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DataPhoenix is a data-first AI consultancy for UK, EU and Swiss businesses. We make your data AI-ready, then deliver AI you can trust with it, from first assessment to production handover.
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Central banks wheeled gold bars between cages in the New York Fed’s basement for decades after money became numbers in ledgers. Cloud got years of lift and shift. Habits always outlast their reasons.
AI is at that stage now. A promising demo meets the real data estate: stale sources, undocumented schemas, PII nobody classified. The demo never becomes a product, and the tool gets blamed for the habits.
We work the new loop: define what done means before any work starts, prepare the data, let AI do the heavy lifting, and put your standards in a harness that travels. Culture change is part of every engagement, not an afterthought.
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Our founder works daily as an AI operator on enterprise data platform delivery. The methods we sell are the methods we use, every day, in production.
Something strange is happening in tech. CTOs of billion dollar companies are quitting to take IC roles at Anthropic.
How DataPhoenix engages: assess your AI readiness first, pilot one use case to production grade, scale what the pilot proved, then hand over so your team runs it.
DataPhoenix also sets up the delivery operating model: how the work runs, not only what is built. Experienced engineers set direction and review the work, AI agents carry parts of delivery, and written standards (a definition of done, review gates and security checks) travel with the project. Your team inherits the roles and quality gates, so delivery capacity can grow by adding people and agents while control stays in place.
Tell DataPhoenix what you want AI to do with your data, and we will show you how to get there safely and in production.
Find out whether your data estate can support AI before you commit budget. Quality, PII risk and use case fit, assessed and documented.
Identify, classify and mask PII across your estate so your AI projects can proceed with regulatory and reputational risk under control.
RAG pipelines, vector stores, embedding design and context management, the infrastructure that determines what your AI actually knows.
Run AI against your internal data using private cloud services or self-hosted models. We can help when your data must stay within your environment.
Platform strategy, vendor selection, architecture guidance and operating model design, before you commit to building anything.
End-to-end design, build and source onboarding, delivered within your framework with full documentation and handover.
AI systems are only as good as the data behind them. We're data engineers first, and we've built that layer for enterprises for years. We also run our own company on AI agents, so the methods we sell are the methods we use.
Yes, with the right controls. Some data can go to public models once masked or tokenised. Some should only ever touch a private deployment. We classify what you hold, then design the safe route for each use case.
No. You need the right slice of data, clean and governed, for one use case. We often start with a thin pipeline feeding a single pilot, then grow the platform as use cases prove out.
A conversation, then an AI readiness assessment. You get a clear picture of what's achievable now, what needs work first, and what it costs, before you commit to building anything.
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Ready to put AI to work on your data? Get in touch with the DataPhoenix team.