Rachael Okonkwo
Co-founder and lead ML engineer. Previously built fraud-detection pipelines at Barclays processing 14 million transactions per day. Specialises in gradient-boosted models and time-series forecasting.
How we got here
Clear Edge Ai Tech was founded in 2019 by three machine-learning engineers who had spent the previous decade building recommendation engines and fraud-detection systems inside large financial institutions. The recurring frustration was the same everywhere: promising prototypes that never reached production because the gap between data science and software engineering was too wide.
So we set up shop in Leeds with a simple rule. Every model we build must be deployable within eight weeks, monitorable by the client's own ops team, and measurably cheaper to run than the manual process it replaces. That discipline has kept us profitable and kept our clients coming back.
Three people have grown to nineteen. We still operate from West Yorkshire, though roughly a third of our team works remotely across the UK. Our client list now spans retail, logistics, healthcare, legal and manufacturing.
What we believe
Our mission is blunt: make Artificial Intelligence useful, not impressive. A model that saves a warehouse manager two hours a day matters more to us than a model that wins a Kaggle competition. The values below shape every decision we make, from hiring to scoping projects.
Ship it or scrap it
If a model cannot reach production within the agreed timeline, we tell you early and recommend an alternative approach. We do not charge for work that sits on a shelf.
Respect the clock
Every engagement has a fixed scope and a deadline. We track hours weekly and flag overruns before they become surprises. You always know where your budget stands.
Teach, don't gatekeep
We document our code, explain our model choices in plain language, and run a handover session so your team can maintain the system without us if they choose to.
Measure everything
We set a quantitative success metric before writing a single line of code. If the deployed model does not hit that metric within the first 90 days, we retrain at no extra cost.
The people behind the models
We hire engineers who have built production systems, not just published papers. Every person on the team has at least five years of hands-on experience with real data at real companies.
Daniel Marsh
Co-founder and head of engineering. Spent eight years at ASOS building the recommendation engine. Obsessed with CI/CD for ML, model versioning and reproducible training runs.
Pradeep Anand
Co-founder and commercial director. Former management consultant at McKinsey where he led analytics transformations for three FTSE 250 retailers. Translates business problems into model specifications.
The wider team includes six ML engineers, four data engineers, two front-end developers, a DevOps specialist, a technical writer and a project manager. We hire from the UK and remote-first roles are available for senior positions.
Key milestones
2019 — Founded in Leeds
Three co-founders, one shared office, and a first client: a regional logistics firm that needed demand forecasting for its fleet scheduling.
2020 — First NHS project
Built an NLP pipeline that extracted structured data from 200,000 unstructured radiology reports, cutting admin time for clinicians by an estimated 11 hours per week across the trust.
2022 — Team reaches 12
Opened a second floor in our Ritchie Copse office. Signed retainer contracts with four enterprise clients in retail and financial services.
2024 — Computer-vision lab launched
Invested in GPU infrastructure to train and fine-tune vision models in-house, reducing our dependency on cloud compute and lowering client costs by roughly 30% for image-heavy projects.