Most companies are sitting on data they never act on. We build the pipelines, models and automations that turn it into forecasts, scores and decisions — deployed where your team works, and measured on results.
The ML lifecycle we run
↻ retrains on real-world drift
throughput per person, automated
decisions that took hours
lift from better scoring
insight that was monthly
What our data science practice delivers
Four capabilities that share one discipline — and each has a real project behind it. Click in for the detail.
Why our models earn their keep
Plenty of teams can train a model. Getting it to change a business number is the part that takes discipline.
Clean, unified, governed data before any modelling — because the best algorithm can't fix bad inputs.
We start from the action the model should change, so the output lands in the workflow, not a report nobody opens.
Drift detection and retraining keep accuracy up after launch, when most models quietly stop being right.
Data science, in production
A lead-gen scale-up was hiring ops staff just to keep up with volume. We trained a qualification model on their own closed-won data and wired it into an automated pipeline — so the best leads surface and route themselves.
Reachloop
Scaling lead-generation company · UK
Leads per head
Response time (was 4 hrs)
Qualified-lead rate
Manual work saved/day
“We were about to hire our way out of a process problem. pyronix automated the process instead. We took ten times the volume with the team we already had — that's the whole ballgame for a scale-up.”
Straight answers
It turns the data you already collect into decisions and automation — forecasting demand, scoring leads or risk, reading documents, and removing manual work. The point isn't a model in a notebook; it's a number that moves on your P&L.
Yes. Most engagements start with the data, not the model. We build the pipelines that clean, unify and govern your data first, because a model is only ever as good as what feeds it.
We design backwards from the decision or workflow it should change, and put the output where people already work — a dashboard, an alert, an action in your app. A prediction that doesn't reach a decision is wasted.
Monitoring and drift detection are part of delivery. When the world shifts and accuracy slips, we catch it and retrain — so the model stays useful instead of silently rotting.
Predictive analytics, NLP and computer vision, data pipeline engineering, and workflow automation — the full path from raw data to a deployed, monitored prediction or automation.
Tell us the decision you want to get right. We'll come back within 24 hours with a data-and-model plan and the metric we'd move.
2000+ vetted engineers · 3 global hubs · 98% client retention
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