We design, train and deploy machine-learning models that sit inside your existing workflows. No rip-and-replace. No six-month "discovery phase." A working prototype in four weeks or we refund the deposit.
Show me what my data can do
Each engagement produces a deployed, monitored system, not a slide deck. Here are the four areas where we spend most of our time.
We build regression and classification models on your historical data to forecast demand, churn, pricing or maintenance windows. Most clients see usable output within three weeks because we focus on a single, well-scoped prediction target first and expand from there. Typical stack: Python, XGBoost or LightGBM, served via a REST endpoint your existing systems can call.
Quality inspection on a production line, vehicle counting from CCTV, document digitisation: if a camera captures it, a convolutional network can label it. We handle annotation, training, edge deployment on NVIDIA Jetson or cloud inference. One food-packaging client reduced visual-inspection staff hours by 74% in the first quarter after go-live.
Ticket routing, sentiment analysis, contract clause extraction, internal chatbots grounded on your own knowledge base. We fine-tune open-weight large language models so your data never leaves your infrastructure. Response latency under 400 ms on a single A100 for most summarisation tasks.
A model is only as reliable as the pipeline feeding it. We design extraction, transformation and loading workflows in Apache Airflow or Dagster, wire up monitoring dashboards and set drift-detection alerts. When your source schema changes at 2 a.m., the pipeline pages us, not you.
Five concrete steps. Typical calendar time from kick-off to production: 6 to 10 weeks.
We review your data landscape, agree on a single measurable outcome and sketch the technical approach. You get a written proposal within two business days.
Our engineers connect to your data sources, profile quality, and establish a naive baseline metric. This tells us exactly how much room the model has to improve.
Two-week sprints. After each sprint you see updated accuracy, precision and recall numbers alongside a working demo. If the numbers plateau, we pivot the feature set early rather than burning budget.
We containerise the model, deploy to your cloud (AWS, Azure or GCP) or on-prem hardware, and wire it into the application layer. Load testing, failover and rollback procedures are part of the deliverable.
Models decay. We set up automated retraining triggers based on prediction-drift thresholds and hand your team a runbook. Optional: a monthly retainer where we handle retraining, feature updates and performance reviews.
Three recent projects, anonymised per client agreement.
Demand-forecasting model retrained nightly on 14 months of order data. Reduced over-stock by 22% and under-stock events by 31% across 12,000 SKUs within eight weeks of deployment.
Computer-vision classifier for early-stage retinal anomalies. Trained on 48,000 labelled fundus images. Reduced average radiologist review time per image from 3.1 minutes to 0.9 minutes while maintaining specificity above 95%.
NLP ticket-routing model trained on two years of Zendesk history. Incoming tickets are auto-classified into 23 categories and assigned to the correct agent queue. Median first-response time dropped from 4.2 hours to 47 minutes.
Describe the problem, attach a sample if you like, and we will reply within one business day.
Address:
7 The Orchard, Reynolds-under-Beatty-Kuhlman, Wales, LC1 9CE, United Kingdom
Phone:
+44 7710 794770
Email:
[email protected]
