answers
“They structured our dbt, reduced platform costs, and left documentation so thorough our team kept building on it. No dependencies, no technical debts. More than a one-time delivery, it became the foundation for reliable metrics and data-driven decisions we're still evolving today. Professional, collaborative, and genuinely focused on long-term value.”
Ready to Make Your Data AI-Ready?
Book a free call. We will audit your current data setup, identify what is blocking AI adoption, and tell you exactly what needs to be in place before connecting AI tools.
Currently accepting 1 of 3 new clients
Frequently Asked Questions About AI-Ready Data
Do you build AI models or deploy machine learning systems?
No. We build the data infrastructure that AI models depend on — the warehouse, the pipelines, the modelled data layer, the documentation, and the automated quality checks. If you already have clean, centralized, tested data and just need someone to build the model, you need an AI/ML engineer. If the data is not there yet, that is where we come in.
Why does my AI tool keep giving wrong or inconsistent answers?
Almost always, the issue is the data underneath, not the AI model. Common causes: your data has no modelled layer so the AI guesses how to join tables, multiple columns that could mean 'revenue' with no clear definition, broken pipelines that leave gaps in your warehouse, and no documentation to give the AI context about what the data actually means. We wrote a detailed breakdown — see our post on why your AI keeps giving wrong answers.
What does 'AI-ready data' actually mean?
Five things need to be in place. Your data is centralized in one warehouse instead of scattered across tools. It has a modelled layer with clean, tested, well-named tables that represent your actual business. Business logic — what counts as a customer, what counts as churned, how you calculate revenue — is defined once and used everywhere. Automated tests catch quality problems before they reach any tool. And documentation gives both your team and your AI tools the context to interpret data correctly.
Should I hire a data engineer or an AI engineer first?
Data engineer first. An AI engineer builds models, trains algorithms, and deploys inference systems. But all of that depends on having clean, reliable, well-structured data to work with. If your data is scattered across systems, undocumented, and untested, an AI engineer will spend most of their time cleaning data instead of building AI. We build the foundation in 2 to 4 months. Your AI engineer inherits infrastructure that actually works.
Can you make our existing data warehouse AI-ready?
Depends on what is already there. If you have a warehouse but it lacks a modelled data layer, has no tests, and no documentation, we build those layers on top of what exists. If the warehouse itself is outdated or unreliable, we may recommend a rebuild. We audit first and tell you honestly what needs work. Not every engagement is a ground-up build.
How long does it take to get data ready for AI tools?
Most foundations take 2 to 4 months. We have built a complete dbt data foundation from scratch in under 3 months for a company that had no infrastructure at all — and their whole team now relies on it daily. The timeline depends on how many data sources you have, how messy the current state is, and how complex your business logic is. We scope everything before we start.
Related Reading
Why Your AI Keeps Giving Wrong Answers (It Is Not the Model)
The five data problems that make AI tools return wrong numbers.
How to Leverage AI for Data Analytics
Why the data foundation comes before the AI layer.
How to Build a Data Stack from Scratch
Step-by-step guide for teams starting with no infrastructure.