Looking for a Fractional AI Engineer? Start With the Data.
Most AI projects fail because of the data underneath, not the model. We build the data layer your AI can safely depend on.
Trusted by teams at- ezaligner
- Buildaz
- Reach University
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, given back after we built the data foundation
This Is What Ready for AI Looks Like
Scattered records get structured, sensitive data gets pulled out before your AI ever sees it, and the answer that comes back is one you can verify, not guess.
Diagram: records scattered across multiple systems are modeled into one governed table, with personal data held back from the AI, so the answer it returns can be verified instead of guessed.
What AI-Ready Data Means
Clean Schema
One warehouse, business logic defined once instead of scattered across tools and spreadsheets.
Tested and Validated
Automated checks catch broken data before anyone asks AI a question.
Classified and Governed
Personal and sensitive data is labelled and held back from AI tools.
Documented Lineage
AI and your team both know what each field means and where it came from.
We build the data layer, not the models. When you hire an AI engineer, they start on a foundation that works.
Our Services
We're confident enough in our delivery to offer a 100% guarantee: no risk on your end, request your last 30 days of payments back at any time.
- ✓Weekly 30-minute sync meetings
- ✓Weekly written updates
- ✓Complete documentation
- ✓Email support with 12-hour response (business hours)*
- ✓30-day post-termination email support
Strategy & Advisory
- ✓Monthly strategic planning session
- ✓Quarterly data health audit
- ✓Quarterly cloud cost optimization review
- ✓Data governance framework setup
- ✓90-day roadmap aligned to business goals
- ✓Team training sessions on pipelines built
- ✓Technology stack & vendor recommendations
Engineering Execution
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- ✓Minimum 0.5 FTE per assigned team member
- ✓Weekly 30-minute sync meetings
- ✓Weekly written updates
- ✓Complete documentation
- ✓Email support with 3-hour response (business hours)*
- ✓60-day post-termination email support
Ongoing maintenance available after your engagement.
* Response means written reply only; it does not include hands-on work, fixes, or incident response.
1 FTE = 40 hours/week (~173 hours/month).
Why Teams Choose Us
Because we've built data foundations from zero at multiple companies, we've learned one thing: data only pays off when it's built around your business goals.
- Startups
- Enterprises
- Consulting
- Non-profits
- A senior team, not a solo contractor: your project never depends on one person
- Obsessed with simplicity: if your team can't use it, we haven't finished
- We've built data systems from zero, multiple times
- We pick the right tools for you, not the ones we prefer
Built data systems from scratch at multiple companies. Obsessed with making things simple, specializes in connecting messy data, cutting unnecessary costs, and building something the whole team can actually use.
What Clients Say About Working With Us
“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.”
Victor G.
CEO at ezaligner
“Our organization has been working with the team for 3 months now and we've made tremendous progress where we were previously stalled. Their communication is reliable, and their work ethic is top notch. In a few months we have collaborated to solve foundational data issues that have plagued our org for years! I'm excited to continue working with this team and would recommend them to anyone looking for data expertise.”
Brad L.
Vice President of Information Technology at Reach University
“The team worked with one of the world's largest retailers, building pipeline architecture with BigQuery, Airflow, and Looker. Strong technical execution, excellent planning, and clear communication with stakeholders. Would hire again.”
Georg B.
CTO at Buildaz
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 11 people rely on the same infrastructure.
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
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.
Frequently Asked Questions About AI-Ready Data
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.
Why does my AI tool give wrong or inconsistent answers?
Almost always, the issue is the data underneath, not the AI model. Common causes: 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.
What does AI-ready data mean?
Four things need to be true. Your data has a clean schema, one warehouse with business logic defined once. It is tested and validated, so automated checks catch broken data before anyone asks AI a question. It is classified and governed, so personal and sensitive data is labelled and held back from AI tools. And it has documented lineage, so AI and your team both know what each field means and where it came from.
Is it safe to connect AI tools to our company data?
Only if personal and sensitive data is identified and held back first. Before we connect anything, we map what's actually sensitive so it's labelled where it lives, then enforce access rules and keep a clear trail of where every number came from. That matters most when GDPR or HIPAA applies to your data. See our post on what GDPR and HIPAA actually require from your data pipeline.
Do you build AI models?
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 your data is already clean, centralized, and tested, you need an AI or ML engineer. If it isn't there yet, that's where we come in.
Related Reading
Why Plugging Your Data Straight Into AI Is Riskier Than It Looks
What's actually at risk, and what to do about it instead.
What AI Needs from Your Data
Four bars to clear before you trust the output.
What GDPR and HIPAA Actually Require From Your Data Pipeline
What they require, and why the deletion request is usually where it falls apart.