We design, train and deploy machine learning systems that solve real problems: reducing manual data entry, predicting equipment failures, and answering customer questions around the clock.
Most companies know they want AI. Fewer know where to start. We handle the gap between an idea on a whiteboard and a model running in production.
Before writing a single line of code, we spend two weeks examining your data quality, volume and access patterns. You receive a 15-page report that tells you exactly which AI approaches will work, which won't, and why. No surprises six months later.
We build classification, regression and generative models using Python, PyTorch and scikit-learn. Each model goes through at least three evaluation rounds with your domain experts before we consider it ready for deployment.
A model that sits in a notebook helps nobody. We containerise it, set up CI/CD pipelines, and connect monitoring dashboards so your team can see accuracy drift, latency and throughput in real time.
Every engagement follows four stages. Timelines vary, but most projects reach production within eight to fourteen weeks.
A 45-minute video call where we ask blunt questions about your data, your budget and the problem you want solved. If AI isn't the right tool, we'll say so.
Our engineers connect to your data sources under a strict NDA, run statistical profiles, and flag gaps or biases. This usually takes five working days.
We train candidate models, benchmark them against your KPIs, and present results fortnightly. You see accuracy, precision and recall numbers, not marketing slides.
The approved model is deployed to your infrastructure or ours. We include 90 days of post-launch support at no extra cost, covering bug fixes, retraining triggers and performance reviews.
"We needed a document classifier that could handle 14 categories of insurance forms. Ai Horizon Guide delivered a model with 94% accuracy in seven weeks, and their monitoring dashboard caught a data-drift issue before it affected customers."
Rachel Ingham, CTO at Bridgewell Insurance
"Their feasibility audit saved us from building a recommendation engine we didn't have enough data to support. Instead they proposed a simpler rule-based system augmented with a small ML component. Honest advice is rare."
David Tran, Head of Product at Stackline Retail
Send us a brief description of what you need and we will reply within one working day with an honest assessment and a rough budget range.
Contact us