We build prediction engines, document classifiers, and anomaly detectors for mid-size UK businesses. Each model trains on your numbers, ships into your stack, and reports back in plain English.
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Four service lines. Each one ships a working model, not a research paper.
Time-series forecasting for demand planning, churn scoring, and financial projections. We use gradient-boosted trees and recurrent networks depending on your data volume. Most clients see a 12–18% improvement over their existing spreadsheet forecasts within the first quarter.
Invoices, contracts, claims forms: our NLP pipeline extracts fields, classifies document types, and flags anomalies. The system handles scanned PDFs and handwritten notes through an OCR layer before the language model runs. Average extraction accuracy sits at 94.6% after fine-tuning on your corpus.
Real-time monitoring for fraud signals, equipment sensor drift, and network intrusion patterns. Our isolation-forest and autoencoder models run on streaming data. A logistics client reduced false positives by 40% after we replaced their rule-based system with a trained model last autumn.
When off-the-shelf APIs fall short, we design architectures from scratch. Computer vision for quality control on production lines, recommendation engines for e-commerce catalogues, or speech-to-action pipelines for internal tooling. You own the weights and the code.
Regulated industries often can't send data to a cloud endpoint. We containerise models with Docker and Kubernetes, deploy them inside your firewall, and set up monitoring dashboards so your ops team can manage updates without calling us. All inference stays on your hardware.
Two recent engagements, summarised with permission.
A 200-employee logistics firm needed to predict next-day parcel volumes per depot. We trained an ensemble model on 14 months of dispatch records, weather data, and school-holiday calendars. After eight weeks in production, the client reduced overnight staffing overruns by 22% and cut idle van hours by roughly 310 per month.
Predictive analytics
Manual review of incoming claims took an average of 7.4 minutes each. Our document-intelligence pipeline now reads the PDF, extracts 23 key fields, cross-references the policy database, and flags inconsistencies. Human reviewers handle only the flagged subset, about 18% of total volume. Processing time dropped to 1.9 minutes per claim on average.
Document intelligenceFive stages. Typical timeline is four to eight weeks from kick-off to production.
We spend two days inside your systems. What's stored, where, in what format, and how clean is it? You get a written report grading each data source on completeness, recency, and relevance to the problem you described.
Before writing model code we estimate expected accuracy ranges, infrastructure costs, and ongoing maintenance load. If the numbers don't justify the project, we say so. About one in five enquiries ends here, and we don't charge for the sketch.
Iterative experimentation on your data. We try multiple architectures, log every run, and share a weekly progress dashboard showing loss curves and validation metrics. You can watch the model improve in near-real time.
The model ships as a REST API, a batch job, or an embedded library, whichever fits your stack. We write integration tests, load tests, and a rollback playbook. Your engineering team reviews the pull request before anything merges.
Data drifts. We set up alerts for prediction-quality decay and schedule quarterly retraining runs. If accuracy drops below the agreed threshold, we retrain within 48 hours at no extra cost during the first year.
Describe your data problem. We reply within one working day.
Office
8 Paucek Garth, Rosenbaum-on-Wolf, England, QY4 3AE, United Kingdom
Phone
+44 55 5333 4318
Email
[email protected]
Office hours: Monday to Friday, 09:00–17:30 GMT