Applied AI & Machine Learning

Overview

Not every AI problem needs an agent or a giant language model. We build focused machine-learning and multimodal systems for prediction, classification, recommendations, extraction, vision and voice. We start with the business decision, test the simplest credible approach and measure it against a useful baseline.

The problem

Teams often buy an AI tool before defining the decision it should improve. The result is an expensive model with no clear baseline, unreliable performance on real data and no owner once it reaches production.

What we build

What this looks like in practice.

Predictive models for demand, churn, risk, pricing and operational forecasting
Classification, scoring, recommendation and anomaly-detection systems
Language systems for extraction, summarisation, routing and sentiment
Vision and document intelligence for images, video and scanned content
Fine-tuning and model adaptation when prompting or retrieval is not enough
Production deployment, monitoring and retraining around measurable outcomes

Outcomes

What changes for your team.

01

Better decisions made earlier from patterns people cannot review at scale

02

A model chosen for the job, not for the headline

03

Performance measured against a real baseline

04

A production system that can be monitored and improved

Questions