AI & DATA SCIENCE DEVELOPMENT
WE BUILD THE AI TO POWER PRODUCTS
Unstructured data in. Intelligence out.
The Process
We start with your documents, reviews, transcripts, emails. Extract the information buried in them. Shape it into data architecture designed for analytics at scale. Then build the intelligence layer on top.
Unstructured text: documents, PDFs, product reviews, emails, transcripts,...
Databases, knowledge graphs, ML models, and more
Recommendations, predictions, automation, analytics, ...
- Recommendations
Show users what they need before they go looking. - Intelligence
Turn raw documents into answers your team can act on. - Analytics
Understand why customers really complain. At scale. - Extraction
Pull key terms from contracts, invoices, and CVs in seconds. - Predictions
Know who's about to churn or which leads will close. - Automation
Let tickets route and prioritise themselves.
Out of the box tools are amazing, but they won't get your production deployment over the line.
Extraction
Every business has different documents, different terminology, different edge cases. Extraction isn't a plug-and-play API. It's custom logic shaped around how your data actually looks. And in the real world, there are constraints: cloud deployments, data residency, privacy requirements, corporate red tape. The technical problem is only half of it.
Architecture
Analytical data models at scale are specialist work. Millions of rows, complex joins, queries that need to return in milliseconds. Architecture decisions made early determine whether your application performs in production or falls over. This is modern data stack territory: warehouses, pipelines, feature stores. You need to know how to optimise them.
Intelligence
You start with a business question or a user story, not a model. The methodology matters, but it's the layers of custom work on top that make it actually work. Feature engineering, edge case handling, performance tuning. Every model is bespoke.
"Laura and the DataFenix team delivered exceptional value on a time-critical project. Laura is that rare combination of strategic thinker and hands-on developer. From autonomous requirements gathering and solution architecture to coding and leading client sign-offs, her ability to move effortlessly between technical depth and executive communication meant she slotted into our team seamlessly. The project delivered on time and exceeded expectations. Highly recommended."
"We've partnered with Laura at Datafenix, a specialist AI and data consultancy, to enhance our data science capabilities in an enterprise-grade environment at a large global asset manager with $80bn in assets under management. Her work has directly supported our sourcing and origination efforts. Laura is a highly experienced data scientist who rapidly introduced advanced techniques tailored to our use cases, quickly translating them into viable, production-ready outcomes. She contributed not only at a technical level but also in shaping the underlying business case. A true self-starter, Laura works effectively to clear objectives without requiring close supervision. Most importantly, our users are delighted — we're uncovering genuinely valuable connections and insights that are materially improving how we identify and pursue opportunities."
"We'd talked to a few people about building this and honestly none of it went anywhere. Laura was the first person who delivered what they said they would. It's live in our application now and working great."
Clarity First
Figure out what's actually possible. Turn early-stage ideas into a spec you can build from.
Builders, Not Advisors
We write the code, train the models, and deploy the systems.
No Layers
You talk directly to the people building your product.
Zero Lock-In
You own every line of code. We're here to ship, not to stay.
Laura Parfitt — Founder
Close to 20 years in data, most of it at the intersection of product and data science. Five years at Google leading teams across Ads, Play, and Stadia — at Play I was analysing how 3 billion users behave in apps. At startups I' ve built data platforms from scratch, hired the teams around them, and shipped everything from recommendation engines to experimentation systems.
I started DataFenix because I kept seeing the same gap: strong dev teams, ambitious products, but no one to own the AI and ML work. Two years on, almost every project has come down to the same core challenge: turning messy, unstructured information into something useful. That's the work now.
I'm hands-on. Specialists join when needed. If you're figuring out whether AI makes sense for your product, let's talk.
Let's Scope It
A focused conversation about what AI could unlock for your product.
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