We build, train and deploy machine-learning models for mid-sized UK companies. You keep control of the data; we handle the maths.
Get in touch Our storyEvery project starts with your data and a clear question. Here is how we typically help.
We train regression and classification models on your historical sales, churn or maintenance records. Most clients see a usable prototype within four weeks, running on their own cloud tenant so the data never leaves their environment. Typical accuracy improvements over manual forecasting range from 18 % to 35 %, measured against a hold-out test set we agree on before work begins.
Customer emails, support tickets, survey comments: we turn free text into structured insight. Our pipeline extracts sentiment, intent and named entities, then routes each item to the right team or dashboard. One logistics client reduced first-response time from 14 hours to under 90 minutes after we deployed a classifier on their helpdesk queue.
Defect detection on a production line, document scanning for insurance claims, or shelf-stock monitoring in retail: we fine-tune convolutional and transformer-based vision models for your specific use case. We supply labelling guidelines and can annotate up to 5 000 images in-house when your team is short on time. Inference typically runs on an edge device or a lightweight API, keeping per-image cost below one penny.
Not sure where AI fits? We run a two-day on-site audit: we map your data sources, interview process owners and score each opportunity by expected return and technical feasibility. You receive a ranked shortlist with effort estimates, not a 90-page slide deck full of buzzwords. The audit fee is deducted from any project you commission within six months.
We follow a phased approach borrowed from applied research, not from software-agency playbooks. The first phase is scoping: we define the question, agree on success metrics and lock down data access. This usually takes three to five working days.
Phase two is experimentation. Our engineers build a minimal model, evaluate it against the agreed metrics and share results in a short written report. No jargon-heavy presentations. If the numbers are not good enough, we stop and you only pay for the hours spent so far.
When the model passes evaluation, phase three wraps it in a production API or batch pipeline, integrates it with your existing systems and hands over monitoring dashboards. We stay on a lightweight retainer for three months after go-live to retrain the model as fresh data arrives and to fix any drift.
Typical end-to-end timelines sit between six and fourteen weeks, depending on data readiness. We have delivered faster when the client already had clean, labelled datasets.
Describe what you are trying to solve. We will reply within one working day with an honest assessment of whether AI is the right tool, and a rough idea of cost.
472 Simonis Grove, Old Bartell-McClure, England, TI27 7SQ, United Kingdom
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